FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
https://doi.org/10.1109/TVCG.2022.3141040
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1929 lines
78 KiB
1929 lines
78 KiB
from flask import Flask, render_template, jsonify, request
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from flask_pymongo import PyMongo
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from flask_cors import CORS, cross_origin
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import json
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import copy
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import warnings
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import re
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import random
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import math
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import pandas as pd
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import numpy as np
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import multiprocessing
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from joblib import Memory
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from xgboost import XGBClassifier
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from sklearn import model_selection
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from bayes_opt import BayesianOptimization
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from sklearn.model_selection import cross_validate
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from sklearn.model_selection import cross_val_predict
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.metrics import classification_report
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from sklearn.feature_selection import mutual_info_classif
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from sklearn.feature_selection import SelectKBest
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from sklearn.feature_selection import f_classif
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from sklearn.feature_selection import RFECV
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.linear_model import LogisticRegression
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import eli5
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from eli5.sklearn import PermutationImportance
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from joblib import Parallel, delayed
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import multiprocessing
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from statsmodels.stats.outliers_influence import variance_inflation_factor
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from statsmodels.tools.tools import add_constant
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# this block of code is for the connection between the server, the database, and the client (plus routing)
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# access MongoDB
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app = Flask(__name__)
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app.config["MONGO_URI"] = "mongodb://localhost:27017/mydb"
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mongo = PyMongo(app)
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cors = CORS(app, resources={r"/data/*": {"origins": "*"}})
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@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
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@app.route('/data/Reset', methods=["GET", "POST"])
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def reset():
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global DataRawLength
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global DataResultsRaw
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global previousState
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previousState = []\
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global filterActionFinal
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filterActionFinal = ''
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global keySpecInternal
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keySpecInternal = 1
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global RANDOM_SEED
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RANDOM_SEED = 42
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global keyData
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keyData = 0
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global keepOriginalFeatures
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keepOriginalFeatures = []
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global XData
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XData = []
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global yData
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yData = []
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global XDataNoRemoval
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XDataNoRemoval = []
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global XDataNoRemovalOrig
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XDataNoRemovalOrig = []
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global XDataStored
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XDataStored = []
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global yDataStored
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yDataStored = []
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global finalResultsData
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finalResultsData = []
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global detailsParams
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detailsParams = []
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global algorithmList
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algorithmList = []
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global ClassifierIDsList
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ClassifierIDsList = ''
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global RetrieveModelsList
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RetrieveModelsList = []
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global allParametersPerfCrossMutr
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allParametersPerfCrossMutr = []
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global all_classifiers
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all_classifiers = []
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global crossValidation
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crossValidation = 5
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global resultsMetrics
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resultsMetrics = []
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global parametersSelData
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parametersSelData = []
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global target_names
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target_names = []
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global keyFirstTime
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keyFirstTime = True
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global target_namesLoc
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target_namesLoc = []
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global featureCompareData
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featureCompareData = []
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global columnsKeep
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columnsKeep = []
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global columnsNewGen
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columnsNewGen = []
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global columnsNames
