def goKmeans():
clusteringNum = request.form['clusteringNum']
dataset = json.loads(request.form.get('dataset'))
if (clusteringNum == '' or int(float(clusteringNum)) < 2):
clusteringNum = 2
dataset = np.array(dataset)
#dataset = np.delete(dataset, 0, 1)
new_list = list(
list(float(a) for a in b if BN.is_number(a)) for b in dataset)
kmeans = KMeans(n_clusters=int(float(clusteringNum)),
random_state=0).fit(new_list)
new_list_as_array = np.array(new_list)
SilhouetteVisualize = SilhouetteVisualizer(kmeans)
SilhouetteVisualize.fit(new_list_as_array)
if (len(new_list) > 10):
k_upper_bound = 10
else:
k_upper_bound = len(new_list)
KElbowVisualize = KElbowVisualizer(KMeans(), k=k_upper_bound)
KElbowVisualize.fit(new_list_as_array) # Fit the data to the visualizer
silhouette = SilhouetteVisualize.silhouette_score_
elbow = KElbowVisualize.elbow_value_
return jsonify({
'inputArray': list(new_list),
'kmeansLabels': (kmeans.labels_.tolist()),
'elbowValue': str(elbow),
'silhouetteValue': ('%.3f' % silhouette)
})
def investigate_cluster_nr(min, max, pca_scores):
rows, cols = 6, 6 #REMEMBER TO ADJUST
fig, ax = plt.subplots(rows, cols, figsize=(15, 8))
row, col, first = 0, 0, 0
for i in range(min, max): #was 5, 30
if first != 0:
if col < cols - 1:
col += 1
elif col == cols - 1:
col = 0
row += 1
first = 1
kmeans_pca = KMeans(n_clusters=i, init='k-means++', random_state=42)
kmeans_pca.fit(pca_scores)
visualizer = SilhouetteVisualizer(kmeans_pca,
colors='yellowbrick',
ax=ax[row][col])
visualizer.fit(pca_scores)
def silhouette(ax):
from sklearn.cluster import KMeans
from yellowbrick.cluster import SilhouetteVisualizer
from sklearn.datasets import make_blobs
kws = {
'centers': 8,
'n_samples': 1000,
'n_features': 12,
'shuffle': True,
}
X = make_blobs()
X, y = make_blobs(centers=8)
visualizer = SilhouetteVisualizer(KMeans(6), ax=ax)
visualizer.title = "Silhouette Clusters for K-Means (k=6) on an 8 Blob Dataset"
visualizer.fit(X)
return visualizer
def shiloutte_score_plot(self, directory):
self._check_model()
plt.figure(figsize=(10, 10))
visualizer = SilhouetteVisualizer(
self.best_estimator_.named_steps['clustering'],
colors='yellowbrick',
is_fitted=True)
visualizer.fit(self.data_preprocessed)
visualizer.show(directory + "/shiloutte_score.png")
visualizer.finalize()
plt.close()
def get_elbow_plot(X):
output_text = ""
try:
model = KMeans(random_state=40, )
elbow_score = KElbowVisualizer(model, k=(1, 30))
elbow_score.fit(X)
elbow_value = elbow_score.elbow_value_
model = KMeans(elbow_value, random_state=42)
silhoutte_score = SilhouetteVisualizer(model, colors='yellowbrick')
silhoutte_score.fit(X)
output_text = """The optimal number of clusters is """ + \
str(silhoutte_score.n_clusters_) + """ and the silhouette score is """ + \
str(np.round(silhoutte_score.silhouette_score_, 2))
except ValueError as e:
print(e)
return output_text
def sc(f, g, krange):
df = pd.read_table(f, index_col=0, header=0)
if g == 'c':
df = df.T
min_k = krange[0]
max_k = krange[1]
num_fig = max_k - min_k + 1
num_row = math.ceil(num_fig / 3.0)
plt.figure(figsize=(12, num_row * 4))
for k in range(min_k, max_k + 1):
model = KMeans(k, random_state=1)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
plt.subplot(num_row, 3, k - min_k + 1)
visualizer.fit(df)
plt.title('k=%d silhouette score %0.2f' %
(k, visualizer.silhouette_score_))
plt.grid(0)
plt.tight_layout()
plt.savefig(os.path.basename(f).replace('.txt', '_silhouette.pdf'))
def plot_cluster_silhouette(estimator, dataset, version):
visualizer = SilhouetteVisualizer(estimator, colors='yellowbrick')
visualizer.fit(data.DATA[dataset][version]['x_train'])
visualizer.show(
f'{PLOTS_FOLDER}/{dataset}/{version}/{dataset}_{version}_{estimator.__class__.__name__}_cluster_silhouettes_k{estimator.n_clusters}.png'
)
plt.clf()
def log_silhouette_chart(model, X, experiment=None, **kwargs):
"""Log Silhouette Coefficients charts for KMeans clusterer.
