def do_stuff(dataset = None, metric = True, drtype = "mds", components = 2):
data_for_mds = np.array(dataset)
if drtype:
if drtype == "mds":
mds = manifold.MDS(n_components=components, n_init=10, max_iter=3000, dissimilarity="euclidean", n_jobs=1, metric=metric)
mds_result = mds.fit(data_for_mds)
elif drtype == "pca":
pca = PCA(n_components=2)
mds_result = pca.fit(euclidean_distances(data_for_mds)).transform(data_for_mds)
elif drtype == "tsne":
model = manifold.TSNE(n_components=2, random_state=0, learning_rate=1000, early_exaggeration=10.0)
mds_result = model.fit_transform(data_for_mds)
clusterings = {}
for i in range(10, 1, -1):
clustering = ac(n_clusters=i, memory=mkdtemp())
clusterings[i] = clustering.fit(data_for_mds).labels_.tolist()
clustering = ac(n_clusters=1, memory=mkdtemp())
clustering.fit(data_for_mds)
output = {
"drInfo": None,
"embedding": None,
"clustering": {
"tree": clustering.children_.tolist(),
"labels": clusterings
}
}
if drtype:
median_distance = False
stress1 = False
raw_stress = False
if drtype == "mds":
raw_stress = mds_result.stress_
disparities = euclidean_distances(data_for_mds)
disparityHalfMatrix = np.triu(disparities)
sumSquaredDisparities = np.sum(np.square(disparityHalfMatrix))
stress1 = math.sqrt(mds_result.stress_ / sumSquaredDisparities)
median_distance = np.median(euclidean_distances(mds_result.embedding_))
embedding = mds_result.embedding_.tolist()
print mds_result.stress_
else:
embedding = mds_result.tolist()
output["drInfo"] = {
"type": drtype,
"metric": metric,
"components": components,
"stress1": stress1,
"rawStress":raw_stress,
"medianDistance": median_distance
}
output["embedding"] = embedding
return output
def do_stuff(dataset = None, metric = True, drtype = "mds", components = 2):
data_for_mds = np.array(dataset)
if drtype:
if drtype == "mds":
mds = manifold.MDS(n_components=components, n_init=10, max_iter=3000, dissimilarity="euclidean", n_jobs=1, metric=metric)
mds_result = mds.fit(data_for_mds)
elif drtype == "pca":
pca = PCA(n_components=2)
mds_result = pca.fit(euclidean_distances(data_for_mds)).transform(data_for_mds)
elif drtype == "tsne":
model = manifold.TSNE(n_components=2, random_state=0, learning_rate=1000, early_exaggeration=10.0)
mds_result = model.fit_transform(data_for_mds)
clusterings = {}
for i in range(10, 1, -1):
clustering = ac(n_clusters=i, memory=mkdtemp())
clusterings[i] = clustering.fit(data_for_mds).labels_.tolist()
clustering = ac(n_clusters=1, memory=mkdtemp())
clustering.fit(data_for_mds)
output = {
"drInfo": None,
"embedding": None,
"clustering": {
"tree": clustering.children_.tolist(),
"labels": clusterings
}
}
if drtype:
median_distance = False
stress1 = False
raw_stress = False
if drtype == "mds":
raw_stress = mds_result.stress_
disparities = euclidean_distances(data_for_mds)
disparityHalfMatrix = np.triu(disparities)
sumSquaredDisparities = np.sum(np.square(disparityHalfMatrix))
stress1 = math.sqrt(mds_result.stress_ / sumSquaredDisparities)
median_distance = np.median(euclidean_distances(mds_result.embedding_))
embedding = mds_result.embedding_.tolist()
print(mds_result.stress_)
else:
embedding = mds_result.tolist()
output["drInfo"] = {
"type": drtype,
"metric": metric,
"components": components,
"stress1": stress1,
"rawStress":raw_stress,
"medianDistance": median_distance
}
output["embedding"] = embedding
return output
def average_linkage(dataset):
path = "data/" + dataset + ".csv"
df = pd.read_csv(path)
if dataset == 'dataset1':
x = df.iloc[:, [0, 1]].values
d = '2D'
else:
x = df.iloc[:, [0, 1, 2]].values
