Unsupervised machine learning algorithms don't have any supervisor to provide any sort of guidance. That is why they are firmly aligned with what some call true artificial intelligence.
In unsupervised learning, there would be no right answer and no teacher for the guidance. Algorithms need to find the interesting pattern in data for learning.
Fundamentally, it is a type of unsupervised learning method and a common technique for statistical data analysis utilized in many fields. Clustering mainly is a task of dividing the set of observations into subsets, called clusters, in such a way that observations in the same cluster are similar in one sense and they are dissimilar to the observations in different clusters. In basic words, we can say that the primary goal of clustering is to group the data on the basis of similarity and dissimilarity.
For example, the following diagram shows similar kind of data in various clusters −
Following are a few basic algorithms for clustering the data −
K-means clustering algorithm is one of the well-known algorithms for clustering the information. We have to assume that the numbers of clusters are already known. This is also called flat clustering. It is an iterative clustering algorithm. The steps given below should be followed for this algorithm −
Step 1 − We have to specify the desired number of K subgroups.
Step 2 − Fix the number of clusters and randomly assign every data point to a cluster. Or in other words we have to classify our data based on the number of clusters.
In this step, cluster centroids ought to be computed.
As this is an iterative algorithm, we need to update the locations of K centroids with each iteration until we locate the global optima or in other words the centroids reach at their optimal locations.
The following code will assist in implementing K-means clustering algorithm in Python. We are going to use the Scikit-learn module.
Let us import the essential packages −
import matplotlib.pyplot as plt import seaborn as sns; sns.set() import numpy as np from sklearn.cluster import KMeans
The following line of code will assist in generating the two-dimensional dataset, containing four blobs, by using make_blob from the sklearn.dataset package.
from sklearn.datasets.samples_generator import make_blobs X, y_true = make_blobs(n_samples = 500, centers = 4, cluster_std = 0.40, random_state = 0)
We can visualize the dataset by utilizing the following code −
plt.scatter(X[:, 0], X[:, 1], s = 50); plt.show()
Here, we are initializing kmeans to be the KMeans algorithm, with the necessary parameter of how many clusters (n_clusters).
kmeans = KMeans(n_clusters = 4)
We have to train the K-means model with the input data.
kmeans.fit(X) y_kmeans = kmeans.predict(X) plt.scatter(X[:, 0], X[:, 1], c = y_kmeans, s = 50, cmap = 'viridis') centers = kmeans.cluster_centers_
The code given below will assist us plot and visualize the machine's findings based on our data, and the fitment according to the number of clusters that are to be found.
plt.scatter(centers[:, 0], centers[:, 1], c = 'black', s = 200, alpha = 0.5); plt.show()
It is another popular and powerful clustering algorithm utilized in unsupervised learning. It doesn't make any assumptions hence it is a non-parametric algorithm. It is additionally called hierarchical clustering or mean shift cluster analysis. Followings would be the essential steps of this algorithm −
First of all, we need to start with the data points assigned to a cluster of their own.
Now, it computes the centroids and update the area of new centroids.
By repeating this cycle, we move closer the peak of cluster i.e. towards the region of higher density.
This algorithm stops at the stage where centroids don't move anymore.
With the assistance of following code we are implementing Mean Shift clustering algorithm in Python. We are going to use Scikit-learn module.
Let us import the essential packages −
import numpy as np from sklearn.cluster import MeanShift import matplotlib.pyplot as plt from matplotlib import style style.use("ggplot")
The following code will help in generating the two-dimensional dataset, containing four blobs, by utilizing make_blob from the sklearn.dataset package.
from sklearn.datasets.samples_generator import make_blobs
We can visualize the dataset with the following code
centers = [[2,2],[4,5],[3,10]] X, _ = make_blobs(n_samples = 500, centers = centers, cluster_std = 1) plt.scatter(X[:,0],X[:,1]) plt.show()
Now, we need to train the Mean Shift cluster model with the information.