Charts are computed for j = 2, 3, ..., n_clusters.
Make sure you created an experiment by using ``neptune.create_experiment()`` before you use this method.
Tip:
Check `Neptune documentation <https://docs.neptune.ai/integrations/scikit_learn.html>`_ for the full example.
Args:
model (:obj:`KMeans`):
| KMeans object.
X (:obj:`ndarray`):
| Training instances to cluster.
experiment (:obj:`neptune.experiments.Experiment`, optional, default is ``None``):
| Neptune ``Experiment`` object to control to which experiment you log the data.
| If ``None``, log to currently active, and most recent experiment.
kwargs:
KMeans parameters.
Returns:
``None``
Examples:
.. code:: python3
km = KMeans(n_init=11, max_iter=270)
X, y = make_blobs(n_samples=579, n_features=17, centers=7, random_state=28743)
neptune.init('my_workspace/my_project')
neptune.create_experiment()
log_silhouette_chart(km, X=X, n_clusters=12)
"""
assert isinstance(model,
KMeans), 'Model should be sklearn KMeans instance.'
exp = _validate_experiment(experiment)
model.set_params(**kwargs)
n_clusters = model.get_params()['n_clusters']
for j in range(2, n_clusters + 1):
model.set_params(**{'n_clusters': j})
model.fit(X)
try:
fig, ax = plt.subplots()
visualizer = SilhouetteVisualizer(model, is_fitted=True, ax=ax)
visualizer.fit(X)
visualizer.finalize()
exp.log_image(
'charts_sklearn',
fig,
image_name='Silhouette Coefficients for k={}'.format(j))
plt.close(fig)
except Exception as e:
print('Did not log Silhouette Coefficients chart. Error {}'.format(
e))
def clustering(self, k):
word_vectors = self.__model_p__.wv
KM_model = KMeans(n_clusters=k,
max_iter=1000,
random_state=True,
n_init=50).fit(X=word_vectors.vectors)
center_closest = []
for i in range(k):
center_closest.append([
el[0] for el in word_vectors.similar_by_vector(
KM_model.cluster_centers_[i], topn=15, restrict_vocab=None)
])
metric_str = 'euclidean'
score = silhouette_score(word_vectors.vectors,
KM_model.predict(word_vectors.vectors),
metric=metric_str)
print("silhouette_score:", score)
SVmodel = SilhouetteVisualizer(KM_model, is_fitted=True)
SVmodel.fit(word_vectors.vectors)
SVmodel.show()
words = pd.DataFrame(word_vectors.vocab.keys(), columns=['words'])
words['vectors'] = words.words.apply(lambda x: word_vectors[f'{x}'])
words['cluster'] = words.vectors.apply(
lambda x: KM_model.predict([np.array(x)]))
words.cluster = words.cluster.apply(lambda x: x[0])
words['closeness_score'] = words.apply(
lambda x: 1 / (KM_model.transform([x.vectors]).min()), axis=1)
return KM_model, center_closest, score, words
def silhouettevisual(model, X, graph):
visualizer = SilhouetteVisualizer(
model,
colors='yellowbrick',
title=" Silhouette Plot of KMeans Clustering for " + graph)
visualizer.fit(X)
visualizer.show()
def do_kmeans(df, k):
k_means = KMeans(init='k-means++', n_clusters=k, n_init=10, max_iter=1000, random_state=40)
k_means.fit(df)
wcss = k_means.inertia_
sil = silhouette_score(df, k_means.labels_)
plt.style.use('default');
sample_silhouette_values = silhouette_samples(df, k_means.labels_)
