class ClusterizationModel:
"""Model for clusterization
Parameters
-----------
n_clusters : integer, optional
The dimension of the projection subspace.
Attributes
----------
n_clusters : int, number of classes
labels : list of int
Labels of each point
"""
def __init__(self, n_clusters=8, model="agglomerative", **kwargs):
self.n_clusters = n_clusters
self.labels = []
self.X = []
self.model_name = model
if model == "hierarchy":
self.base_model = HierarchyModel(n_clusters, **kwargs)
elif model == "KMeans":
self.base_model = KMeans(n_clusters, **kwargs)
elif model == "agglomerative":
self.base_model = AgglomerativeClustering(linkage='ward', n_clusters=n_clusters, **kwargs)
elif model == "dbscan":
self.base_model = DBSCAN(**kwargs)
else:
self.base_model = cluster.SpectralClustering(n_clusters, **kwargs)
self.model_name = "SpectralClustering"
def _preproc_data(self, X):
if isinstance(X, pd.DataFrame):
return X.as_matrix()
return X
def fit(self, x, y=None):
"""Creates an affinity matrix for X using the selected affinity,
then applies spectral clustering to this affinity matrix.
Parameters
----------
x : The input samples, shape = [n_samples, n_features]
Returns
-------
self : object
Returns self.
"""
self.X = x
self.base_model.fit(self._preproc_data(x), y)
self.labels = self.base_model.labels_
n_clusters_ = len(set(self.labels)) - (1 if -1 in self.labels else 0)
if n_clusters_ != self.n_clusters:
logging.warning("Clustering model provides different cluster count than expected: %s instead of %s" % (
n_clusters_, self.n_clusters))
self.n_clusters = n_clusters_
return self
def get_mean_values(self):
data = pd.DataFrame(self.X)
data['label'] = pd.Series(self.labels)
result = data.groupby('label').mean()
result['Cluster size'] = data.groupby('label').count().iloc[:, 0]
return result
def get_labels(self):
return self.labels
def get_silhouette_score(self):
from sklearn.metrics import silhouette_score
return silhouette_score(self.X, self.labels,
metric='euclidean')
def draw_clusters(self, method=None, title=None, axis=None, show=True, **kwargs):
data = self._preproc_data(self.X)
reduced_data = PCA(n_components=2).fit_transform(data)
if axis is None:
draw_obj = plt
else:
draw_obj = axis
if title is None:
title = self.model_name + " %s clusters total" % self.n_clusters
if method == "areas":
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
# Step size of the mesh. Decrease to increase the quality of the VQ.
parts_n = kwargs.pop("parts_n", 10)
h_x = (x_max - x_min) / parts_n # point in the mesh [x_min, m_max]x[y_min, y_max].
h_y = (y_max - y_min) / parts_n # point in the mesh [x_min, m_max]x[y_min, y_max].
xx, yy = np.meshgrid(np.arange(x_min, x_max, h_x), np.arange(y_min, y_max, h_y))
neighbors_classifier = KNeighborsClassifier().fit(reduced_data, self.labels)
