def task2A():
dataOriginal = sampling.originalData()
#dataOriginal.drop(['A15'], axis=1)
del dataOriginal['A15']
dataRandom = sampling.randomSampling()
del dataRandom['A15']
dataStrat = sampling.stratifiedSampling()
del dataStrat['A15']
def intrinsicDim(data):
x = StandardScaler().fit_transform(data)
A = np.asmatrix(x.T) * np.asmatrix(x)
U, S, V = np.linalg.svd(A)
eigVals = S**2 / np.sum(S**2)
cumulative = [sum(eigVals[:i]) for i in range(1, 15)]
intrinsicDim = pd.DataFrame({
"dimension": [np.arange(1, 15)],
"eigenValues": [eigVals],
"cumulativeEigVals": [cumulative]
})
return intrinsicDim.to_json()
#Original Data
intrinsicDimOrg = intrinsicDim(dataOriginal)
intrinsicDimRand = intrinsicDim(dataRandom)
intrinsicDimStrat = intrinsicDim(dataStrat)
data = [intrinsicDimOrg, intrinsicDimRand, intrinsicDimStrat]
data = pd.DataFrame(data)
data = data.to_json()
return render_template('task.html', taskJS="task2a", data=data)
def task1B():
dataOriginal = sampling.originalData()
#dataOriginal.drop(['A15'], axis=1)
del dataOriginal['A15']
dataRandom = sampling.randomSampling()
del dataRandom['A15']
dataStrat = sampling.stratifiedSampling()
del dataStrat['A15']
def kmeansElbow(data):
dictionary = {}
for i in range(1, 10):
km = KMeans(n_clusters=i)
#kmeans for stratified sampled data
alldistances = km.fit(data)
#totalDistance = np.min(alldistances, axis=1).sum()
dictionary[i] = alldistances.inertia_ #totalDistance/i;
return dictionary
dictOriginal = kmeansElbow(dataOriginal)
dictRandom = kmeansElbow(dataRandom)
dictStrat = kmeansElbow(dataStrat)
dict = [dictOriginal, dictRandom, dictStrat]
# kmeansData=pd.DataFrame({"clusters":[clusters],"avgDistance":[avgDistance]})
kmeansData = pd.DataFrame(dict)
data = kmeansData.to_json()
return render_template('task.html', taskJS="task1b", data=data)
def task3A():
dataOriginal = sampling.originalData()
dataOriginalY = dataOriginal['A15']
del dataOriginal['A15']
dataOriginal = StandardScaler().fit_transform(dataOriginal)
dataRandom = sampling.randomSampling()
dataRandomY = dataRandom['A15']
del dataRandom['A15']
dataRandom = StandardScaler().fit_transform(dataRandom)
dataStrat = sampling.stratifiedSampling()
dataStratY = dataStrat['A15']
del dataStrat['A15']
dataStrat = StandardScaler().fit_transform(dataStrat)
pca = PCA(n_components=2)
originalPCA = pca.fit_transform(dataOriginal)
randomPCA = pca.fit_transform(dataRandom)
stratPCA = pca.fit_transform(dataStrat)
print(originalPCA[:, :2])
data = [
originalPCA, dataOriginalY, randomPCA, dataRandomY, stratPCA,
dataStratY
]
data = pd.DataFrame(data)
data = data.to_json()
return render_template('task.html', taskJS="task3a", data=data)
def task3c():
dataOriginal = sampling.originalData()
dataOriginalY = dataOriginal['A15']
del dataOriginal['A15']
dataOriginal = StandardScaler().fit_transform(dataOriginal)
dataRandom = sampling.randomSampling()
dataRandomY = dataRandom['A15']
del dataRandom['A15']
dataRandom = StandardScaler().fit_transform(dataRandom)
dataStrat = sampling.stratifiedSampling()
dataStratY = dataStrat['A15']
del dataStrat['A15']
dataStrat = StandardScaler().fit_transform(dataStrat)
pca = PCA(n_components=3)
def return_dict_arr(data, yVal):
array = []
yVal = np.array(yVal)
for i in range(len(data)):
array.append({
"target": yVal[i],
"PCA1": data[i, 0],
"PCA2": data[i, 1],
"PCA3": data[i, 2]
})
return array
originalPCA = pca.fit_transform(dataOriginal)
originalPCA = {"values": return_dict_arr(originalPCA, dataOriginalY)}
randomPCA = pca.fit_transform(dataRandom)
randomPCA = {"values": return_dict_arr(randomPCA, dataRandomY)}
stratPCA = pca.fit_transform(dataStrat)
stratPCA = {"values": return_dict_arr(stratPCA, dataStratY)}
# print(originalPCA[:,:2])
data = [
json.dumps(originalPCA),
json.dumps(randomPCA),
json.dumps(stratPCA)
] #,"randomPCA":dataOriginalY,dataRandomY,stratPCA,dataStratY}
data = pd.DataFrame(data)
data = data.to_json()
#data=json.dumps(data)
return render_template('task3c.html', taskJS="task3c", data=data)
def task3B():
dataOriginal = sampling.originalData()
dataOriginalY = dataOriginal['A15']
del dataOriginal['A15']
dataOriginal = StandardScaler().fit_transform(dataOriginal)
dataRandom = sampling.randomSampling()
dataRandomY = dataRandom['A15']
del dataRandom['A15']
dataRandom = StandardScaler().fit_transform(dataRandom)
dataStrat = sampling.stratifiedSampling()
dataStratY = dataStrat['A15']
del dataStrat['A15']
dataStrat = StandardScaler().fit_transform(dataStrat)
mds_data = manifold.MDS(n_components=2, dissimilarity='precomputed')
similarity = pairwise_distances(dataOriginal, metric='euclidean')
originalMDSEu = mds_data.fit_transform(similarity)
similarity = pairwise_distances(dataRandom, metric='euclidean')
randomMDSEu = mds_data.fit_transform(similarity)
similarity = pairwise_distances(dataStrat, metric='euclidean')
stratMDSEu = mds_data.fit_transform(similarity)
similarity = pairwise_distances(dataOriginal, metric='correlation')
originalMDSCo = mds_data.fit_transform(similarity)
similarity = pairwise_distances(dataRandom, metric='correlation')
randomMDSCo = mds_data.fit_transform(similarity)
similarity = pairwise_distances(dataStrat, metric='correlation')
stratMDSCo = mds_data.fit_transform(similarity)
data=[originalMDSEu,dataOriginalY,randomMDSEu,dataRandomY,stratMDSEu,dataStratY,\
originalMDSCo,dataOriginalY,randomMDSCo,dataRandomY,stratMDSCo,dataStratY]
data = pd.DataFrame(data)
data = data.to_json()
return render_template('task.html', taskJS="task3b", data=data)
def task1A():
dataStrat = sampling.stratifiedSampling().to_json()
dataRandom = sampling.randomSampling().to_json()
data = {"stratifiedSampling": dataStrat, "randomSampling": dataRandom}
return render_template('task.html', taskJS="task1a", data=data)