sizes = 200*sample_silhouette_values
plt.figure(figsize=(16, 10));
plt.grid(True);
plt.scatter(df.iloc[:, 0], df.iloc[:, 1], s=sizes, c=k_means.labels_)
plt.scatter(k_means.cluster_centers_[:, 0], k_means.cluster_centers_[:, 1], marker='x', s=300, c="black")
plt.title("K-Means (K={}, WCSS={:.2f}, Sil={:.2f})".format(k, wcss, sil), fontsize=20);
plt.xlabel('Age', fontsize=22);
plt.ylabel('Income', fontsize=22);
plt.xticks(fontsize=18);
plt.yticks(fontsize=18);
plt.show()
visualizer = SilhouetteVisualizer(k_means)
visualizer.fit(df)
visualizer.poof()
fig = visualizer.ax.get_figure();
print("K={}, WCSS={:.2f}, Sil={:.2f}".format(k, wcss, sil))
def kmeans_exp():
with open('features_GMM.csv', mode='r') as feature_file:
feature_reader = csv.reader(feature_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
i = 0
for fs in feature_reader:
if i > 0:
print(f"user n#{i}")
print(fs)
fs.pop()
df_training = pd.DataFrame()
training_list, testing_dict, training_fs_list, validation_fs_dict = load_dataset(
i)
training_list = training_list[0:16] + training_list[
21:25] + training_list[36:40]
for element in training_list:
df_training = df_training.append(element)
# knn = KNeighborsClassifier()
model = KMeans(n_clusters=6, random_state=42).fit(df_training)
for signature in testing_dict['genuine']:
cluster = model.predict(signature)
print("testing signature:")
occurences = Counter(cluster)
print(occurences)
visualizer = SilhouetteVisualizer(model)
visualizer.fit(df_training)
visualizer.show()
i += 1
def silhouette_plot(self, latent_data):
"""Silhouette Plots and Scores to determine optimal K in KMeans"""
fig, ax = plt.subplots(2, 2, figsize=(15, 8))
for i in [2, 3, 4, 5]:
'''
Create KMeans instance for different number of clusters
'''
km = KMeans(n_clusters=i, max_iter=10, random_state=0)
q, mod = divmod(i, 2)
'''
Create SilhouetteVisualizer instance with KMeans instance
Fit the visualizer
'''
visualizer = SilhouetteVisualizer(km,
colors='yellowbrick',
ax=ax[q - 1][mod])
visualizer.fit(latent_data)
fig.suptitle('Silhouette Plots for 2, 3, 4, 5 Cluster Centers',
fontsize=18)
plt.savefig('Silhouette-Visualization.png', bbox_inches='tight')
plt.close('all')
def silhouette(ax=None):
X, y = make_blobs(centers=12, n_samples=1000, n_features=16, shuffle=True)
viz = SilhouetteVisualizer(KMeans(9), ax=ax)
viz.fit(X)
viz.finalize()
return viz
def showSilhouette():
# Make 8 blobs dataset
X, y = make_blobs(centers=8)
# Instantiate the clustering model and visualizer
model = MiniBatchKMeans(6)
visualizer = SilhouetteVisualizer(model)
visualizer.fit(X) # Fit the training data to the visualizer
visualizer.poof() # Draw/show/poof the data
def create_silhouette_chart(model, X, **kwargs):
"""Create silhouette coefficients charts for KMeans clusterer.
Charts are computed for j = 2, 3, ..., n_clusters.
Tip:
Check Sklearn-Neptune integration
`documentation <https://docs-beta.neptune.ai/essentials/integrations/machine-learning-frameworks/sklearn>`_
for the full example.
