from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from data_preprocessing import getEcoliX, getEcoliY, getAdultX, getAdultY, getEcoliTestX, getEcoliTestY import time if __name__ == "__main__": acc = {} np.random.seed(0) ecoliX = getEcoliX() ecoliY = getEcoliY() ecoliTestX = getEcoliTestX() ecoliTestY = getEcoliTestY() adultX = getAdultX() adultX, adultTestX = adultX.iloc[:6000, :], adultX.iloc[6000:, :] adultY = getAdultY() adultY, adultTestY = adultY[:6000, ], adultY[6000:, ] start_time = time.clock() mlp = MLPClassifier(hidden_layer_sizes=(4, ), learning_rate='constant', learning_rate_init=0.2, max_iter=500, early_stopping=True, random_state=5) mlp.fit(ecoliX, ecoliY) print("ecoli", time.clock() - start_time, "seconds") acc['ecoli-train'] = accuracy_score(ecoliY, mlp.predict(ecoliX)) acc['ecoli-test'] = accuracy_score(ecoliTestY, mlp.predict(ecoliTestX)) start_time = time.clock()
from clustertesters import adult_KMeansTestCluster as kmtc from data_preprocessing import getAdultX, getAdultY import matplotlib.pyplot as plt from sklearn.cluster import KMeans import numpy as np if __name__ == "__main__": X = getAdultX() y = getAdultY() tester = kmtc.KMeansTestCluster(X, y, clusters=range(1, 11), plot=True, targetcluster=2, stats=True) tester.run() # plot clustering kmeans = KMeans(n_clusters=3, max_iter=500, init='k-means++') labels = kmeans.fit_predict(X) # View the results # Set the size of the plot plt.figure(figsize=(14, 7)) # Create a colormap colormap = np.array(['red', 'lime', 'black', 'blue', 'yellow']) x1 = X.iloc[:, 0] x2 = X.iloc[:, 1] plt.scatter(x=x1, y=x2, c=colormap[labels], s=40)