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#!flask/bin/python
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import sys
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import os
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from flask import Flask, request, Response, jsonify
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from flask_cors import CORS
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from multiprocessing import Pool
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from scipy.spatial import procrustes
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from scipy.spatial import distance
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from sklearn_extra.cluster import KMedoids
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from sklearn import metrics
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from sklearn.decomposition import PCA
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV, train_test_split
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from sklearn.neighbors import KNeighborsClassifier
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from scipy import spatial
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from scipy import stats
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import numpy as np
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import pandas as pd
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import random, json
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import bhtsne
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app = Flask(__name__)
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CORS(app)
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@app.route('/resetAll', methods = ['POST'])
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def Reset():
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print('mpike')
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global dataProc
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dataProc = []
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global D_highSpace
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D_highSpace = []
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global overalProjectionsNumber
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overalProjectionsNumber = []
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global projectionsAll
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projectionsAll = []
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global SelectedListofParams
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SelectedListofParams = []
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global SelectedProjectionsReturn
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SelectedProjectionsReturn = []
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global clusterIndex
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clusterIndex = []
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global convertLabels
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convertLabels = []
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global D_lowSpaceList
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D_lowSpaceList = []
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global KeepKs
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KeepKs = []
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global metricsMatrixEntire
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metricsMatrixEntire = []
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global metricsMatrix
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metricsMatrix = []
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global metricsMatrixSel
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metricsMatrixSel = []
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global metricsMatrixEntireSel
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metricsMatrixEntireSel = []
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return 'Reset'
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# NOTE: Only works with labeled data
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def neighborhood_hit(X, y, k):
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knn = KNeighborsClassifier(n_neighbors=k)
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knn.fit(X, y)
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neighbors = knn.kneighbors(X, return_distance=False)
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y = np.array(y)
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neigh = y[neighbors]
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tile = np.tile(y.reshape((-1, 1)), k)
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equals = (neigh == tile)
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returnthis = np.mean(np.mean(equals).astype('uint8'), axis=1)
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return returnthis
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def trustworthiness(D_high, D_low, k):
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n = D_high.shape[0]
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nn_orig = D_high.argsort()
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nn_proj = D_low.argsort()
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knn_orig = nn_orig[:, :k + 1][:, 1:]
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knn_proj = nn_proj[:, :k + 1][:, 1:]
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sum_i = 0
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for i in range(n):
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U = np.setdiff1d(knn_proj[i], knn_orig[i])
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sum_j = 0
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for j in range(U.shape[0]):
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sum_j += np.where(nn_orig[i] == U[j])[0] - k
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sum_i += sum_j
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return float((1 - (2 / (n * k * (2 * n - 3 * k - 1)) * sum_i)).squeeze())
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def continuity(D_high, D_low, k):
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n = D_high.shape[0]
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nn_orig = D_high.argsort()
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nn_proj = D_low.argsort()
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knn_orig = nn_orig[:, :k + 1][:, 1:]
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knn_proj = nn_proj[:, :k + 1][:, 1:]
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sum_i = 0
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for i in range(n):
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V = np.setdiff1d(knn_proj[i], knn_orig[i])
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sum_j = 0
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for j in range(V.shape[0]):
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sum_j += np.where(nn_proj[i] == V[j])[0] - k
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sum_i += sum_j