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columnsNames = []
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global fileName
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fileName = []
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global listofTransformations
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listofTransformations = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
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return 'The reset was done!'
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# retrieve data from client and select the correct data set
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@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
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@app.route('/data/ServerRequest', methods=["GET", "POST"])
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def retrieveFileName():
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global DataRawLength
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global DataResultsRaw
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global DataResultsRawTest
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global DataRawLengthTest
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global fileName
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fileName = []
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fileName = request.get_data().decode('utf8').replace("'", '"')
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global keySpecInternal
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keySpecInternal = 1
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global filterActionFinal
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filterActionFinal = ''
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global dataSpacePointsIDs
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dataSpacePointsIDs = []
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global RANDOM_SEED
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RANDOM_SEED = 42
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global keyData
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keyData = 0
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global keepOriginalFeatures
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keepOriginalFeatures = []
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global XData
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XData = []
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global XDataNoRemoval
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XDataNoRemoval = []
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global XDataNoRemovalOrig
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XDataNoRemovalOrig = []
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global previousState
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previousState = []
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global yData
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yData = []
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global XDataStored
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XDataStored = []
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global yDataStored
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yDataStored = []
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global finalResultsData
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finalResultsData = []
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global ClassifierIDsList
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ClassifierIDsList = ''
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global algorithmList
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algorithmList = []
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global detailsParams
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detailsParams = []
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# Initializing models
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global RetrieveModelsList
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RetrieveModelsList = []
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global resultsList
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resultsList = []
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global allParametersPerfCrossMutr
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allParametersPerfCrossMutr = []
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global HistoryPreservation
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HistoryPreservation = []
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global all_classifiers
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all_classifiers = []
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global crossValidation
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crossValidation = 5
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global parametersSelData
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parametersSelData = []
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global StanceTest
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StanceTest = False
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global target_names
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target_names = []
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global keyFirstTime
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keyFirstTime = True
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global target_namesLoc
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target_namesLoc = []
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global featureCompareData
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featureCompareData = []
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global columnsKeep
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columnsKeep = []
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global columnsNewGen
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columnsNewGen = []
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global columnsNames
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columnsNames = []
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global listofTransformations
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listofTransformations = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
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DataRawLength = -1
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DataRawLengthTest = -1
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data = json.loads(fileName)
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if data['fileName'] == 'HeartC':
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CollectionDB = mongo.db.HeartC.find()
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target_names.append('Healthy')
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target_names.append('Diseased')
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elif data['fileName'] == 'BiodegC':
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StanceTest = True
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CollectionDB = mongo.db.biodegC.find()
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CollectionDBTest = mongo.db.biodegCTest.find()
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CollectionDBExternal = mongo.db.biodegCExt.find()
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target_names.append('Non-biodegradable')
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target_names.append('Biodegradable')
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elif data['fileName'] == 'BreastC':
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CollectionDB = mongo.db.breastC.find()
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elif data['fileName'] == 'DiabetesC':