# Obtain labels for each point in mesh. Use last trained model.
Z = neighbors_classifier.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
draw_obj.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
draw_obj.plot(reduced_data[:, 0], reduced_data[:, 1], 'o', markersize=9)
if "cluster_centers_" in self.base_model.__dict__:
# Plot the centroids as a white X
centroids = self.base_model.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=9,
color='w', zorder=10)
# plt.xlim(x_min, x_max)
# plt.ylim(y_min, y_max)
# plt.xticks(())
# plt.yticks(())
elif method == "dendrogram":
def fancy_dendrogram(*args, **kwargs):
max_d = kwargs.pop('max_d', None)
if max_d and 'color_threshold' not in kwargs:
kwargs['color_threshold'] = max_d
annotate_above = kwargs.pop('annotate_above', 0)
ddata = dendrogram(*args, **kwargs)
if not kwargs.get('no_plot', False):
plt.xlabel('sample index or (cluster size)')
plt.ylabel('distance')
for i, d, c in zip(ddata['icoord'], ddata['dcoord'], ddata['color_list']):
x = 0.5 * sum(i[1:3])
y = d[1]
if y > annotate_above:
plt.plot(x, y, 'o', c=c)
plt.annotate("%.3g" % y, (x, y), xytext=(0, -5),
textcoords='offset points',
va='top', ha='center')
if max_d:
plt.axhline(y=max_d, c='k')
return ddata
plt.xlabel('sample index or (cluster size)')
plt.ylabel('distance')
fancy_dendrogram(
self.base_model.Z,
truncate_mode='lastp', # show only the last p merged clusters
p=400, # show only the last p merged clusters
leaf_rotation=90., # rotates the x axis labels
leaf_font_size=8., # font size for the x axis labels
max_d=self.base_model.get_max_distance(),
annotate_above=3, # useful in small plots so annotations don't overlap
show_contracted=True, # to get a distribution impression in truncated branches
)
else:
core_samples_mask = np.zeros_like(self.labels, dtype=bool)
if 'core_sample_indices_' in self.base_model.__dict__:
core_samples_mask[self.base_model.core_sample_indices_] = True
# Black removed and is used for noise instead.
unique_labels = set(self.labels)
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = 'k'
class_member_mask = (self.labels == k)
xy = reduced_data[class_member_mask & core_samples_mask]
xy2 = reduced_data[class_member_mask & ~core_samples_mask]
draw_obj.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=14)
draw_obj.plot(xy2[:, 0], xy2[:, 1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=9)
patch = mpatches.Rectangle([0, 0], 0, 0, color="black", label="score = " + str(self.get_silhouette_score()))
draw_obj.legend(handles=[patch])
if axis is None:
plt.title(title)
else:
axis.set_title(title)
if show:
plt.show()
class ClusterizationModel:
"""Model for clusterization
Parameters
-----------
n_clusters : integer, optional
The dimension of the projection subspace.
Attributes
----------
n_clusters : int, number of classes
labels : list of int
Labels of each point
"""
def __init__(self, n_clusters=8, model="agglomerative", **kwargs):
self.n_clusters = n_clusters
self.labels = []
self.X = []
self.model_name = model
if model == "hierarchy":
self.base_model = HierarchyModel(n_clusters, **kwargs)
elif model == "KMeans":
self.base_model = KMeans(n_clusters, **kwargs)
elif model == "agglomerative":
self.base_model = AgglomerativeClustering(linkage='ward',
n_clusters=n_clusters,
**kwargs)
elif model == "dbscan":
self.base_model = DBSCAN(**kwargs)
else:
self.base_model = cluster.SpectralClustering(n_clusters, **kwargs)
self.model_name = "SpectralClustering"
def _preproc_data(self, X):
if isinstance(X, pd.DataFrame):
return X.as_matrix()
return X
def fit(self, x, y=None):
"""Creates an affinity matrix for X using the selected affinity,
then applies spectral clustering to this affinity matrix.
Parameters
----------
x : The input samples, shape = [n_samples, n_features]
Returns
-------
self : object
Returns self.
"""
self.X = x
self.base_model.fit(self._preproc_data(x), y)
self.labels = self.base_model.labels_
n_clusters_ = len(set(self.labels)) - (1 if -1 in self.labels else 0)
if n_clusters_ != self.n_clusters:
logging.warning(
"Clustering model provides different cluster count than expected: %s instead of %s"
% (n_clusters_, self.n_clusters))
self.n_clusters = n_clusters_
return self
def get_mean_values(self):
data = pd.DataFrame(self.X)
data['label'] = pd.Series(self.labels)
result = data.groupby('label').mean()
result['Cluster size'] = data.groupby('label').count().iloc[:, 0]
return result
def get_labels(self):
return self.labels
def get_silhouette_score(self):
from sklearn.metrics import silhouette_score
return silhouette_score(self.X, self.labels, metric='euclidean')
def draw_clusters(self,
method=None,
title=None,
axis=None,
show=True,
**kwargs):
data = self._preproc_data(self.X)
reduced_data = PCA(n_components=2).fit_transform(data)
if axis is None:
draw_obj = plt
else:
draw_obj = axis
if title is None:
title = self.model_name + " %s clusters total" % self.n_clusters
if method == "areas":
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:,
0].min() - 1, reduced_data[:,
0].max() + 1
y_min, y_max = reduced_data[:,
1].min() - 1, reduced_data[:,
1].max() + 1
# Step size of the mesh. Decrease to increase the quality of the VQ.
parts_n = kwargs.pop("parts_n", 10)
h_x = (
x_max - x_min
) / parts_n # point in the mesh [x_min, m_max]x[y_min, y_max].