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return float((1 - (2 / (n * k * (2 * n - 3 * k - 1)) * sum_i)).squeeze())
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def normalized_stress(D_high, D_low):
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return np.sum((D_high - D_low)**2) / np.sum(D_high**2) / 100
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def shepard_diagram_correlation(D_high, D_low):
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if len(D_high.shape) > 1:
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D_high = spatial.distance.squareform(D_high)
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if len(D_low.shape) > 1:
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D_low = spatial.distance.squareform(D_low)
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return stats.spearmanr(D_high, D_low)[0]
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def preprocess(data):
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dataPandas = pd.DataFrame(data)
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dataPandas.dropna()
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for column in dataPandas:
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if ('*' in column):
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gatherLabels = dataPandas[column]
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del dataPandas[column]
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length = len(dataPandas.columns)
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dataNP = dataPandas.to_numpy()
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return dataNP, length, gatherLabels
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def multi_run_wrapper(args):
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embedding_array = bhtsne.run_bh_tsne(*args)
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return embedding_array
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def procrustesFun(projections):
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similarityList = []
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for proj1 in projections:
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disparityList = []
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for proj2 in projections:
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mtx1, mtx2, disparity = procrustes(proj1, proj2)
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if np.array_equal(proj1, proj2):
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disparityList.append(0)
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else:
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disparityList.append(1/disparity)
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similarityList.append(disparityList)
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clusterIndex = Clustering(similarityList)
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return clusterIndex
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def Clustering(similarity):
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similarityNP = np.array(similarity)
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n_clusters = 36
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kmedoids = KMedoids(n_clusters=n_clusters, random_state=0, metric='precomputed').fit(similarityNP)
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global dataProc
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clusterIndex = []
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for c in range(n_clusters):
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cluster_indices = np.argwhere(kmedoids.labels_ == c).reshape(-1,)
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D_c = similarityNP[cluster_indices][:, cluster_indices]
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center = np.argmin(np.sum(D_c, axis=0))
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clusterIndex.append(cluster_indices[center])
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return clusterIndex
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@app.route('/receiver', methods = ['POST'])
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def calculateGrid():
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data = request.get_data().decode('utf8').replace("'", '"')
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data = json.loads(data)
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global dataProc
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dataProc, length, labels = preprocess(data)
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global D_highSpace
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D_highSpace = distance.squareform(distance.pdist(dataProc))
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DEFAULT_NO_DIMS = 2
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INITIAL_DIMENSIONS = 50
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DEFAULT_PERPLEXITY = 50
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DEFAULT_THETA = 0.5
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EMPTY_SEED = -1
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VERBOSE = True
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DEFAULT_USE_PCA = False
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perplexity = [25,30] # 10 perplexity
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learning_rate = [10,20,30,40,50,60] # 15 learning rate
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n_iter = [200,250,300,350] # 7 iterations
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global overalProjectionsNumber
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overalProjectionsNumber = 0
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overalProjectionsNumber = len(perplexity)*len(learning_rate)*len(n_iter)
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global projectionsAll
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pool = Pool()
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listofParamsPlusData = []
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listofParamsAll= []
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for k in n_iter:
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for j in learning_rate:
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for i in perplexity:
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listofParamsPlusData.append((dataProc,DEFAULT_NO_DIMS,length,i,j,EMPTY_SEED,VERBOSE,DEFAULT_USE_PCA,k))
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listofParamsAll.append((i,j,k))
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projectionsAll = pool.map(multi_run_wrapper, listofParamsPlusData)
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pool.close()
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pool.join()
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global SelectedListofParams
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SelectedListofParams = []
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global SelectedProjectionsReturn