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CollectionDB = mongo.db.diabetesC.find()
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target_names.append('Negative')
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target_names.append('Positive')
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elif data['fileName'] == 'ContraceptiveC':
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CollectionDB = mongo.db.ContraceptiveC.find()
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target_names.append('No-use')
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target_names.append('Long-term')
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target_names.append('Short-term')
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elif data['fileName'] == 'VehicleC':
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CollectionDB = mongo.db.VehicleC.find()
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target_names.append('Van')
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target_names.append('Saab')
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target_names.append('Bus')
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target_names.append('Opel')
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elif data['fileName'] == 'WineC':
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CollectionDB = mongo.db.WineC.find()
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target_names.append('Fine')
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target_names.append('Superior')
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target_names.append('Inferior')
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else:
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CollectionDB = mongo.db.IrisC.find()
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DataResultsRaw = []
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for index, item in enumerate(CollectionDB):
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item['_id'] = str(item['_id'])
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item['InstanceID'] = index
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DataResultsRaw.append(item)
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DataRawLength = len(DataResultsRaw)
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DataResultsRawTest = []
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if (StanceTest):
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for index, item in enumerate(CollectionDBTest):
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item['_id'] = str(item['_id'])
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item['InstanceID'] = index
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DataResultsRawTest.append(item)
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DataRawLengthTest = len(DataResultsRawTest)
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dataSetSelection()
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return 'Everything is okay'
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# Retrieve data set from client
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@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
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@app.route('/data/SendtoSeverDataSet', methods=["GET", "POST"])
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def sendToServerData():
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uploadedData = request.get_data().decode('utf8').replace("'", '"')
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uploadedDataParsed = json.loads(uploadedData)
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DataResultsRaw = uploadedDataParsed['uploadedData']
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DataResults = copy.deepcopy(DataResultsRaw)
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for dictionary in DataResultsRaw:
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for key in dictionary.keys():
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if (key.find('*') != -1):
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target = key
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continue
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continue
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DataResultsRaw.sort(key=lambda x: x[target], reverse=True)
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DataResults.sort(key=lambda x: x[target], reverse=True)
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for dictionary in DataResults:
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del dictionary[target]
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global AllTargets
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global target_names
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global target_namesLoc
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AllTargets = [o[target] for o in DataResultsRaw]
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AllTargetsFloatValues = []
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global fileName
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data = json.loads(fileName)
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previous = None
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Class = 0
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for i, value in enumerate(AllTargets):
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if (i == 0):
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previous = value
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if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
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target_names.append(value)
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else:
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pass
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if (value == previous):
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AllTargetsFloatValues.append(Class)
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else:
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Class = Class + 1
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if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
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target_names.append(value)
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else:
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pass
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AllTargetsFloatValues.append(Class)
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previous = value
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ArrayDataResults = pd.DataFrame.from_dict(DataResults)
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global XData, yData, RANDOM_SEED
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XData, yData = ArrayDataResults, AllTargetsFloatValues
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global XDataStored, yDataStored
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XDataStored = XData.copy()
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yDataStored = yData.copy()
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global XDataStoredOriginal
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XDataStoredOriginal = XData.copy()
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global finalResultsData
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finalResultsData = XData.copy()
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global XDataNoRemoval
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XDataNoRemoval = XData.copy()
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global XDataNoRemovalOrig
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XDataNoRemovalOrig = XData.copy()
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return 'Processed uploaded data set'