d = '3D'
hac = ac(n_clusters = None, distance_threshold = 1, linkage = 'average')
hac.fit(x)
linkages = create_dendogram(hac)
dendrogram(linkages, truncate_mode = 'lastp')
plt.title("Dendogram for Average Linkage HAC with " + dataset)
filename = "averageHAC" + d + "_dendogram"
plt.savefig(filename)
plt.clf()
if d == '2D':
generate_2D_plot(x, 3, 'average')
else:
generate_3D_plot(x, 26, 'average')
def generate_2D_plot(x, clusters, linkage):
hac = ac(n_clusters = clusters, affinity = 'euclidean', linkage = linkage)
hac.fit_predict(x)
plt.title("Cluster Map for " + linkage.capitalize() + " Linkage HAC with dataset1")
plt.scatter(x[:, 0], x[:, 1], c = hac.labels_)
filename = linkage + "HAC2D_cluster"
plt.savefig(filename)
plt.clf()
def generate_3D_plot(x, clusters, linkage):
hac = ac(n_clusters = clusters, affinity = 'euclidean', linkage = linkage)
hac.fit_predict(x)
fig = plt.figure()
ax = Axes3D(fig)
ax.set_title("Cluster Map for " + linkage.capitalize() + " Linkage HAC with dataset2")
ax.scatter(x[:, 0], x[:, 1], x[:, 2], c = hac.labels_)
filename = linkage + "HAC3D_cluster"
plt.savefig(filename)
plt.clf()
#fit the data points to the k means algorithm
kmeans.fit(points)
print(kmeans.cluster_centers_)
y_kmeans = kmeans.fit_predict(points)
f1 = plt.figure()
plt.title('K-means clustering')
plt.scatter(points[y_kmeans == 0, 0], points[y_kmeans == 0, 1], c='red')
plt.scatter(points[y_kmeans == 1, 0], points[y_kmeans == 1, 1], c='blue')
plt.scatter(points[y_kmeans == 2, 0], points[y_kmeans == 2, 1], c='black')
plt.scatter(points[y_kmeans == 3, 0], points[y_kmeans == 3, 1], c='cyan')
plt.show()
#create dendogram
#dendogram = sch.dendrogram(sch.linkage(points,method='ward'))
hc = ac(n_clusters=2, affinity='euclidean', linkage='ward')
y_hc = hc.fit_predict(points)
f2 = plt.figure()
plt.scatter(points[y_hc == 0, 0], points[y_hc == 0, 1], c='red')
plt.scatter(points[y_hc == 1, 0], points[y_hc == 1, 1], c='blue')
plt.scatter(points[y_hc == 2, 0], points[y_hc == 2, 1], c='black')
plt.scatter(points[y_hc == 3, 0], points[y_hc == 3, 1], c='cyan')
plt.title('Heirarchical Clustering')
plt.show()
#Birch clustering
bir = Birch(n_clusters=2, threshold=0.8, branching_factor=200)
bir.fit(points)
y_bir = bir.fit_predict(points)
def run(self, clusters=3):
self.y = ac(n_clusters=clusters).fit_predict(self.X)
print "Raw dataset:\n", dataset.head()
x = dataset.iloc[:, [3, 4]].values #Taking columns 4 & 5
print "Independent variables:\n", x
## Using dendrogram to find the optimal number of clusters
dendrogram = sch.dendrogram(sch.linkage(
x, method='ward')) #ward method minimizes variance within each cluster
plt.title('Dendrogram')
plt.xlabel('Customers')
plt.ylabel('Euclidean distances')
plt.show()
# Largest distance where we can make vertically without crossing any horizontal line: optimal clusters = 5
## Fitting Hierarchical clustering to the mall dataset
hc = ac(
n_clusters=5, affinity='euclidean', linkage='ward'
) #affinity = distance to make the linkage, ward method minimizes variance within each cluster. Use the same linkage as the one used to build the dendrogram.
y_hc = hc.fit_predict(
x) #Fitting AgglomerativeClustering to data x to create vector y.
print "Clusters:\n", y_hc #y_hc only shows the clusters. Join this with matrix x to analyse the behaviour of each clusters
##Visualising the clusters (Only for 2d clustering i.e. 2 columns of interest)
plt.scatter(x[y_hc == 0, 0],
x[y_hc == 0, 1],
s=100,
c='red',
label='Cluster 1')
plt.scatter(x[y_hc == 1, 0],
x[y_hc == 1, 1],
s=100,
c='blue',