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SelectedProjectionsReturn = []
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global clusterIndex
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clusterIndex = procrustesFun(projectionsAll)
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metricNeigh = []
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metricTrust = []
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metricCont = []
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metricStress = []
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metricShepCorr = []
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global convertLabels
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convertLabels = []
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for index, label in enumerate(labels):
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if (label == 0):
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convertLabels.append(0)
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elif (label == 1):
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convertLabels.append(1)
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elif (label == 'Benign'):
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convertLabels.append(0)
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elif (label == 'Malignant'):
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convertLabels.append(1)
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elif (label == 'Iris-setosa'):
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convertLabels.append(0)
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elif (label == 'Iris-versicolor'):
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convertLabels.append(1)
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elif (label == 'Iris-virginica'):
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convertLabels.append(2)
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else:
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pass
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global D_lowSpaceList
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D_lowSpaceList = []
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global KeepKs
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KeepKs = []
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for index in clusterIndex:
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SelectedProjectionsReturn.append(projectionsAll[index].tolist())
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SelectedListofParams.append(listofParamsAll[index])
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D_lowSpace = distance.squareform(distance.pdist(projectionsAll[index]))
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D_lowSpaceList.append(D_lowSpace)
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k = listofParamsAll[index][0] # k = perplexity
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KeepKs.append(k)
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#resultNeigh = neighborhood_hit(np.array(projectionsAll[index]), convertLabels, k)
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resultNeigh = trustworthiness(D_highSpace, D_lowSpace, k)
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resultTrust = trustworthiness(D_highSpace, D_lowSpace, k)
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resultContinuity = continuity(D_highSpace, D_lowSpace, k)
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resultStress = normalized_stress(D_highSpace, D_lowSpace)
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resultShep = shepard_diagram_correlation(D_highSpace, D_lowSpace)
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metricNeigh.append(resultNeigh)
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metricTrust.append(resultTrust)
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metricCont.append(resultContinuity)
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metricStress.append(resultStress)
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metricShepCorr.append(resultShep)
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max_value_neigh = max(metricNeigh)
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min_value_neigh = min(metricNeigh)
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max_value_trust = max(metricTrust)
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min_value_trust = min(metricTrust)
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max_value_cont = max(metricCont)
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min_value_cont = min(metricCont)
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max_value_stress = max(metricStress)
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min_value_stress = min(metricStress)
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max_value_shep = max(metricShepCorr)
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min_value_shep = min(metricShepCorr)
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global metricsMatrixEntire
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metricsMatrixEntire = []
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for index, data in enumerate(metricTrust):
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#valueNeigh = (metricNeigh[index] - min_value_neigh) / (max_value_neigh - min_value_neigh)
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valueNeigh = (metricTrust[index] - min_value_trust) / (max_value_trust - min_value_trust)
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valueTrust = (metricTrust[index] - min_value_trust) / (max_value_trust - min_value_trust)
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valueCont = (metricCont[index] - min_value_cont) / (max_value_cont - min_value_cont)
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valueStress = (metricStress[index] - min_value_stress) / (max_value_stress - min_value_stress)
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valueShep = (metricShepCorr[index] - min_value_shep) / (max_value_shep - min_value_shep)
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metricsMatrixEntire.append([valueNeigh,valueTrust,valueCont,valueStress,valueShep])
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sortNeigh = sorted(range(len(metricNeigh)), key=lambda k: metricNeigh[k], reverse=True)
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sortTrust = sorted(range(len(metricTrust)), key=lambda k: metricTrust[k], reverse=True)
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sortCont = sorted(range(len(metricCont)), key=lambda k: metricCont[k], reverse=True)
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sortStress = sorted(range(len(metricStress)), key=lambda k: metricStress[k], reverse=True)
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sortShepCorr = sorted(range(len(metricShepCorr)), key=lambda k: metricShepCorr[k], reverse=True)
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global metricsMatrix
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metricsMatrix = []
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metricsMatrix.append(sortNeigh)