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def dataSetSelection():
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global XDataTest, yDataTest
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XDataTest = pd.DataFrame()
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global StanceTest
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global AllTargets
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global target_names
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target_namesLoc = []
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if (StanceTest):
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DataResultsTest = copy.deepcopy(DataResultsRawTest)
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for dictionary in DataResultsRawTest:
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for key in dictionary.keys():
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if (key.find('*') != -1):
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target = key
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continue
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continue
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DataResultsRawTest.sort(key=lambda x: x[target], reverse=True)
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DataResultsTest.sort(key=lambda x: x[target], reverse=True)
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for dictionary in DataResultsTest:
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del dictionary['_id']
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del dictionary['InstanceID']
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del dictionary[target]
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AllTargetsTest = [o[target] for o in DataResultsRawTest]
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AllTargetsFloatValuesTest = []
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previous = None
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Class = 0
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for i, value in enumerate(AllTargetsTest):
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if (i == 0):
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previous = value
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target_namesLoc.append(value)
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if (value == previous):
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AllTargetsFloatValuesTest.append(Class)
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else:
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Class = Class + 1
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target_namesLoc.append(value)
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AllTargetsFloatValuesTest.append(Class)
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previous = value
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ArrayDataResultsTest = pd.DataFrame.from_dict(DataResultsTest)
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XDataTest, yDataTest = ArrayDataResultsTest, AllTargetsFloatValuesTest
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DataResults = copy.deepcopy(DataResultsRaw)
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for dictionary in DataResultsRaw:
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for key in dictionary.keys():
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if (key.find('*') != -1):
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target = key
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continue
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continue
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DataResultsRaw.sort(key=lambda x: x[target], reverse=True)
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DataResults.sort(key=lambda x: x[target], reverse=True)
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for dictionary in DataResults:
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del dictionary['_id']
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del dictionary['InstanceID']
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del dictionary[target]
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AllTargets = [o[target] for o in DataResultsRaw]
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AllTargetsFloatValues = []
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global fileName
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data = json.loads(fileName)
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previous = None
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Class = 0
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for i, value in enumerate(AllTargets):
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if (i == 0):
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previous = value
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if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
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target_names.append(value)
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else:
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pass
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if (value == previous):
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AllTargetsFloatValues.append(Class)
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else:
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Class = Class + 1
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if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
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target_names.append(value)
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else:
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pass
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AllTargetsFloatValues.append(Class)
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previous = value
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ArrayDataResults = pd.DataFrame.from_dict(DataResults)
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global XData, yData, RANDOM_SEED
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XData, yData = ArrayDataResults, AllTargetsFloatValues
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global keepOriginalFeatures
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global OrignList
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keepOriginalFeatures = XData.copy()
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keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(keepOriginalFeatures.columns)]
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columnsNewGen = keepOriginalFeatures.columns.values.tolist()
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OrignList = keepOriginalFeatures.columns.values.tolist()
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XData.columns = ['F'+str(idx+1) for idx, col in enumerate(XData.columns)]
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global XDataStored, yDataStored
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XDataStored = XData.copy()
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yDataStored = yData.copy()
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global XDataStoredOriginal
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XDataStoredOriginal = XData.copy()
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global finalResultsData
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finalResultsData = XData.copy()
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global XDataNoRemoval
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XDataNoRemoval = XData.copy()
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global XDataNoRemovalOrig
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XDataNoRemovalOrig = XData.copy()
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warnings.simplefilter('ignore')