ms = MeanShift() ms.fit(X) labels = ms.labels_ cluster_centers = ms.cluster_centers_
The following code will print the cluster centers and the expected number of cluster according to the input data −
print(cluster_centers) n_clusters_ = len(np.unique(labels)) print("Estimated clusters:", n_clusters_) [[ 3.23005036 3.84771893] [ 3.02057451 9.88928991]] Estimated clusters: 2
The code given below will help plot and visualize the machine's discoveries based on our data, and the fitment according to the number of clusters that are to be found.
colors = 10*['r.','g.','b.','c.','k.','y.','m.'] for i in range(len(X)): plt.plot(X[i][0], X[i][1], colors[labels[i]], markersize = 10) plt.scatter(cluster_centers[:,0],cluster_centers[:,1], marker = "x",color = 'k', s = 150, linewidths = 5, zorder = 10) plt.show()
The real world data isn't naturally organized into number of distinctive clusters. Due to this reason, it is difficult to visualize and draw inferences. That is why we need to measure the clustering performance as well as its quality. It can be finished with the help of silhouette analysis.
This technique can be used to check the quality of clustering by estimating the distance between the clusters. Basically, it gives a way to assess the parameters like number of clusters by giving a silhouette score. This score is a metric that measures how close each point in one cluster is to the points in the neighboring clusters.
The score has a range of [-1, 1]. Following is the investigation of this score −
Score of +1 − Score near +1 indicates that the sample is far away from the neighboring cluster.
Score of 0 − Score 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters.
Score of -1 − Negative score indicates that the samples have been assigned to the wrong clusters.
In this segment, we will learn how to calculate the silhouette score.
Silhouette score can be calculated by utilizing the following formula −
$$silhouette score = \frac{\left ( p-q \right )}{max\left ( p,q \right )}$$
Here, is the mean distance to thr points in the closest cluster that the data point is not a part of. And, i the mean intra-cluster distance to all the points in its own cluster.
For finding the optimal number of clusters, we need to run the clustering algorithm again by importing the metrics module from the sklearn package. In the following example, we will run the K-means clustering algorithm to locate the optimal number of clusters −
Import the fundamental packages as appeared −
import matplotlib.pyplot as plt import seaborn as sns; sns.set() import numpy as np from sklearn.cluster import KMeans
With the help of the following code, we will produce the two-dimensional dataset, containing four blobs, by using make_blob from the sklearn.dataset package.
from sklearn.datasets.samples_generator import make_blobs X, y_true = make_blobs(n_samples = 500, centers = 4, cluster_std = 0.40, random_state = 0)
Initialize the variables as appeared −
scores = [] values = np.arange(2, 10)
We have to iterate the K-means model through all the values and furthermore need to train it with the input data.
for num_clusters in values: kmeans = KMeans(init = 'k-means++', n_clusters = num_clusters, n_init = 10) kmeans.fit(X)
Now, estimate the silhouette score for the current clustering model utilizing the Euclidean distance metric −
score = metrics.silhouette_score(X, kmeans.labels_, metric = 'euclidean', sample_size = len(X))
The following line of code will help in showing the number of clusters as well as Silhouette score.
print("\nNumber of clusters =", num_clusters) print("Silhouette score =", score) scores.append(score)
You will get the following output −
Number of clusters = 9 Silhouette score = 0.340391138371 num_clusters = np.argmax(scores) + values[0] print('\nOptimal number of clusters =', num_clusters)
Presently, the output for optimal number of clusters would be as follows −
Optimal number of clusters = 2
If we want to build recommender systems such as a movie recommender system then we need to understand the concept of finding the closest neighbors. It is because the recommender system uses the concept of closest neighbors.
The concept of finding closest neighbors may be defined as the way of finding the closest point to the input point from the given dataset. The main use of this KNN)K-nearest neighbors) algorithm is to build classification systems that classify a data point on the proximity of the input data point to different classes.
