Excuse me if the questions are too simple but I am getting into machine learning having time constraints.
I must apply a mixed classification to a df with the following steps:
Apply the KMeans with 50 clusters
From the barycenters and labels obtained for each cluster, a dendrogram must be displayed, in order to choose the right k.
Then apply an HCA algorithm from the barycenters obtained in step 1 with the number of clusters from step 2.
Calculate the barycenters of each new group
Use the calculated barycenters to consolidate the clusters by the KMeans algorithm.
What I do is:
clf = KMeans(n_clusters=50)
centroids = clf.cluster_centers_
labels = clf.labels_
From there I get confused with the dendrogram. So far I have used it only over the whole df and I am not certain how to involve the barycenters and labels from the KMeans correctly.
Z = linkage(df, method='ward', metric='euclidean')
dendrogram(Z, labels=df.index, leaf_rotation=90., color_threshold=0)
plt.show()
Last but not least, I do not know how to get the barycenters in the AgglomerativeClustering.
Any clarification would be of help. Thanks in advance!
Say i have the following dataframe stored as a variable called coordinates, where the first few rows look like:
business_lat business_lng business_rating
0 19.111841 72.910729 5.
1 19.111342 72.908387 5.
2 19.111342 72.908387 4.
3 19.137815 72.914085 5.
4 19.119677 72.905081 2.
5 19.119677 72.905081 2.
. . .
. . .
. . .
As you can see this data is geospatial (has a lat and a lng) AND every row has an additional value, business_rating, that corresponds to the rating of the business at the latlng in that row. I want to cluster the data, where businesses that are nearby and have similar ratings are assigned into the same cluster. Essentially I need a a geospatial cluster with an additional requirement that the clustering must consider the rating column.
I've looked online and can't really find much addressing approaches for this: only things for strict geospatial clustering (only features to cluster on are latlng) or non spatial clustering.
I have a simple DBSCAN running below, but when i plot the results of the clustering it does not seem to be doing what I want correctly.
from sklearn.cluster import DBSCAN
import numpy as np
db = DBSCAN(eps=2/6371., min_samples=5, algorithm='ball_tree', metric='haversine').fit(np.radians(coordinates))
Would I be better served trying to tweak the parameters of the DBSCAN, doing some additional processing of the data or using a different approach all together?
The tricky part about clustering two different types of information (location and rating) is determining how they should relate to each other. It's simple to ask when it is just one domain and you are comparing the same units. My approach would be to look at how to relate rows within a domain and then determine some interaction between the domains. This could be done using scaling options like MinMaxScaler mentioned, however, I think this is a bit heavy handed and we could use our knowledge of the domains to cluster better.
Handling Location
Location distance is best handled directly as this has real world meaning that we can precalculate distances for. The meaning of meters apart is direct to what we
You could use the scaling option mentioned in the previous answer but this risks distorting the location data. For example, if you have a long and thin set of locations, MinMaxScaling would give more importance to variation on the thin axis than the long axis. If you are going to use scaling, do it on the computed distance matrix, not on the lat lon themselves.
import numpy as np
from sklearn.metrics.pairwise import haversine_distances
points_in_radians = df[['business_lat','business_lng']].apply(np.radians).values
distances_in_km = haversine_distances(points_in_radians) * 6371
Adding in Rating
We can think of the problem through asking a couple of questions that relate rating to distance. We could ask, how different must ratings be to separate observations in the same place? What is the meter difference to rating difference ratio? With an idea of ratio, we can calculate another distance matrix for the rating difference for all observations and use this to scale or add on the original location distance matrix or we could increase the distance for every gap in rating. This location-plus-ratings-difference matrix can then be clustered on.
from sklearn.metrics.pairwise import euclidean_distances
added_km_per_rating_gap = 1
rating_distances = euclidean_distances(df[['business_rating']].values) * added_km_per_rating_gap
We can then simply add these together and cluster on the resulting matrix.
from sklearn.cluster import DBSCAN
distance_matrix = rating_distances + distances_in_km
clustering = DBSCAN(metric='precomputed', eps=1, min_samples=2)
clustering.fit(distance_matrix)
What we have done is cluster by location, adding a penalty for ratings difference. Making that penalty direct and controllable allows for optimisation to find the best clustering.
Testing
The problem I'm finding is that (with my test data at least) DBSCAN has a tendency to 'walk' from observation to observation forming clusters that either blend ratings together because the penalty is not high enough or separates into single rating groups. It might be that DBSCAN is not suitable for this type of clustering. If I had more time, I would look for some open data to test this on and try other clustering methods.
Here is the code I used to test. I used the square of the ratings distance to emphasise larger gaps.
import random
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=300, centers=6, cluster_std=0.60, random_state=0)
ratings = np.array([random.randint(1,4) for _ in range(len(X)//2)] \
+[random.randint(2,5) for _ in range(len(X)//2)]).reshape(-1, 1)
distances_in_km = euclidean_distances(X)
rating_distances = euclidean_distances(ratings)
def build_clusters(multiplier, eps):
rating_addition = (rating_distances ** 2) * multiplier
distance_matrix = rating_addition + distances_in_km
clustering = DBSCAN(metric='precomputed', eps=eps, min_samples=10)
clustering.fit(distance_matrix)
return clustering.labels_
Using the DBSCAN methodology, we can calculate the distance between points (the Euclidean distance or some other distance) and look for points which are far away from others. You may want to consider using the MinMaxScaler to normalize values, so one feature doesn't overwhelm other features.