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executeModel([], 0, '')
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return 'Everything is okay'
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def create_global_function():
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global estimator
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def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
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# initialize model
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n_estimators = int(n_estimators)
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max_depth = int(max_depth)
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model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, subsample=subsample, colsample_bytree=colsample_bytree, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
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# set in cross-validation
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result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
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# result is mean of test_score
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return np.mean(result['test_score'])
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# check this issue later because we are not getting the same results
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def executeModel(exeCall, flagEx, nodeTransfName):
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global keyFirstTime
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global estimator
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global yPredictProb
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global scores
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global featureImportanceData
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global XData
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global XDataStored
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global previousState
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global columnsNewGen
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global columnsNames
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global listofTransformations
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global XDataStoredOriginal
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global finalResultsData
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global OrignList
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global tracker
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global XDataNoRemoval
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global XDataNoRemovalOrig
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columnsNames = []
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scores = []
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if (len(exeCall) == 0):
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if (flagEx == 3):
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XDataStored = XData.copy()
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XDataNoRemovalOrig = XDataNoRemoval.copy()
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OrignList = columnsNewGen
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elif (flagEx == 2):
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XData = XDataStored.copy()
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XDataStoredOriginal = XDataStored.copy()
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XDataNoRemoval = XDataNoRemovalOrig.copy()
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columnsNewGen = OrignList
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else:
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XData = XDataStored.copy()
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XDataNoRemoval = XDataNoRemovalOrig.copy()
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XDataStoredOriginal = XDataStored.copy()
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else:
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if (flagEx == 4):
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XDataStored = XData.copy()
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XDataNoRemovalOrig = XDataNoRemoval.copy()
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#XDataStoredOriginal = XDataStored.copy()
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elif (flagEx == 2):
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XData = XDataStored.copy()
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XDataStoredOriginal = XDataStored.copy()
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XDataNoRemoval = XDataNoRemovalOrig.copy()
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columnsNewGen = OrignList
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else:
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XData = XDataStored.copy()
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#XDataNoRemoval = XDataNoRemovalOrig.copy()
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XDataStoredOriginal = XDataStored.copy()
|
|
|
|
# Bayesian Optimization CHANGE INIT_POINTS!
|
|
if (keyFirstTime):
|
|
create_global_function()
|
|
params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "max_depth": (6,12), "subsample": (0.8,1), "colsample_bytree": (0.8,1)}
|
|
bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
|
|
bayesopt.maximize(init_points=35, n_iter=15, acq='ucb') # 35 and 15
|
|
bestParams = bayesopt.max['params']
|
|
estimator = XGBClassifier(n_estimators=int(bestParams.get('n_estimators')), eta=bestParams.get('eta'), max_depth=int(bestParams.get('max_depth')), subsample=bestParams.get('subsample'), colsample_bytree=bestParams.get('colsample_bytree'), probability=True, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
|
|
columnsNewGen = OrignList
|
|
|
|
if (len(exeCall) != 0):
|
|
if (flagEx == 1):
|
|
currentColumnsDeleted = []
|
|
for uniqueValue in exeCall:
|
|
currentColumnsDeleted.append(tracker[uniqueValue])
|
|
for column in XData.columns:
|
|
if (column in currentColumnsDeleted):
|
|
XData = XData.drop(column, axis=1)
|
|
XDataStoredOriginal = XDataStoredOriginal.drop(column, axis=1)
|
|
elif (flagEx == 2):
|
|
columnsKeepNew = []
|
|
columns = XDataGen.columns.values.tolist()
|
|
for indx, col in enumerate(columns):
|
|
if indx in exeCall:
|
|
columnsKeepNew.append(col)
|
|
columnsNewGen.append(col)
|
|
|
|
XDataTemp = XDataGen[columnsKeepNew]
|
|
XData[columnsKeepNew] = XDataTemp.values
|
|
XDataStoredOriginal[columnsKeepNew] = XDataTemp.values
|
|
XDataNoRemoval[columnsKeepNew] = XDataTemp.values
|
|
elif (flagEx == 4):
|
|
splittedCol = nodeTransfName.split('_')
|
|
for col in XDataNoRemoval.columns:
|
|
if ((splittedCol[0] in col)):
|
|
storeRenamedColumn = col
|
|
XData.rename(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
|
|
XDataNoRemoval.rename(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
|
|
currentColumn = columnsNewGen[exeCall[0]]
|
|
subString = currentColumn[currentColumn.find("(")+1:currentColumn.find(")")]
|
|
replacement = currentColumn.replace(subString, nodeTransfName)
|
|
for ind, column in enumerate(columnsNewGen):
|
|
if ((splittedCol[0] in column)):
|
|
columnsNewGen[ind] = columnsNewGen[ind].replace(storeRenamedColumn, nodeTransfName)
|
|
if (len(splittedCol) == 1):
|
|
XData[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
|
|
XDataNoRemoval[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
|
|
else:
|
|
if (splittedCol[1] == 'r'):
|
|
XData[nodeTransfName] = XData[nodeTransfName].round()
|
|
elif (splittedCol[1] == 'b'):
|
|
number_of_bins = np.histogram_bin_edges(XData[nodeTransfName], bins='auto')
|
|
emptyLabels = []
|
|
for index, number in enumerate(number_of_bins):
|
|
if (index == 0):
|
|
pass
|
|
else:
|
|
emptyLabels.append(index)
|
|
XData[nodeTransfName] = pd.cut(XData[nodeTransfName], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