The Python code given below helps in finding the K-nearest neighbors of a given data set −
Import the necessary packages as shown below. Here, we are utilizing the NearestNeighbors module from the sklearn package
import numpy as np import matplotlib.pyplot as plt from sklearn.neighbors import NearestNeighbors
Let us now characterize the input data −
A = np.array([[3.1, 2.3], [2.3, 4.2], [3.9, 3.5], [3.7, 6.4], [4.8, 1.9], [8.3, 3.1], [5.2, 7.5], [4.8, 4.7], [3.5, 5.1], [4.4, 2.9],])
Now, we need to define the closest neighbors −
k = 3
We also need to give the test data from which the closest neighbors is to be found −
test_data = [3.3, 2.9]
The following code can imagine and plot the input data defined by us −
plt.figure() plt.title('Input data') plt.scatter(A[:,0], A[:,1], marker = 'o', s = 100, color = 'black')
Now, we have to build the K Nearest Neighbor. The object additionally needs to be trained
knn_model = NearestNeighbors(n_neighbors = k, algorithm = 'auto').fit(X) distances, indices = knn_model.kneighbors([test_data])
Now, we can print the K closest neighbors as follows
print("\nK Nearest Neighbors:") for rank, index in enumerate(indices[0][:k], start = 1): print(str(rank) + " is", A[index])
We can visualize the closest neighbors along with the test data point
plt.figure() plt.title('Nearest neighbors') plt.scatter(A[:, 0], X[:, 1], marker = 'o', s = 100, color = 'k') plt.scatter(A[indices][0][:][:, 0], A[indices][0][:][:, 1], marker = 'o', s = 250, color = 'k', facecolors = 'none') plt.scatter(test_data[0], test_data[1], marker = 'x', s = 100, color = 'k') plt.show()
K Nearest Neighbors
1 is [ 3.1 2.3] 2 is [ 3.9 3.5] 3 is [ 4.4 2.9]
A K-Nearest Neighbors (KNN) classifier is a classification model that utilizes the nearest neighbors algorithm to classify a given information point. We have implemented the KNN algorithm in the last section, presently we are going to build a KNN classifier utilizing that algorithm.
The basic concept of K-nearest neighbor classification is to discover a predefined number, i.e., the 'k' − of training samples closest in distance to a new sample, which has to be classified. New samples will get their label from the neighbors itself. The KNN classifiers have a fixed user characterized constant for the number of neighbors which must be resolved. For the distance, standard Euclidean distance is the most common decision. The KNN Classifier works straightforwardly on the learned samples rather than creating the rules for learning. The KNN algorithm is among the least of all machine learning algorithms. It has been quite successful in a large number of classification and regression problems, for example, character recognition or picture analysis.
Example
We are building a KNN classifier to recognize digits. For this, we will utilize the MNIST dataset. We will write this code in the Jupyter Notebook.
Import the fundamental packages as shown below.
Here we are utilizing the KNeighborsClassifier module from the sklearn.neighbors package −
from sklearn.datasets import * import pandas as pd %matplotlib inline from sklearn.neighbors import KNeighborsClassifier import matplotlib.pyplot as plt import numpy as np
The following code will show the image of digit to verify what picture we have to test −
def Image_display(i): plt.imshow(digit['images'][i],cmap = 'Greys_r') plt.show()
Now, we need to load the MNIST dataset. Actually there are total 1797 pictures but we are utilizing the first 1600 pictures as training sample and the remaining 197 would be kept for testing purpose.
digit = load_digits() digit_d = pd.DataFrame(digit['data'][0:1600])
Now, on showing the pictures we can see the output as follows −
Image_display(0)
Image of 0 is shown as follows −
Image of 9 is shown as follows −
Presently, we have to create the training and testing data set and supply testing data set to the KNN classifiers.
train_x = digit['data'][:1600] train_y = digit['target'][:1600] KNN = KNeighborsClassifier(20) KNN.fit(train_x,train_y)
The following output will create the K closest neighbor classifier constructor −
KNeighborsClassifier(algorithm = 'auto', leaf_size = 30, metric = 'minkowski', metric_params = None, n_jobs = 1, n_neighbors = 20, p = 2, weights = 'uniform')
We have to create the testing sample by providing any arbitrary number greater than 1600, which were the training samples.
test = np.array(digit['data'][1725]) test1 = test.reshape(1,-1) Image_display(1725)
Image of 6 is shown as follows −
Now we will predict the test data as follows −
KNN.predict(test1)
The above code will generate the following output −
array([6])
Now, consider the following −
digit['target_names']
The above code will generate the following output −
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])