ax2.set_xlabel("Feature space for the 1st feature")
ax2.set_ylabel("Feature space for the 2nd feature")
plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
"with n_clusters = %d" % n_clusters),
fontsize=14, fontweight='bold')
bin_count = np.bincount(cluster_labels)
parties_in_cluster = np.bincount(train.labels[cluster_labels == 0].astype(np.int64))
plt.show()
if __name__ == "__main__":
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from data_preparation import prepare_the_data
import os
working_direcotry = r"C:\Users\ORI\Documents\IDC-non-sync\ML_Course\Election\Data\\"
file_name = os.path.join(working_direcotry, r'ElectionsData.csv')
train, validation, test, feature_categorical_dictionary, train_idx, test_idx, number_to_party_dictionary = prepare_the_data(file_name,
working_direcotry)
pca = PCA()
X_r = pca.fit(train.data).transform(train.data)
plot_kmeans(train.data, X_r, pca)
def find_steady_coalition():
working_direcotry = r"C:\Users\ORI\Documents\IDC-non-sync\ML_Course\Election\Data\\"
file_name = os.path.join(working_direcotry, r'ElectionsData.csv')
train, validation, test, feature_categorical_dictionary, train_idx, test_idx, number_to_party_dictionary = prepare_the_data(file_name,
working_direcotry)
good_colation_found = False
for n_clusters in [5,4,3]:
print ("---------------")
linkage = 'ward'
X = train.data
clusters = AgglomerativeClustering(linkage=linkage, n_clusters=n_clusters)
clusters.fit_predict(X)
bin_count_of_kmeans_clusters = np.bincount(clusters.labels_)
normalized_bin_count_of_kmeans_clusters = bin_count_of_kmeans_clusters/np.sum(bin_count_of_kmeans_clusters).astype('float32')
#is there any cluster with more than 50% of the votes?
coalition_exists = np.any(normalized_bin_count_of_kmeans_clusters > 0.5)
print "number_of_clustes {0}".format(n_clusters)
print "coalition_exists: {0} ".format(coalition_exists)
# find all the parties belong to the cluster
biggest_cluster = np.argmax(normalized_bin_count_of_kmeans_clusters)
biggest_cluster_voters = np.bincount(train.labels[clusters.labels_ == biggest_cluster].astype('int64'))
#normalize the votes by the size of their parties:
votes_out_of_party = biggest_cluster_voters/np.bincount( train.labels.astype('int32')).astype('float32')
#commited_to_coalition_parties = partyw with majority of the votes in the cluster
commited_to_coalition_parties = votes_out_of_party > 0.5
percentage_of_voters_in_commited_coalition = np.sum(biggest_cluster_voters[votes_out_of_party > 0.5])*1.0/len(train.labels)*1.0
print percentage_of_voters_in_commited_coalition
if percentage_of_voters_in_commited_coalition> 0.5:
print "coalition found"
parties_in_coalition = number_to_party_dictionary.keys()
print "parties in coalition:{0}".format([number_to_party_dictionary[k] for k in np.array(number_to_party_dictionary.keys())[votes_out_of_party > 0.5]])
break
print ("---------------")