|
|
XData[nodeTransfName] = pd.to_numeric(XData[nodeTransfName], downcast='signed')
|
|
elif (splittedCol[1] == 'zs'):
|
|
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].mean())/XData[nodeTransfName].std()
|
|
elif (splittedCol[1] == 'mms'):
|
|
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].min())/(XData[nodeTransfName].max()-XData[nodeTransfName].min())
|
|
elif (splittedCol[1] == 'l2'):
|
|
dfTemp = []
|
|
dfTemp = np.log2(XData[nodeTransfName])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XData[nodeTransfName] = dfTemp
|
|
elif (splittedCol[1] == 'l1p'):
|
|
XData[nodeTransfName] = np.log1p(XData[nodeTransfName])
|
|
elif (splittedCol[1] == 'l10'):
|
|
dfTemp = []
|
|
dfTemp = np.log10(XData[nodeTransfName])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XData[nodeTransfName] = dfTemp
|
|
elif (splittedCol[1] == 'e2'):
|
|
dfTemp = []
|
|
dfTemp = np.exp2(XData[nodeTransfName])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XData[nodeTransfName] = dfTemp
|
|
elif (splittedCol[1] == 'em1'):
|
|
dfTemp = []
|
|
dfTemp = np.expm1(XData[nodeTransfName])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XData[nodeTransfName] = dfTemp
|
|
elif (splittedCol[1] == 'p2'):
|
|
XData[nodeTransfName] = np.power(XData[nodeTransfName], 2)
|
|
elif (splittedCol[1] == 'p3'):
|
|
XData[nodeTransfName] = np.power(XData[nodeTransfName], 3)
|
|
else:
|
|
XData[nodeTransfName] = np.power(XData[nodeTransfName], 4)
|
|
XDataNoRemoval[nodeTransfName] = XData[nodeTransfName]
|
|
XDataStored = XData.copy()
|
|
XDataNoRemovalOrig = XDataNoRemoval.copy()
|
|
|
|
columnsNamesLoc = XData.columns.values.tolist()
|
|
|
|
for col in columnsNamesLoc:
|
|
splittedCol = col.split('_')
|
|
if (len(splittedCol) == 1):
|
|
for tran in listofTransformations:
|
|
columnsNames.append(splittedCol[0]+'_'+tran)
|
|
else:
|
|
for tran in listofTransformations:
|
|
if (splittedCol[1] == tran):
|
|
columnsNames.append(splittedCol[0])
|
|
else:
|
|
columnsNames.append(splittedCol[0]+'_'+tran)
|
|
|
|
print(XData)
|
|
|
|
featureImportanceData = estimatorFeatureSelection(XDataNoRemoval, estimator)
|
|
|
|
tracker = []
|
|
for value in columnsNewGen:
|
|
value = value.split(' ')
|
|
if (len(value) > 1):
|
|
tracker.append(value[1])
|
|
else:
|
|
tracker.append(value[0])
|
|
|
|
estimator.fit(XData, yData)
|
|
yPredict = estimator.predict(XData)
|
|
yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
|
|
|
|
num_cores = multiprocessing.cpu_count()
|
|
inputsSc = ['accuracy','precision_macro','recall_macro']
|
|
|
|
flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
|
|
scoresAct = [item for sublist in flat_results for item in sublist]
|
|
|
|
howMany = 0
|
|
|
|
if (keyFirstTime):
|
|
previousState = scoresAct
|
|
keyFirstTime = False
|
|
howMany = 3
|
|
|
|
if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
|
|
finalResultsData = XData.copy()
|
|
print('Improved!')
|
|
|
|
if (keyFirstTime == False):
|
|
if ((scoresAct[0]-scoresAct[1]) > (previousState[0]-previousState[1])):
|
|
previousState[0] = scoresAct[0]
|
|
previousState[1] = scoresAct[1]
|
|
howMany = howMany + 1
|
|
elif ((scoresAct[2]-scoresAct[3]) > (previousState[2]-previousState[3])):
|
|
previousState[2] = scoresAct[2]
|
|
previousState[3] = scoresAct[3]
|
|
howMany = howMany + 1
|
|
elif ((scoresAct[4]-scoresAct[5]) > (previousState[4]-previousState[5])):
|
|
previousState[4] = scoresAct[4]
|
|
previousState[5] = scoresAct[5]
|
|
howMany = howMany + 1
|
|
else:
|
|
pass
|
|
|
|
scores = scoresAct + previousState
|
|
|
|
if (howMany == 3):
|
|
scores.append(1)
|
|
else:
|
|
scores.append(0)
|
|
|
|
return 'Everything Okay'
|
|
|
|
@app.route('/data/RequestBestFeatures', methods=["GET", "POST"])
|
|
def BestFeat():
|
|
global finalResultsData
|
|
finalResultsDataJSON = finalResultsData.to_json()
|
|
|
|
response = {
|
|
'finalResultsData': finalResultsDataJSON
|
|
}
|
|
return jsonify(response)
|
|
|
|
def featFun (clfLocalPar,DataLocalPar,yDataLocalPar):
|
|
PerFeatureAccuracyLocalPar = []
|
|
scores = model_selection.cross_val_score(clfLocalPar, DataLocalPar, yDataLocalPar, cv=None, n_jobs=-1)
|
|
PerFeatureAccuracyLocalPar.append(scores.mean())
|
|
return PerFeatureAccuracyLocalPar
|
|
|
|
|
|
# location = './cachedir'
|
|
# memory = Memory(location, verbose=0)
|
|
|
|
# # calculating for all algorithms and models the performance and other results
|
|
# @memory.cache
|
|
def estimatorFeatureSelection(Data, clf):
|
|
|
|
resultsFS = []
|
|
permList = []
|
|
PerFeatureAccuracy = []
|
|
PerFeatureAccuracyAll = []
|
|
ImpurityFS = []
|
|
RankingFS = []
|
|
|
|
estim = clf.fit(Data, yData)
|
|
|
|
importances = clf.feature_importances_
|
|
# std = np.std([tree.feature_importances_ for tree in estim.feature_importances_],
|
|
# axis=0)
|
|
|
|
maxList = max(importances)
|
|
minList = min(importances)
|
|
|
|
for f in range(Data.shape[1]):
|
|
ImpurityFS.append((importances[f] - minList) / (maxList - minList))
|
|
|
|
estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED)
|
|
|
|
selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation)
|
|
selector = selector.fit(Data, yData)
|
|
RFEImp = selector.ranking_
|
|
print(RFEImp)
|
|
for f in range(Data.shape[1]):
|
|
if (RFEImp[f] == 1):
|
|
RankingFS.append(0.95)
|
|
elif (RFEImp[f] == 2):
|
|
RankingFS.append(0.85)
|
|
elif (RFEImp[f] == 3):
|
|
RankingFS.append(0.75)
|
|
elif (RFEImp[f] == 4):
|
|
RankingFS.append(0.65)
|
|
elif (RFEImp[f] == 5):
|
|
RankingFS.append(0.55)
|
|
elif (RFEImp[f] == 6):
|
|
RankingFS.append(0.45)
|
|
elif (RFEImp[f] == 7):
|
|
RankingFS.append(0.35)
|
|
elif (RFEImp[f] == 8):
|
|
RankingFS.append(0.25)
|
|
elif (RFEImp[f] == 9):
|
|
RankingFS.append(0.15)
|
|
else:
|
|
RankingFS.append(0.05)
|
|
|
|
perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData)
|
|
permList.append(perm.feature_importances_)
|
|
n_feats = Data.shape[1]
|
|
|
|
num_cores = multiprocessing.cpu_count()
|
|
print("Parallelization Initialized")
|
|
flat_results = Parallel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats))
|
|
PerFeatureAccuracy = [item for sublist in flat_results for item in sublist]
|
|
# for i in range(n_feats):
|
|
# scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1)
|
|
# PerFeatureAccuracy.append(scoresHere.mean())
|
|
PerFeatureAccuracyAll.append(PerFeatureAccuracy)
|
|
|
|
clf.fit(Data, yData)
|
|
yPredict = clf.predict(Data)
|
|
yPredict = np.nan_to_num(yPredict)
|
|
|
|
RankingFSDF = pd.DataFrame(RankingFS)
|
|
RankingFSDF = RankingFSDF.to_json()
|
|
|
|
ImpurityFSDF = pd.DataFrame(ImpurityFS)
|
|
ImpurityFSDF = ImpurityFSDF.to_json()
|
|
|
|
perm_imp_eli5PD = pd.DataFrame(permList)
|
|
perm_imp_eli5PD = perm_imp_eli5PD.to_json()
|
|
|
|
PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracyAll)
|
|
PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
|
|
|
|
bestfeatures = SelectKBest(score_func=f_classif, k='all')
|
|
fit = bestfeatures.fit(Data,yData)
|
|
dfscores = pd.DataFrame(fit.scores_)
|
|
dfcolumns = pd.DataFrame(Data.columns)
|
|
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
|
|
featureScores.columns = ['Specs','Score'] #naming the dataframe columns
|
|
featureScores = featureScores.to_json()
|
|
|
|
resultsFS.append(featureScores)
|
|
resultsFS.append(ImpurityFSDF)
|
|
resultsFS.append(perm_imp_eli5PD)
|
|
resultsFS.append(PerFeatureAccuracyPandas)
|
|
resultsFS.append(RankingFSDF)
|
|
|
|
return resultsFS
|
|
|
|
@app.route('/data/sendFeatImp', methods=["GET", "POST"])
|
|
def sendFeatureImportance():
|
|
global featureImportanceData
|
|
|
|
response = {
|
|
'Importance': featureImportanceData
|
|
}
|
|
return jsonify(response)
|
|
|
|
@app.route('/data/sendFeatImpComp', methods=["GET", "POST"])
|
|
def sendFeatureImportanceComp():
|
|
global featureCompareData
|
|
global columnsKeep
|
|
|
|
response = {
|
|
'ImportanceCompare': featureCompareData,
|
|
'FeatureNames': columnsKeep
|
|
}
|
