GDM_functions

gistfile1.txt

prinf("hello")

#concataning eigenvectors & eigenvectorsNeg eigenvectorsConcat = np.concatenate((eigenvectors, eigenvectorsNeg), axis=0)

#concataning targets y & yNeg y = np.repeat(1, 78) yNeg = np.repeat(0, 78) yConcat = np.concatenate((y, yNeg), axis=0)

# creating a dataframe using eigenvectorsConcat & yConcat embeddings = [] for id in range(156): embedding = eigenvectorsConcat[id] #club = KG.nodes[id]['club'] embeddings.append([embedding[0], embedding[1], embedding[2], embedding[3], yConcat[id]]) df = pd.DataFrame(embeddings, columns=['x_a', 'x_b', 'x_c', 'x_d', 'y']) df

`features = ['x_a', 'x_b', 'x_c', 'x_d']
X = dfDataframe[features]
#X

y = dfDataframe['y']
y

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.34, stratify=y, random_state=1)`

`from sklearn.neural_network import MLPClassifier

classifier = 'MLPClassifier'
clf = MLPClassifier(random_state=1, max_iter=390).fit(X_train, y_train)

#print(classifier+ ' classifier for ' +dataset+ ' dataset')
print(classifier+ ' Run Time:')
%timeit clf.fit(X_train, y_train)

print(f"Train score: {clf.score(X_train, y_train)}")
print(f"Test score: {clf.score(X_test, y_test)}")

clf.score(X_test, y_test)
print('Accuracy for ' +classifier+ ' classifier: %.2f' % clf.score(X_test, y_test))

from sklearn import metrics
predicted = clf.predict(X_test)
print(f"Classification report for classifier MLPClassifier:\n"
f"{metrics.classification_report(y_test, predicted)}\n")`

`import seaborn as sns

cols = ['x_a','x_b','x_c','x_d','y']
correlation_coefficient = np.corrcoef(df[cols].values.T)
print(correlation_coefficient)

sns.heatmap(correlation_coefficient, annot=True,
yticklabels = cols, xticklabels=cols)
plt.show()`