Args:
model (:obj:`KMeans`):
| KMeans object.
X (:obj:`ndarray`):
| Training instances to cluster.
kwargs:
KMeans parameters.
Returns:
``neptune.types.FileSeries`` object that you can assign to run's ``base_namespace``.
Examples:
.. code:: python3
import neptune.new.integrations.sklearn as npt_utils
km = KMeans(n_init=11, max_iter=270)
X, y = make_blobs(n_samples=579, n_features=17, centers=7, random_state=28743)
run = neptune.init(project='my_workspace/my_project')
run['kmeans/silhouette'] = npt_utils.create_silhouette_chart(km, X, n_clusters=12)
"""
assert isinstance(model,
KMeans), 'Model should be sklearn KMeans instance.'
charts = []
model.set_params(**kwargs)
n_clusters = model.get_params()['n_clusters']
for j in range(2, n_clusters + 1):
model.set_params(**{'n_clusters': j})
model.fit(X)
try:
fig, ax = plt.subplots()
visualizer = SilhouetteVisualizer(model, is_fitted=True, ax=ax)
visualizer.fit(X)
visualizer.finalize()
charts.append(neptune.types.File.as_image(fig))
plt.close(fig)
except Exception as e:
print('Did not log Silhouette Coefficients chart. Error {}'.format(
e))
return neptune.types.FileSeries(charts)
def Silhouette_plot(x, from_k, to_k):
sil_score = []
for k in range(from_k, to_k + 1):
#Instatiate the clustering model and visualizer
m = KMeans(n_clusters=k)
visualizer = SilhouetteVisualizer(m)
visualizer.fit(x)
#Draw/show/poof the data
visualizer.poof()
sil_score.append([visualizer.silhouette_score_.round(3), k])
return sil_score
def silhouette(matrix, k):
"""
This function is also not explicitly used since it shows the decided 'k' is good or not.
:param matrix: tf-idf matrix
:param k: decided k (from elbow matrix)
:return: show graph with all cluster's internal similarities and uniqueness with other clusters.
"""
model_kmeans = KMeans(n_clusters=k, max_iter=200)
silhouette = SilhouetteVisualizer(model_kmeans)
silhouette.fit(matrix)
silhouette.poof()
def silhouette_plot(text, model, cv):
'''
Loads in a saved model and produces a silhouette score plot
'''
path = 'models/{}'.format(model)
pipe = load(path)
kmeans = pipe.named_steps['kmeans']
svd = pipe.named_steps['truncatedsvd']
X = svd.fit_transform(cv)
visualizer = SilhouetteVisualizer(kmeans, colors='sns_deep')
visualizer.fit(X)
visualizer.show(outpath="plots/Silhouette.png")
plt.close()
def clustering(fname="clustering.png"):
# Create side-by-side axes grid
_, axes = plt.subplots(ncols=2, figsize=(18, 6))
X, y = make_blobs(centers=7)
# Add K-Elbow to the left
oz = KElbowVisualizer(MiniBatchKMeans(), k=(3, 12), ax=axes[0])
oz.fit(X, y)
oz.finalize()
# Add SilhouetteVisualizer to the right
oz = SilhouetteVisualizer(Birch(n_clusters=5), ax=axes[1])
oz.fit(X, y)
oz.finalize()
# Save figure
path = os.path.join(FIGURES, fname)
plt.tight_layout()
plt.savefig(path)
def main():
xtrain1, xtest1, ytrain1, ytest1, xtrain2, xtest2, ytrain2, ytest2 = load_data(
)
km = KMeans(4)
visualizer = SilhouetteVisualizer(km, colors='yellowbrick')
visualizer.fit(xtrain1)
visualizer.show()
ytest = km.fit_predict(xtrain1)
print(metrics.homogeneity_score(ytrain1, ytest))
score(xtrain2, 20, ytrain2)
elbowplot(xtrain2, 20, "distortion",
"K Means Clustering Distortion vs Number of Clusters dat2",
"figs/kmeans/kmeans_elbow_dat2.png")
elbowplot(xtrain1, 100, "distortion",
