unsupervised module

Unsupervised machine learning module.

Cluster

__init__(self, data) special

Instantiate the Cluster class object.

Parameters:

Name	Type	Description	Default
data	array	Numpy array containing geospatial and attribute data.	required

Source code in mygeopackage/unsupervised.py

def __init__(self,data):
    """Instantiate the Cluster class object.

    Args:
        data (array): Numpy array containing geospatial and attribute data.
    """
    self.cluster_centers = []
    self.labels = []
    self.data = data
    self.identifier = 0

show(self, map=None, to_display=True)

Draw the object on the map with Folium.

Parameters:

Name	Type	Description	Default
map	Folium map object	If set to None, the function will create a new map object. If given the map object, the layer will be drawn on the map. Defaults to None.	None

Source code in mygeopackage/unsupervised.py

def show(self,map = None, to_display=True):
    """Draw the object on the map with Folium.

    Args:
        map (Folium map object, optional): If set to None, the function will create a new map object. If given the map object, the layer will be drawn on the map. Defaults to None.
    """
    cluster_class = np.unique(self.labels)
    data = self.data
    if map == None:
        m = folium.Map(location=[data[0][1],data[0][0]],zoom_start=10)
    else:
        m = map
    for label in cluster_class:
        color = rgb_to_hex([randint(100, 255), randint(100, 255), randint(100, 255)])
        for member in self.data[np.where(self.labels == label)]:
            folium.CircleMarker([member[1],member[0]],radius=6,popup='Cluster: '+str(label),fill=True,color=color,fill_color=color,fill_opacity=1).add_to(m)

    if to_display == True:
        display(m)

toGeoJson(self)

Convert cluster results to Geojson

Returns:

Type	Description
GeoJSON (str)	Serialized JSON text.

Source code in mygeopackage/unsupervised.py

def toGeoJson(self):
    """Convert cluster results to Geojson

    Returns:
        GeoJSON (str): Serialized JSON text.
    """
    geojson = dict()
    geojson['type'] = 'FeatureCollection'
    geojson['name'] = 'K-Means Results'
    geojson['features'] = []

    for i in range(len(self.data)):
        item = dict()
        item['type'] = 'Feature'
        item['properties'] = dict()
        item['properties']['ID'] = self.data[i][self.identifier]
        item['properties']['Class'] = int(self.labels[i])
        item['geometry'] = dict()
        item['geometry']['type'] = 'Point'
        item['geometry']['coordinates'] = [self.data[i][0],self.data[i][1]]
        geojson['features'].append(item)
    #print(geojson)
    return json.dumps(geojson)

dbscan(eps, min_samples, field, cluster, identifier)

DBSCAN unsupervised learning for geospatial or attribute data.

Parameters:

Name	Type	Description	Default
eps	float	The maximum distance between two samples for one to be considered as in the neighborhood of the other.	required
min_samples	int	The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.	required
field	int	The index for the fields to be clustered.	required
cluster	Cluster	Cluster class object to store the results.	required
identifier	int	The index for the field as the identifier of the dataset.	required

Source code in mygeopackage/unsupervised.py

def dbscan(eps,min_samples,field,cluster:Cluster,identifier):
    """DBSCAN unsupervised learning for geospatial or attribute data.

    Args:
        eps (float): The maximum distance between two samples for one to be considered as in the neighborhood of the other.
        min_samples (int): The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.
        field (int): The index for the fields to be clustered.
        cluster (Cluster): Cluster class object to store the results.
        identifier (int): The index for the field as the identifier of the dataset.
    """
    _dbscan = DBSCAN(eps=eps,min_samples=min_samples).fit(cluster.data[:,field].astype(np.float64))
    cluster_results = cluster
    cluster_results.cluster_centers = None
    cluster_results.labels = _dbscan.labels_
    cluster_results.identifier = identifier

k_means(n, field, cluster, identifier)

K-Means unsupervised learning for geospatial or attribute data.

Parameters:

Name	Type	Description	Default
n	int	Desired number of clusters for K-Means analysis.	required
field	int	The index for the fields to be clustered.	required
cluster	Cluster	Cluster class object to store the results.	required
identifier	int	The index for the field as the identifier of the dataset.	required

Source code in mygeopackage/unsupervised.py

def k_means(n,field,cluster:Cluster,identifier):
    """K-Means unsupervised learning for geospatial or attribute data.

    Args:
        n (int): Desired number of clusters for K-Means analysis.
        field (int): The index for the fields to be clustered.
        cluster (Cluster): Cluster class object to store the results.
        identifier (int): The index for the field as the identifier of the dataset.
    """
    kmeans = KMeans(n_clusters=n).fit(cluster.data[:,field])
    cluster_results = cluster
    cluster_results.cluster_centers = kmeans.cluster_centers_
    cluster_results.labels = kmeans.labels_
    cluster_results.identifier = identifier
    #cluster_results = new Cluster(geo.data,kmeans.cluster_centers_,kmeans.labels_)

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Last update: 2021-05-03