Where is your code and what are your final results? Without an actual code sample, I can only guess what you are doing.
I hacked together some sample code for you. You can see the results below.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import seaborn as sns; sns.set()
import csv
df = pd.read_csv('C:\\your_path_here\\business.csv')
X=df.loc[:,['review_count','latitude','longitude']]
K_clusters = range(1,10)
kmeans = [KMeans(n_clusters=i) for i in K_clusters]
Y_axis = df[['latitude']]
X_axis = df[['longitude']]
score = [kmeans[i].fit(Y_axis).score(Y_axis) for i in range(len(kmeans))]# Visualize
plt.plot(K_clusters, score)
plt.xlabel('Number of Clusters')
plt.ylabel('Score')
plt.title('Elbow Curve')
plt.show()
kmeans = KMeans(n_clusters = 3, init ='k-means++')
kmeans.fit(X[X.columns[0:2]]) # Compute k-means clustering.
X['cluster_label'] = kmeans.fit_predict(X[X.columns[0:2]])
centers = kmeans.cluster_centers_ # Coordinates of cluster centers.
labels = kmeans.predict(X[X.columns[0:2]]) # Labels of each point
X.head(10)
X.plot.scatter(x = 'latitude', y = 'longitude', c=labels, s=50, cmap='viridis')
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)
from scipy.stats import zscore
df["zscore"] = zscore(df["review_count"])
df["outlier"] = df["zscore"].apply(lambda x: x <= -2.5 or x >= 2.5)
df[df["outlier"]]
df_cord = df[["latitude", "longitude"]]
df_cord.plot.scatter(x = "latitude", y = "latitude")
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df_cord = scaler.fit_transform(df_cord)
df_cord = pd.DataFrame(df_cord, columns = ["latitude", "longitude"])
df_cord.plot.scatter(x = "latitude", y = "longitude")
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(
eps = 0.5,
metric="euclidean",
min_samples = 3,
n_jobs = -1)
clusters = outlier_detection.fit_predict(df_cord)
clusters
from matplotlib import cm
cmap = cm.get_cmap('Accent')
df_cord.plot.scatter(
x = "latitude",
y = "longitude",
c = clusters,
cmap = cmap,
colorbar = False
)
The final result looks a little weird, to tell you the truth. Remember, not everything is clusterable.
I have a python script which do clustering over a data file which is in svmlight format.
I use the function sklearn.datasets.load_svmlight_file to load the data from the data file.
I know that this function returns a sparse matrix.
I need to scatter plot the clusters, can any body help me please.
This what I have done:
import sklearn.datasets
import sys
from sklearn.cluster import KMeans
dataFilename = sys.argv[1]
X, y = sklearn.datasets.load_svmlight_file(dataFilename)
kmeans = KMeans(n_clusters = 3)
kmeans.fit(X)
labels = kmeans.labels_
print(labels)
centroids = kmeans.cluster_centers_
Without having the dataset, I would suggest the following:
Since load_svmlight_file() returns a sparse matrix, turn X into a NumPy array using samples = X.toarray() prior to fitting the model.
Plot two features (for example) of the dataset using:
plt.scatter(samples[:,0], samples[:,1], c=labels). This colours the clusters by their predicted labels.
Follow this with plt.scatter(centroids[:,0], centroids[:,1], marker='D') to see the location of the centroids with diamonds.
Note that samples[:,n] represents an array containing the sample values for the nth feature of the dataset.
I hope this helps. If not, please let me know.
I have a RGB image of the following shape ((3L, 5L, 5L). It means 5 by 5 pixels image having 3 layers (R,G,andB).I want to cluster it using DBSCAN algorithm as follows. But I got an error message that ValueError: Found array with dim 3. Expected <= 2. Can not I use for my 3d image?
import numpy as np
from sklearn.cluster import DBSCAN
from collections import Counter
data = np.random.rand(3,5,5)
print np.shape(data)
print data
db = DBSCAN(eps=0.12, min_samples=3).fit(data)
print db
DBSCAN(algorithm='auto', eps=0.12, leaf_size=30, metric='euclidean',
min_samples=1, p=None, random_state=None)
labels = db.labels_
print Counter(labels)
To cluster you need to say what the distance between two points is. DBSCAN is not a graph clustering algorithm, it works with features. You need to represent each pixel as features, so that the distances are appropriate.
The features could just be RGB, in which case similar colors are clustered together. Or the features could also include x, y coordinates, which would mean spacial distances are also considered.
If you want to consider spatial distances, I'd suggest you take a look at scikit-image's segmentation module, which contains a couple of popular image segmentation methods.
When utilizing the sklearn class sklearn.cluster for K-means clustering, a fitted k-means object has 3 attributes, including a numpy array of cluster centers (centers x features) named cluster_centers_. However, these centers don't have an attached label.
My question is: are the centers (rows) in cluster_centers_ ordered by the label value? That is, does row 1 correspond to the center for the cluster labeled 1? Or are they placed in the array randomly? A pointer to any documentation would be more than sufficient.
Thanks.
I couldn't find the documentation but yes it is ordered by cluster.
So:
kmeans.cluster_centers_[0] = centroid of cluster 0