"K Means Clustering Distortion vs Number of Clusters dat1",
"figs/kmeans/kmeans_elbow_dat1.png")
elbowplot(xtrain2,
40,
"silhouette",
"K Means Clustering Silhouette Score vs Number of Clusters dat2",
"figs/kmeans/kmeans_silhouette_dat2.png",
elbow=False)
elbowplot(xtrain1,
100,
"silhouette",
"K Means Clustering Silhouette Score vs Number of Clusters dat1",
"figs/kmeans/kmeans_silhouette_dat1.png",
elbow=False)
elbowplot(
xtrain2,
20,
"calinski_harabasz",
"K Means Clustering Calinski Harabasz Score vs Number of Clusters dat2",
"figs/kmeans/kmeans_calinski_dat2.png",
elbow=False)
elbowplot(
xtrain1,
100,
"calinski_harabasz",
"K Means Clustering Calinski Harabasz Score vs Number of Clusters dat1",
"figs/kmeans/kmeans_calinski_dat1.png",
elbow=False)
def silhoutte_yellowbrick(
X,
y,
features,
):
plt.switch_backend('agg')
plt.clf()
X_train, X_test, y_train, y_test = train_test_split(X[features],
y,
stratify=y,
test_size=0.01)
X = pd.DataFrame(X_test, columns=features)
y = pd.Series(y_test)
n_clusters = y.nunique()
model = MiniBatchKMeans(n_clusters)
visualizer_sil = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer_sil.fit(X)
visualizer_sil.finalize()
return plt
def kmeans_silhouette_plots(tfidf, num_clusters=[3, 5, 7, 9, 11]):
'''
Vectorizer results are normalized, which makes KMeans behave as
spherical k-means for better results. Since LSA/SVD results are
not normalized, we have to redo the normalization.
The best silhouette value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.
'''
print('\nUse kmeans silhouette score to visualize Silhouette Coefficients')
for k in num_clusters:
start = datetime.now()
svd = TruncatedSVD(n_components=50, n_iter=10, random_state=0)
normalizer = Normalizer(copy=False)
lsa = make_pipeline(svd, normalizer)
reduced = lsa.fit_transform(tfidf)
model = KMeans(n_clusters=k, init='k-means++')
# Instantiate the clustering model and visualizer
visualizer = SilhouetteVisualizer(model)
# Fit the training data to the visualizer
visualizer.fit(reduced)
visualizer.finalize()
filename = r'images/silhouette_plots/kmeans_silh_plot_' + str(
k) + '_clusters_' + str(tfidf.shape[0]) + '_docs.png'
plt.savefig(filename)
plt.close()
end = datetime.now()
print(' ' + filename)
print(" Time taken: {}".format(end - start))
visualizer.poof()
model = KMeans(
n_clusters=4,
random_state=0,
n_jobs=-1,
)
visualizer = InterclusterDistance(model)
visualizer.fit(results) # Fit the data to the visualizer
# Finalize and render the figure
visualizer.show(outpath="charts/income.k-means.PCA.InterclusterDistance.png")
visualizer.poof()
model = KMeans(n_clusters=4, random_state=0)
visualizer = SilhouetteVisualizer(model)
visualizer.fit(results) # Fit the data to the visualizer
# Finalize and render the figure
visualizer.show(outpath="charts/income.k-means.PCA.SilhouetteVisualizer.png")
lowest_bic = np.infty
bic = []
n_components_range = range(1, 7)
cv_types = ['spherical', 'tied', 'diag', 'full']
for cv_type in cv_types:
for n_components in n_components_range:
# Fit a Gaussian mixture with EM
gmm = GaussianMixture(n_components=n_components,
covariance_type=cv_type)
gmm.fit(results)
# Clustering Evaluation Imports
from functools import partial
from sklearn.cluster import MiniBatchKMeans
from sklearn.datasets import make_blobs as sk_make_blobs