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metricsMatrix.append(sortTrust)
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metricsMatrix.append(sortCont)
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metricsMatrix.append(sortStress)
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metricsMatrix.append(sortShepCorr)
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return 'OK'
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@app.route('/sender')
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def background_process():
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global SelectedProjectionsReturn
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global projectionsAll
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global overalProjectionsNumber
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global metricsMatrix
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global metricsMatrixEntire
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while (len(projectionsAll) != overalProjectionsNumber):
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pass
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return jsonify({ 'projections': SelectedProjectionsReturn, 'parameters': SelectedListofParams, 'metrics': metricsMatrix, 'metricsEntire': metricsMatrixEntire })
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@app.route('/receiverOptimizer', methods = ['POST'])
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def OptimizeSelection():
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dataReceived= request.get_data().decode('utf8').replace("'", '"')
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dataReceived = json.loads(dataReceived)
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dataSelected = []
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for data in dataReceived:
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if data != None:
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dataSelected.append(data)
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metricNeigh = []
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metricTrust = []
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metricCont = []
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metricStress = []
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metricShepCorr = []
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for index, loop in enumerate(clusterIndex):
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#resultNeigh = neighborhood_hit(np.array(projectionsAll[index]), convertLabels, k)
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resultNeigh = trustworthiness(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :], KeepKs[index])
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resultTrust = trustworthiness(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :], KeepKs[index])
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resultContinuity = continuity(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :], KeepKs[index])
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resultStress = normalized_stress(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :])
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resultShep = normalized_stress(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :])
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#resultShep = shepard_diagram_correlation(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :])
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|
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metricNeigh.append(resultNeigh)
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|
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metricTrust.append(resultTrust)
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|
|
metricCont.append(resultContinuity)
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|
|
metricStress.append(resultStress)
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|
|
metricShepCorr.append(resultShep)
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|
|
|
|
|
|
|
|
|
max_value_neigh = max(metricNeigh)
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|
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|
|
min_value_neigh = min(metricNeigh)
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|
|
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|
|
max_value_trust = max(metricTrust)
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|
|
min_value_trust = min(metricTrust)
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|
|
max_value_cont = max(metricCont)
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|
|
min_value_cont = min(metricCont)
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|
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|
|
|
|
max_value_stress = max(metricStress)
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|
|
|
|
min_value_stress = min(metricStress)
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|
|
|
|
|
|
|
|
|
max_value_shep = max(metricShepCorr)
|
|
|
|
|
min_value_shep = min(metricShepCorr)
|
|
|
|
|
|
|
|
|
|
global metricsMatrixEntireSel
|
|
|
|
|
metricsMatrixEntireSel = []
|
|
|
|
|
|
|
|
|
|
for index, data in enumerate(metricTrust):
|
|
|
|
|
#valueNeigh = (metricNeigh[index] - min_value_neigh) / (max_value_neigh - min_value_neigh)
|
|
|
|
|
valueNeigh = (metricTrust[index] - min_value_trust) / (max_value_trust - min_value_trust)
|
|
|
|
|
valueTrust = (metricTrust[index] - min_value_trust) / (max_value_trust - min_value_trust)
|
|
|
|
|
valueCont = (metricCont[index] - min_value_cont) / (max_value_cont - min_value_cont)
|
|
|
|
|
valueStress = (metricStress[index] - min_value_stress) / (max_value_stress - min_value_stress)
|
|
|
|
|
valueShep = (metricShepCorr[index] - min_value_shep) / (max_value_shep - min_value_shep)
|
|
|
|
|
metricsMatrixEntireSel.append([valueNeigh,valueTrust,valueCont,valueStress,valueShep])
|
|
|
|
|
|
|
|
|
|
sortNeigh = sorted(range(len(metricNeigh)), key=lambda k: metricNeigh[k], reverse=True)
|
|
|
|
|
sortTrust = sorted(range(len(metricTrust)), key=lambda k: metricTrust[k], reverse=True)
|
|
|
|
|
sortCont = sorted(range(len(metricCont)), key=lambda k: metricCont[k], reverse=True)
|
|
|
|
|
sortStress = sorted(range(len(metricStress)), key=lambda k: metricStress[k], reverse=True)
|
|
|
|
|
sortShepCorr = sorted(range(len(metricShepCorr)), key=lambda k: metricShepCorr[k], reverse=True)
|
|
|
|
|
|
|
|
|
|
global metricsMatrixSel
|
|
|
|
|
metricsMatrixSel = []
|
|
|
|
|
|
|
|
|
|
metricsMatrixSel.append(sortNeigh)
|
|
|
|
|
metricsMatrixSel.append(sortTrust)
|
|
|
|
|
metricsMatrixSel.append(sortCont)
|
|
|
|
|
metricsMatrixSel.append(sortStress)
|
|
|
|
|
metricsMatrixSel.append(sortShepCorr)
|
|
|
|
|
|
|
|
|
|
return 'OK'
|
|
|
|
|
|
|
|
|
|
@app.route('/senderOptimizer')
|
|
|
|
|
def SendOptimizedProjections():
|
|
|
|
|
global metricsMatrixSel
|
|
|
|
|
global metricsMatrixEntireSel
|
|
|
|
|
|
|
|
|
|
return jsonify({'metrics': metricsMatrixSel, 'metricsEntire': metricsMatrixEntireSel })
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
app.run("0.0.0.0", "5000")
|
|
|
|
|
|