|
return jsonify(response)
|
|
|
|
def solve(sclf,XData,yData,crossValidation,scoringIn,loop):
|
|
scoresLoc = []
|
|
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
|
|
|
|
scoresLoc.append(temp.mean())
|
|
scoresLoc.append(temp.std())
|
|
|
|
return scoresLoc
|
|
|
|
@app.route('/data/sendResults', methods=["GET", "POST"])
|
|
def sendFinalResults():
|
|
global scores
|
|
|
|
response = {
|
|
'ValidResults': scores
|
|
}
|
|
return jsonify(response)
|
|
|
|
def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
|
|
|
|
# XDataNumericColumn = XData.select_dtypes(include='number')
|
|
XDataNumeric = XDataStoredOriginal.select_dtypes(include='number')
|
|
|
|
columns = list(XDataNumeric)
|
|
|
|
global packCorrTransformed
|
|
packCorrTransformed = []
|
|
|
|
for count, i in enumerate(columns):
|
|
dicTransf = {}
|
|
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+0].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
XDataNumericCopy[i] = XDataNumericCopy[i].round()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+1].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
number_of_bins = np.histogram_bin_edges(XDataNumericCopy[i], bins='auto')
|
|
emptyLabels = []
|
|
for index, number in enumerate(number_of_bins):
|
|
if (index == 0):
|
|
pass
|
|
else:
|
|
emptyLabels.append(index)
|
|
XDataNumericCopy[i] = pd.cut(XDataNumericCopy[i], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
|
|
XDataNumericCopy[i] = pd.to_numeric(XDataNumericCopy[i], downcast='signed')
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+2].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].mean())/XDataNumericCopy[i].std()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+3].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].min())/(XDataNumericCopy[i].max()-XDataNumericCopy[i].min())
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+4].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
dfTemp = []
|
|
dfTemp = np.log2(XDataNumericCopy[i])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XDataNumericCopy[i] = dfTemp
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+5].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
XDataNumericCopy[i] = np.log1p(XDataNumericCopy[i])
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+6].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
dfTemp = []
|
|
dfTemp = np.log10(XDataNumericCopy[i])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XDataNumericCopy[i] = dfTemp
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+7].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
dfTemp = []
|
|
dfTemp = np.exp2(XDataNumericCopy[i])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XDataNumericCopy[i] = dfTemp
|
|
if (np.isinf(dfTemp.var())):
|
|
flagInf = True
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+8].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
dfTemp = []
|
|
dfTemp = np.expm1(XDataNumericCopy[i])
|
|
dfTemp = dfTemp.replace(np.nan, 0)
|
|
dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
|
|
dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
|
|
XDataNumericCopy[i] = dfTemp
|
|
if (np.isinf(dfTemp.var())):
|
|
flagInf = True
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+9].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 2)
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+10].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 3)
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
splittedCol = columnsNames[(count)*len(listofTransformations)+11].split('_')
|
|
if(len(splittedCol) == 1):
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
else:
|
|
d={}
|
|
flagInf = False
|
|
XDataNumericCopy = XDataNumeric.copy()
|
|
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 4)
|
|
for number in range(1,6):
|
|
quadrantVariable = str('quadrant%s' % number)
|
|
illusion = locals()[quadrantVariable]
|
|
d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
|
|
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
|
|
packCorrTransformed.append(dicTransf)
|
|
|
|
return 'Everything Okay'
|
|
|
|
def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count, flagInf):
|
|
|
|
corrMatrix1 = DataRows1.corr()
|
|
corrMatrix1 = corrMatrix1.abs()
|
|
corrMatrix2 = DataRows2.corr()
|
|
corrMatrix2 = corrMatrix2.abs()
|
|
corrMatrix3 = DataRows3.corr()
|
|
corrMatrix3 = corrMatrix3.abs()
|
|
corrMatrix4 = DataRows4.corr()
|
|
corrMatrix4 = corrMatrix4.abs()
|
|
corrMatrix5 = DataRows5.corr()
|
|
corrMatrix5 = corrMatrix5.abs()
|
|
corrMatrix1 = corrMatrix1.loc[[feature]]
|
|
corrMatrix2 = corrMatrix2.loc[[feature]]
|
|
corrMatrix3 = corrMatrix3.loc[[feature]]
|
|
corrMatrix4 = corrMatrix4.loc[[feature]]
|
|
corrMatrix5 = corrMatrix5.loc[[feature]]
|
|
|
|
DataRows1 = DataRows1.reset_index(drop=True)
|
|
DataRows2 = DataRows2.reset_index(drop=True)
|
|
DataRows3 = DataRows3.reset_index(drop=True)
|
|
DataRows4 = DataRows4.reset_index(drop=True)
|
|
DataRows5 = DataRows5.reset_index(drop=True)
|
|
|
|
targetRows1 = [yData[i] for i in quadrant1]
|
|
targetRows2 = [yData[i] for i in quadrant2]
|
|
targetRows3 = [yData[i] for i in quadrant3]
|
|
targetRows4 = [yData[i] for i in quadrant4]
|
|
targetRows5 = [yData[i] for i in quadrant5]
|
|
|
|
targetRows1Arr = np.array(targetRows1)
|
|
targetRows2Arr = np.array(targetRows2)
|
|
targetRows3Arr = np.array(targetRows3)
|
|
targetRows4Arr = np.array(targetRows4)
|
|
targetRows5Arr = np.array(targetRows5)
|
|
|
|
uniqueTarget1 = unique(targetRows1)
|
|
uniqueTarget2 = unique(targetRows2)
|
|
uniqueTarget3 = unique(targetRows3)
|
|
uniqueTarget4 = unique(targetRows4)
|
|
uniqueTarget5 = unique(targetRows5)
|
|
|
|
if (len(targetRows1Arr) > 0):
|
|
onehotEncoder1 = OneHotEncoder(sparse=False)
|
|
targetRows1Arr = targetRows1Arr.reshape(len(targetRows1Arr), 1)
|
|
onehotEncoder1 = onehotEncoder1.fit_transform(targetRows1Arr)
|
|
hotEncoderDF1 = pd.DataFrame(onehotEncoder1)
|
|
concatDF1 = pd.concat([DataRows1, hotEncoderDF1], axis=1)
|
|
corrMatrixComb1 = concatDF1.corr()
|
|
corrMatrixComb1 = corrMatrixComb1.abs()
|
|
corrMatrixComb1 = corrMatrixComb1.iloc[:,-len(uniqueTarget1):]
|
|
X1 = add_constant(DataRows1.dropna())
|
|
VIF1 = pd.Series([variance_inflation_factor(X1.values, i)
|
|
for i in range(X1.shape[1])],
|
|
index=X1.columns)
|
|
VIF1 = VIF1.replace(np.nan, 0)
|
|
VIF1 = VIF1.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF1 = VIF1.replace(np.inf, 1.7976931348623157e+308)
|
|
VIF1 = VIF1.loc[[feature]]
|
|
if ((len(targetRows1Arr) > 2) and (flagInf == False)):
|
|
MI1 = mutual_info_classif(DataRows1, targetRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI1List = MI1.tolist()
|
|
MI1List = MI1List[count]
|
|
else:
|
|
MI1List = []
|
|
else:
|
|
corrMatrixComb1 = pd.DataFrame()
|
|
VIF1 = pd.Series()
|
|
MI1List = []
|
|
|
|
if (len(targetRows2Arr) > 0):
|
|
onehotEncoder2 = OneHotEncoder(sparse=False)
|
|
targetRows2Arr = targetRows2Arr.reshape(len(targetRows2Arr), 1)
|
|
onehotEncoder2 = onehotEncoder2.fit_transform(targetRows2Arr)
|
|
hotEncoderDF2 = pd.DataFrame(onehotEncoder2)
|
|
concatDF2 = pd.concat([DataRows2, hotEncoderDF2], axis=1)
|
|
corrMatrixComb2 = concatDF2.corr()
|
|
corrMatrixComb2 = corrMatrixComb2.abs()
|
|
corrMatrixComb2 = corrMatrixComb2.iloc[:,-len(uniqueTarget2):]
|
|
X2 = add_constant(DataRows2.dropna())
|
|
VIF2 = pd.Series([variance_inflation_factor(X2.values, i)
|
|
for i in range(X2.shape[1])],
|
|
index=X2.columns)
|
|
VIF2 = VIF2.replace(np.nan, 0)
|
|
VIF2 = VIF2.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF2 = VIF2.replace(np.inf, 1.7976931348623157e+308)
|
|
VIF2 = VIF2.loc[[feature]]
|
|
if ((len(targetRows2Arr) > 2) and (flagInf == False)):
|
|
MI2 = mutual_info_classif(DataRows2, targetRows2Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI2List = MI2.tolist()
|
|
MI2List = MI2List[count]
|
|
else:
|
|
MI2List = []
|
|
else:
|
|
corrMatrixComb2 = pd.DataFrame()
|
|
VIF2 = pd.Series()
|
|
MI2List = []