h_y = (
y_max - y_min
) / parts_n # point in the mesh [x_min, m_max]x[y_min, y_max].
xx, yy = np.meshgrid(np.arange(x_min, x_max, h_x),
np.arange(y_min, y_max, h_y))
neighbors_classifier = KNeighborsClassifier().fit(
reduced_data, self.labels)
# Obtain labels for each point in mesh. Use last trained model.
Z = neighbors_classifier.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
draw_obj.imshow(Z,
interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto',
origin='lower')
draw_obj.plot(reduced_data[:, 0],
reduced_data[:, 1],
'o',
markersize=9)
if "cluster_centers_" in self.base_model.__dict__:
# Plot the centroids as a white X
centroids = self.base_model.cluster_centers_
plt.scatter(centroids[:, 0],
centroids[:, 1],
marker='x',
s=169,
linewidths=9,
color='w',
zorder=10)
# plt.xlim(x_min, x_max)
# plt.ylim(y_min, y_max)
# plt.xticks(())
# plt.yticks(())
elif method == "dendrogram":
def fancy_dendrogram(*args, **kwargs):
max_d = kwargs.pop('max_d', None)
if max_d and 'color_threshold' not in kwargs:
kwargs['color_threshold'] = max_d
annotate_above = kwargs.pop('annotate_above', 0)
ddata = dendrogram(*args, **kwargs)
if not kwargs.get('no_plot', False):
plt.xlabel('sample index or (cluster size)')
plt.ylabel('distance')
for i, d, c in zip(ddata['icoord'], ddata['dcoord'],
ddata['color_list']):
x = 0.5 * sum(i[1:3])
y = d[1]
if y > annotate_above:
plt.plot(x, y, 'o', c=c)
plt.annotate("%.3g" % y, (x, y),
xytext=(0, -5),
textcoords='offset points',
va='top',
ha='center')
if max_d:
plt.axhline(y=max_d, c='k')
return ddata
plt.xlabel('sample index or (cluster size)')
plt.ylabel('distance')
fancy_dendrogram(
self.base_model.Z,
truncate_mode='lastp', # show only the last p merged clusters
p=400, # show only the last p merged clusters
leaf_rotation=90., # rotates the x axis labels
leaf_font_size=8., # font size for the x axis labels
max_d=self.base_model.get_max_distance(),
annotate_above=
3, # useful in small plots so annotations don't overlap
show_contracted=
True, # to get a distribution impression in truncated branches
)
else:
core_samples_mask = np.zeros_like(self.labels, dtype=bool)
if 'core_sample_indices_' in self.base_model.__dict__:
core_samples_mask[self.base_model.core_sample_indices_] = True
# Black removed and is used for noise instead.
unique_labels = set(self.labels)
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = 'k'
class_member_mask = (self.labels == k)
xy = reduced_data[class_member_mask & core_samples_mask]
xy2 = reduced_data[class_member_mask & ~core_samples_mask]
draw_obj.plot(xy[:, 0],
xy[:, 1],
'o',
markerfacecolor=col,
markeredgecolor='k',
markersize=14)
draw_obj.plot(xy2[:, 0],
xy2[:, 1],
'o',
markerfacecolor=col,
markeredgecolor='k',
markersize=9)
patch = mpatches.Rectangle([0, 0],
0,
0,
color="black",
label="score = " +
str(self.get_silhouette_score()))
draw_obj.legend(handles=[patch])
if axis is None:
plt.title(title)
else:
axis.set_title(title)
if show:
plt.show()
