clustering_api.py

__author__ = 'Michael Kern'
__version__ = '0.0.1'
__email__ = 'kernm@in.tum.de'

########################################################################################################################
# libraries

# use flask library for server activities
import flask
# load services (that are executed by the server when certain website is called)
from clustering_service import *

# create new flask application for hosting namespace
app = flask.Flask(__name__)

########################################################################################################################

@app.route('/kmeans/<k>/<initMethod>/<distance>/<datasetID>')
def kmeansClustering(k, initMethod, distance, datasetID):
    """
    Access k-means clustering plugin.
    :param k: number of clusters
    :param initMethod:  initialization method for initial clusters
    :param distance: distance measurement
    :param datasetID:  identifier of data set
    :return: jsonified output
    """
    try:
        data = loadData(datasetID)
        response = runKMeans(data, int(k), initMethod, distance)
        return flask.jsonify(response)
    except:
        return flask.jsonify({})

########################################################################################################################

@app.route('/hierarchical/<k>/<method>/<distance>/<datasetID>')
def hierarchicalClustering(k, method, distance, datasetID):
    """
    Access hierarchical clustering plugin.
    :param k: number of desired clusters
    :param method: type of single linkage
    :param distance: distance measurement
    :param datasetID: identifier of data set
    :return: jsonified output
    """
    try:
        data = loadData(datasetID)
        response = runHierarchical(data, int(k), method, distance)
        return flask.jsonify(response)
    except:
        return flask.jsonify({})

########################################################################################################################

@app.route('/affinity/<damping>/<factor>/<preference>/<distance>/<datasetID>')
def affinityPropagationClustering(damping, factor, preference, distance, datasetID):
    """
    Access affinity propagation clustering plugin.
    :param damping:
    :param factor:
    :param preference:
    :param distance: distance measurement
    :param datasetID:
    :return:
    """
    try:
        data = loadData(datasetID)
        response = runAffinityPropagation(data, float(damping), float(factor), preference, distance)
        return flask.jsonify(response)
    except:
        return flask.jsonify({})

########################################################################################################################

@app.route('/fuzzy/<numClusters>/<m>/<threshold>/<distance>/<datasetID>')
def fuzzyClustering(numClusters, m, threshold, distance, datasetID):
    """
    :param numClusters:
    :param m:
    :param threshold:
    :param distance:
    :param datasetID:
    :return:
    """
    try:
        data = loadData(datasetID)
        response = runFuzzy(data, int(numClusters), float(m), float(threshold), distance)
        return flask.jsonify(response)
    except:
        return flask.jsonify({})

########################################################################################################################

def loadAttribute(jsonData, attr):
    import json
    data = json.loads(jsonData)
    if attr in data:
        return data[attr]
    else:
        return None

########################################################################################################################

@app.route('/distances/<metric>/<datasetID>/<sorted>', methods=['POST'])
def getDistances(metric, datasetID, sorted):
    """
    Compute the distances of the current stratification values to its centroid.
    :param metric:
    :param datasetID:
    :return: distances and labels sorted in ascending order
    """
    data = loadData(datasetID)
    labels = []
    externLabels = None

    if 'group' in flask.request.values:
        labels = loadAttribute(flask.request.values['group'], 'labels')
        externLabels = loadAttribute(flask.request.values['group'], 'externLabels')
    else:
        return ''

    response = getClusterDistances(data, labels, metric, externLabels, sorted)
    return flask.jsonify(response)

########################################################################################################################

@app.route('/dendrogram/<numClusters>/<datasetID>', methods=['POST'])
def dendrogramClusters(numClusters, datasetID):
    data = loadData(datasetID)

    if 'group' in flask.request.values:
        dendrogram = loadAttribute(flask.request.values['group'], 'dendrogram')
    else:
        return ''

    response = getClustersFromDendrogram(data, dendrogram, int(numClusters))
    return flask.jsonify(response)


########################################################################################################################

def create():
  """
  Standard Caleydo convention for creating the service when server is initialized.
  :return: Returns implementation of this plugin with given name
  """
  return app