from yellowbrick.cluster import SilhouetteVisualizer
# Helpers for easy dataset creation
N_SAMPLES = 1000
N_FEATURES = 12
SHUFFLE = True
# Make blobs partial
make_blobs = partial(sk_make_blobs,
n_samples=N_SAMPLES,
n_features=N_FEATURES,
shuffle=SHUFFLE)
if __name__ == '__main__':
# Make 8 blobs dataset
X, y = make_blobs(centers=8)
# Instantiate the clustering model and visualizer
model = MiniBatchKMeans(6)
visualizer = SilhouetteVisualizer(model)
visualizer.fit(X) # Fit the training data to the visualizer
visualizer.poof(outpath="images/silhouette.png") # Draw/show/poof the data
silhouette_avg = silhouette_score(x, m.labels_).round(3)
sils.append([silhouette_avg, k])
#Compute the silhouette score for each sample
# sample_silhouette_value = silhouette_samples(x,m.labels_)
# print(sample_silhouette_value)
return sils
ss = sil_score(x, 2, 5)
print(f'score={ss}')
print(f'optinum number of clusters ={max(ss)[1]}')
#
#Visualize Silhouette
#Instantiate the clustering model and visualizer
model = KMeans(n_clusters=3)
visualizer = SilhouetteVisualizer(model)
#fit the training data to visualizer
visualizer.fit(x)
#Draw/show/poof the data
visualizer.poof()
print(visualizer.silhouette_score_) #near 1 is good
def Silhouette_plot(x, from_k, to_k):
sil_score = []
for k in range(from_k, to_k + 1):
#Instatiate the clustering model and visualizer
m = KMeans(n_clusters=k)
visualizer = SilhouetteVisualizer(m)
visualizer.fit(x)
#Draw/show/poof the data
model = KMeans(n_clusters=k, random_state=1) # 모델 선택
model.fit(X) # 모델 훈련
kmeans_models.append(model)
inertias.append(model.inertia_)
silhouettes.append(silhouette_score(X, model.labels_))
print('inertias:', inertias)
print('silhouettes:', silhouettes)
# k - 실루엣 점수 그래프
plt.plot(k_ranges[1:], silhouettes[1:], marker='o')
plt.xlabel('k')
plt.ylabel('silhouette score')
plt.show()
# 실루엣 점수(silhouette score) = (b - a) / max(a, b)
# a: 동일한 클러스터 안에서 다른 샘플들과의 거리의 평균
# b: 가장 가까운 클러스터까지의 평균 거리
# -1 <= ss <= 1
# 실루엣 점수가 큰 모델의 클러스터 개수가 적절한 클러스터 개수
# ss = 1: 샘플들이 자기 클러스터 안에 잘 모여 있고,
# 다른 클러스터와는 멀리 떨어져 있는 경우
# ss = 0: 샘들들이 클러스터 경계에 몰려 있는 경우
# ss = -1: 샘플들이 잘못된 클러스터에 포함되는 경우.
# 실루엣 다이어그램
# pip install yellowbrick
for model in kmeans_models[1:]: # 훈련된 각각의 KMeans 모델들에 대해서
visualizer = SilhouetteVisualizer(model, color='yellowbrick')
visualizer.fit(X)
visualizer.show()
def silhouette():
X, _ = make_blobs(centers=8)
oz = SilhouetteVisualizer(MiniBatchKMeans(6), ax=newfig())
oz.fit(X)
savefig(oz, "silhouette")
def explore_DBSCAN_clustering(
df,
num_cols=None,
metric="euclidean",
eps=[0.5],
min_samples=[5],
include_silhouette=True,
include_PCA=True,
random_state=None,
):
"""fit and plot DBSCAN clustering algorithms
Parameters
----------
df : pandas.DataFrame
the dataset, should be transformed with StandardScaler
num_cols : list, optional
list of numeric column names, in case of None, get all numeric columns
metric : str, optional
metric, by default "euclidean"
eps : list, optional
list of eps hyperparams, by default [0.5]
min_samples: list, optional
list of min_samples hyperparams, by default [5]
include_silhouette : bool, optional
whether Silhouette plots should be generated, by default True
include_PCA : bool, optional
whether PCA plots should be generated, by default True
random_state : int, optional
