Merge pull request #139 from anuragkumarak95/master

added k means clustering algorithm, usage doc inside.

machine_learning/k_means_clust.py

@@ -0,0 +1,172 @@
+'''README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com)
+
+Requirements:
+  - sklearn
+  - numpy
+  - matplotlib
+
+Python:
+  - 3.5
+
+Inputs:
+  - X , a 2D numpy array of features.
+  - k , number of clusters to create.
+  - initial_centroids , initial centroid values generated by utility function(mentioned in usage).
+  - maxiter , maximum number of iterations to process.
+  - heterogeneity , empty list that will be filled with hetrogeneity values if passed to kmeans func.
+
+Usage:
+  1. define 'k' value, 'X' features array and 'hetrogeneity' empty list
+  
+  2. create initial_centroids,
+        initial_centroids = get_initial_centroids(
+            X, 
+            k, 
+            seed=0 # seed value for initial centroid generation, None for randomness(default=None)
+            )
+
+  3. find centroids and clusters using kmeans function.
+  
+        centroids, cluster_assignment = kmeans(
+            X, 
+            k, 
+            initial_centroids, 
+            maxiter=400,
+            record_heterogeneity=heterogeneity, 
+            verbose=True # whether to print logs in console or not.(default=False)
+            )
+  
+  
+  4. Plot the loss function, hetrogeneity values for every iteration saved in hetrogeneity list.
+        plot_heterogeneity(
+            heterogeneity, 
+            k
+        )
+  
+  5. Have fun..
+  
+'''
+from sklearn.metrics import pairwise_distances
+import numpy as np
+
+TAG = 'K-MEANS-CLUST/ '
+
+def get_initial_centroids(data, k, seed=None):
+    '''Randomly choose k data points as initial centroids'''
+    if seed is not None: # useful for obtaining consistent results
+        np.random.seed(seed)
+    n = data.shape[0] # number of data points
+        
+    # Pick K indices from range [0, N).
+    rand_indices = np.random.randint(0, n, k)
+    
+    # Keep centroids as dense format, as many entries will be nonzero due to averaging.
+    # As long as at least one document in a cluster contains a word,
+    # it will carry a nonzero weight in the TF-IDF vector of the centroid.
+    centroids = data[rand_indices,:]
+    
+    return centroids
+
+def centroid_pairwise_dist(X,centroids):
+    return pairwise_distances(X,centroids,metric='euclidean')
+
+def assign_clusters(data, centroids):
+    
+    # Compute distances between each data point and the set of centroids:
+    # Fill in the blank (RHS only)
+    distances_from_centroids = centroid_pairwise_dist(data,centroids)
+    
+    # Compute cluster assignments for each data point:
+    # Fill in the blank (RHS only)
+    cluster_assignment = np.argmin(distances_from_centroids,axis=1)
+    
+    return cluster_assignment
+
+def revise_centroids(data, k, cluster_assignment):
+    new_centroids = []
+    for i in range(k):
+        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
+        member_data_points = data[cluster_assignment==i]
+        # Compute the mean of the data points. Fill in the blank (RHS only)
+        centroid = member_data_points.mean(axis=0)
+        new_centroids.append(centroid)
+    new_centroids = np.array(new_centroids)
+    
+    return new_centroids
+
+def compute_heterogeneity(data, k, centroids, cluster_assignment):
+    
+    heterogeneity = 0.0
+    for i in range(k):
+        
+        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
+        member_data_points = data[cluster_assignment==i, :]
+        
+        if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty
+            # Compute distances from centroid to data points (RHS only)
+            distances = pairwise_distances(member_data_points, [centroids[i]], metric='euclidean')
+            squared_distances = distances**2
+            heterogeneity += np.sum(squared_distances)
+        
+    return heterogeneity
+
+from matplotlib import pyplot as plt
+def plot_heterogeneity(heterogeneity, k):
+    plt.figure(figsize=(7,4))
+    plt.plot(heterogeneity, linewidth=4)
+    plt.xlabel('# Iterations')
+    plt.ylabel('Heterogeneity')
+    plt.title('Heterogeneity of clustering over time, K={0:d}'.format(k))
+    plt.rcParams.update({'font.size': 16})
+    plt.show()
+
+def kmeans(data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False):
+    '''This function runs k-means on given data and initial set of centroids.
+       maxiter: maximum number of iterations to run.(default=500)
+       record_heterogeneity: (optional) a list, to store the history of heterogeneity as function of iterations
+                             if None, do not store the history.
+       verbose: if True, print how many data points changed their cluster labels in each iteration'''
+    centroids = initial_centroids[:]
+    prev_cluster_assignment = None
+    
+    for itr in range(maxiter):        
+        if verbose:
+            print(itr, end='')
+        
+        # 1. Make cluster assignments using nearest centroids
+        cluster_assignment = assign_clusters(data,centroids)
+            
+        # 2. Compute a new centroid for each of the k clusters, averaging all data points assigned to that cluster.
+        centroids = revise_centroids(data,k, cluster_assignment)
+            
+        # Check for convergence: if none of the assignments changed, stop
+        if prev_cluster_assignment is not None and \
+          (prev_cluster_assignment==cluster_assignment).all():
+            break
+        
+        # Print number of new assignments 
+        if prev_cluster_assignment is not None:
+            num_changed = np.sum(prev_cluster_assignment!=cluster_assignment)
+            if verbose:
+                print('    {0:5d} elements changed their cluster assignment.'.format(num_changed))   
+        
+        # Record heterogeneity convergence metric
+        if record_heterogeneity is not None:
+            # YOUR CODE HERE
+            score = compute_heterogeneity(data,k,centroids,cluster_assignment)
+            record_heterogeneity.append(score)
+        
+        prev_cluster_assignment = cluster_assignment[:]
+        
+    return centroids, cluster_assignment
+
+# Mock test below
+if False: # change to true to run this test case.
+    import sklearn.datasets as ds
+    dataset = ds.load_iris()
+    k = 3
+    heterogeneity = []
+    initial_centroids = get_initial_centroids(dataset['data'], k, seed=0)
+    centroids, cluster_assignment = kmeans(dataset['data'], k, initial_centroids, maxiter=400,
+                                        record_heterogeneity=heterogeneity, verbose=True)
+    plot_heterogeneity(heterogeneity, k)