a number determines random number generation for centroid initialization, by default None
Returns
-------
Tuple
list
a list of n_clusters values returned by DBSCAN models
dict
a dictionary with key=type of plot, value=list of plots
Examples
-------
>>> original_df = pd.read_csv("/data/menu.csv")
>>> numeric_features = eda.get_numeric_columns(original_df)
>>> numeric_transformer = make_pipeline(SimpleImputer(), StandardScaler())
>>> preprocessor = make_column_transformer(
>>> (numeric_transformer, numeric_features)
>>> )
>>> df = pd.DataFrame(
>>> data=preprocessor.fit_transform(original_df), columns=numeric_features
>>> )
>>> n_clusters, dbscan_plots = explore_DBSCAN_clusterting(df)
"""
if num_cols is None:
num_cols = get_numeric_columns(df)
else:
_verify_numeric_cols(df, num_cols)
x = df[num_cols]
results = {}
n_clusters = []
s_plots = []
pca_plots = []
print("------------------------")
print("DBSCAN CLUSTERING")
print("------------------------")
for e in eps:
for ms in min_samples:
dbscan = DBSCAN(eps=e, min_samples=ms, metric=metric)
dbscan.fit(x)
k = len(set(dbscan.labels_)) - 1 # exclduing -1 labels
n_clusters.append(k)
print(f"eps={e}, min_samples={ms}, n_cluster={k}")
if include_silhouette and k > 0:
# generat Silhouette plot
dbscan.n_clusters = k
dbscan.predict = lambda x: dbscan.labels_
fig, ax = plt.subplots()
s_visualizer = SilhouetteVisualizer(dbscan, colors="yellowbrick", ax=ax)
s_visualizer.fit(x)
s_visualizer.show()
s_plots.append(fig)
# plt.clf()
plt.close()
else:
s_plots.append(None)
if include_PCA:
# genrate PCA plot
p_lot = plot_pca_clusters(x, dbscan.labels_, random_state=random_state)
pca_plots.append(p_lot)
else:
pca_plots.append(None)
results["Silhouette"] = s_plots
results["PCA"] = pca_plots
return n_clusters, results
# Load the data from the files in the corpus
for cat in categories:
for name in os.listdir(os.path.join(path, cat)):
files.append(os.path.join(path, cat, name))
target.append(cat)
with open(os.path.join(path, cat, name), 'r') as f:
data.append(f.read())
# Return the data bunch for use similar to the newsgroups example
return Bunch(
categories=categories,
files=files,
data=data,
target=target,
)
corpus = load_corpus('hobbies')
tfidf = TfidfVectorizer(stop_words='english')
docs = tfidf.fit_transform(corpus.data)
# Instantiate the clustering model and visualizer
visualizer = SilhouetteVisualizer(KMeans(n_clusters=6))
visualizer.fit(docs)
visualizer.poof()
# Instantiate the clustering model and visualizer
visualizer = KElbowVisualizer(KMeans(), metric='silhouette', k=[4,10])
visualizer.fit(docs)
visualizer.poof()
'869Individual_AssignmentQ1_graphs/jelwery-kmeans-elbow-interia.png')
plt.figure()
plt.grid(True)
plt.plot(list(silhouettes.keys()), list(silhouettes.values()))
plt.title('K-Means, Elbow Method')
plt.xlabel("Number of clusters, K")
plt.ylabel("Silhouette")
plt.savefig(
'869Individual_AssignmentQ1_graphs/jelwery-kmeans-elbow-silhouette.png')
# In[18]:
###sklearn.metrics.davies_bouldin_score(X, k_means.labels_)
visualizer = SilhouetteVisualizer(k_means)
visualizer.fit(X)
visualizer.poof()
fig = visualizer.ax.get_figure()
fig.savefig(
'869Individual_AssignmentQ1_graphs/jelwery-kmeans-5-silhouette.png',
transparent=False)
# # Answer to Question [1], Part [c]
# In[19]:
###Intepretting the Clusters
###Means
k_means.cluster_centers_