|
|
|
|
if (len(targetRows3Arr) > 0):
|
|
onehotEncoder3 = OneHotEncoder(sparse=False)
|
|
targetRows3Arr = targetRows3Arr.reshape(len(targetRows3Arr), 1)
|
|
onehotEncoder3 = onehotEncoder3.fit_transform(targetRows3Arr)
|
|
hotEncoderDF3 = pd.DataFrame(onehotEncoder3)
|
|
concatDF3 = pd.concat([DataRows3, hotEncoderDF3], axis=1)
|
|
corrMatrixComb3 = concatDF3.corr()
|
|
corrMatrixComb3 = corrMatrixComb3.abs()
|
|
corrMatrixComb3 = corrMatrixComb3.iloc[:,-len(uniqueTarget3):]
|
|
X3 = add_constant(DataRows3.dropna())
|
|
VIF3 = pd.Series([variance_inflation_factor(X3.values, i)
|
|
for i in range(X3.shape[1])],
|
|
index=X3.columns)
|
|
VIF3 = VIF3.replace(np.nan, 0)
|
|
VIF3 = VIF3.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF3 = VIF3.replace(np.inf, 1.7976931348623157e+308)
|
|
VIF3 = VIF3.loc[[feature]]
|
|
if ((len(targetRows3Arr) > 2) and (flagInf == False)):
|
|
MI3 = mutual_info_classif(DataRows3, targetRows3Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI3List = MI3.tolist()
|
|
MI3List = MI3List[count]
|
|
else:
|
|
MI3List = []
|
|
else:
|
|
corrMatrixComb3 = pd.DataFrame()
|
|
VIF3 = pd.Series()
|
|
MI3List = []
|
|
|
|
if (len(targetRows4Arr) > 0):
|
|
onehotEncoder4 = OneHotEncoder(sparse=False)
|
|
targetRows4Arr = targetRows4Arr.reshape(len(targetRows4Arr), 1)
|
|
onehotEncoder4 = onehotEncoder4.fit_transform(targetRows4Arr)
|
|
hotEncoderDF4 = pd.DataFrame(onehotEncoder4)
|
|
concatDF4 = pd.concat([DataRows4, hotEncoderDF4], axis=1)
|
|
corrMatrixComb4 = concatDF4.corr()
|
|
corrMatrixComb4 = corrMatrixComb4.abs()
|
|
corrMatrixComb4 = corrMatrixComb4.iloc[:,-len(uniqueTarget4):]
|
|
X4 = add_constant(DataRows4.dropna())
|
|
VIF4 = pd.Series([variance_inflation_factor(X4.values, i)
|
|
for i in range(X4.shape[1])],
|
|
index=X4.columns)
|
|
VIF4 = VIF4.replace(np.nan, 0)
|
|
VIF4 = VIF4.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF4 = VIF4.replace(np.inf, 1.7976931348623157e+308)
|
|
VIF4 = VIF4.loc[[feature]]
|
|
if ((len(targetRows4Arr) > 2) and (flagInf == False)):
|
|
MI4 = mutual_info_classif(DataRows4, targetRows4Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI4List = MI4.tolist()
|
|
MI4List = MI4List[count]
|
|
else:
|
|
MI4List = []
|
|
else:
|
|
corrMatrixComb4 = pd.DataFrame()
|
|
VIF4 = pd.Series()
|
|
MI4List = []
|
|
|
|
if (len(targetRows5Arr) > 0):
|
|
onehotEncoder5 = OneHotEncoder(sparse=False)
|
|
targetRows5Arr = targetRows5Arr.reshape(len(targetRows5Arr), 1)
|
|
onehotEncoder5 = onehotEncoder5.fit_transform(targetRows5Arr)
|
|
hotEncoderDF5 = pd.DataFrame(onehotEncoder5)
|
|
concatDF5 = pd.concat([DataRows5, hotEncoderDF5], axis=1)
|
|
corrMatrixComb5 = concatDF5.corr()
|
|
corrMatrixComb5 = corrMatrixComb5.abs()
|
|
corrMatrixComb5 = corrMatrixComb5.iloc[:,-len(uniqueTarget5):]
|
|
X5 = add_constant(DataRows5.dropna())
|
|
VIF5 = pd.Series([variance_inflation_factor(X5.values, i)
|
|
for i in range(X5.shape[1])],
|
|
index=X5.columns)
|
|
VIF5 = VIF5.replace(np.nan, 0)
|
|
VIF5 = VIF5.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF5 = VIF5.replace(np.inf, 1.7976931348623157e+308)
|
|
VIF5 = VIF5.loc[[feature]]
|
|
if ((len(targetRows5Arr) > 2) and (flagInf == False)):
|
|
MI5 = mutual_info_classif(DataRows5, targetRows5Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI5List = MI5.tolist()
|
|
MI5List = MI5List[count]
|
|
else:
|
|
MI5List = []
|
|
else:
|
|
corrMatrixComb5 = pd.DataFrame()
|
|
VIF5 = pd.Series()
|
|
MI5List = []
|
|
|
|
if(corrMatrixComb1.empty):
|
|
corrMatrixComb1 = pd.DataFrame()
|
|
else:
|
|
corrMatrixComb1 = corrMatrixComb1.loc[[feature]]
|
|
if(corrMatrixComb2.empty):
|
|
corrMatrixComb2 = pd.DataFrame()
|
|
else:
|
|
corrMatrixComb2 = corrMatrixComb2.loc[[feature]]
|
|
if(corrMatrixComb3.empty):
|
|
corrMatrixComb3 = pd.DataFrame()
|
|
else:
|
|
corrMatrixComb3 = corrMatrixComb3.loc[[feature]]
|
|
if(corrMatrixComb4.empty):
|
|
corrMatrixComb4 = pd.DataFrame()
|
|
else:
|
|
corrMatrixComb4 = corrMatrixComb4.loc[[feature]]
|
|
if(corrMatrixComb5.empty):
|
|
corrMatrixComb5 = pd.DataFrame()
|
|
else:
|
|
corrMatrixComb5 = corrMatrixComb5.loc[[feature]]
|
|
|
|
targetRows1ArrDF = pd.DataFrame(targetRows1Arr)
|
|
targetRows2ArrDF = pd.DataFrame(targetRows2Arr)
|
|
targetRows3ArrDF = pd.DataFrame(targetRows3Arr)
|
|
targetRows4ArrDF = pd.DataFrame(targetRows4Arr)
|
|
targetRows5ArrDF = pd.DataFrame(targetRows5Arr)
|
|
|
|
concatAllDF1 = pd.concat([DataRows1, targetRows1ArrDF], axis=1)
|
|
concatAllDF2 = pd.concat([DataRows2, targetRows2ArrDF], axis=1)
|
|
concatAllDF3 = pd.concat([DataRows3, targetRows3ArrDF], axis=1)
|
|
concatAllDF4 = pd.concat([DataRows4, targetRows4ArrDF], axis=1)
|
|
concatAllDF5 = pd.concat([DataRows5, targetRows5ArrDF], axis=1)
|
|
|
|
corrMatrixCombTotal1 = concatAllDF1.corr()
|
|
corrMatrixCombTotal1 = corrMatrixCombTotal1.abs()
|
|
corrMatrixCombTotal2 = concatAllDF2.corr()
|
|
corrMatrixCombTotal2 = corrMatrixCombTotal2.abs()
|
|
corrMatrixCombTotal3 = concatAllDF3.corr()
|
|
corrMatrixCombTotal3 = corrMatrixCombTotal3.abs()
|
|
corrMatrixCombTotal4 = concatAllDF4.corr()
|
|
corrMatrixCombTotal4 = corrMatrixCombTotal4.abs()
|
|
corrMatrixCombTotal5 = concatAllDF5.corr()
|
|
corrMatrixCombTotal5 = corrMatrixCombTotal5.abs()
|
|
|
|
corrMatrixCombTotal1 = corrMatrixCombTotal1.loc[[feature]]
|
|
corrMatrixCombTotal1 = corrMatrixCombTotal1.iloc[:,-1]
|
|
corrMatrixCombTotal2 = corrMatrixCombTotal2.loc[[feature]]
|
|
corrMatrixCombTotal2 = corrMatrixCombTotal2.iloc[:,-1]
|
|
corrMatrixCombTotal3 = corrMatrixCombTotal3.loc[[feature]]
|
|
corrMatrixCombTotal3 = corrMatrixCombTotal3.iloc[:,-1]
|
|
corrMatrixCombTotal4 = corrMatrixCombTotal4.loc[[feature]]
|
|
corrMatrixCombTotal4 = corrMatrixCombTotal4.iloc[:,-1]
|
|
corrMatrixCombTotal5 = corrMatrixCombTotal5.loc[[feature]]
|
|
corrMatrixCombTotal5 = corrMatrixCombTotal5.iloc[:,-1]
|
|
|
|
corrMatrixCombTotal1 = pd.concat([corrMatrixCombTotal1.tail(1)])
|
|
corrMatrixCombTotal2 = pd.concat([corrMatrixCombTotal2.tail(1)])
|
|
corrMatrixCombTotal3 = pd.concat([corrMatrixCombTotal3.tail(1)])
|
|
corrMatrixCombTotal4 = pd.concat([corrMatrixCombTotal4.tail(1)])
|
|
corrMatrixCombTotal5 = pd.concat([corrMatrixCombTotal5.tail(1)])
|
|
|
|
packCorrLoc = []
|
|
|
|
packCorrLoc.append(corrMatrix1.to_json())
|
|
packCorrLoc.append(corrMatrix2.to_json())
|
|
packCorrLoc.append(corrMatrix3.to_json())
|
|
packCorrLoc.append(corrMatrix4.to_json())
|
|
packCorrLoc.append(corrMatrix5.to_json())
|
|
|
|
packCorrLoc.append(corrMatrixComb1.to_json())
|
|
packCorrLoc.append(corrMatrixComb2.to_json())
|
|
packCorrLoc.append(corrMatrixComb3.to_json())
|
|
packCorrLoc.append(corrMatrixComb4.to_json())
|
|
packCorrLoc.append(corrMatrixComb5.to_json())
|
|
|
|
packCorrLoc.append(corrMatrixCombTotal1.to_json())
|
|
packCorrLoc.append(corrMatrixCombTotal2.to_json())
|
|
packCorrLoc.append(corrMatrixCombTotal3.to_json())
|
|
packCorrLoc.append(corrMatrixCombTotal4.to_json())
|
|
packCorrLoc.append(corrMatrixCombTotal5.to_json())
|
|
|
|
packCorrLoc.append(VIF1.to_json())
|
|
packCorrLoc.append(VIF2.to_json())
|
|
packCorrLoc.append(VIF3.to_json())
|
|
packCorrLoc.append(VIF4.to_json())
|
|
packCorrLoc.append(VIF5.to_json())
|
|
|
|
packCorrLoc.append(json.dumps(MI1List))
|
|
packCorrLoc.append(json.dumps(MI2List))
|
|
packCorrLoc.append(json.dumps(MI3List))
|
|
packCorrLoc.append(json.dumps(MI4List))
|
|
packCorrLoc.append(json.dumps(MI5List))
|
|
|
|
return packCorrLoc
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
|
|
@app.route('/data/thresholdDataSpace', methods=["GET", "POST"])
|
|
def Seperation():
|
|
|
|
thresholds = request.get_data().decode('utf8').replace("'", '"')
|
|
thresholds = json.loads(thresholds)
|
|
thresholdsPos = thresholds['PositiveValue']
|
|
thresholdsNeg = thresholds['NegativeValue']
|
|
|
|
getCorrectPrediction = []
|
|
|
|
for index, value in enumerate(yPredictProb):
|
|
getCorrectPrediction.append(value[yData[index]]*100)
|
|
|
|
quadrant1 = []
|
|
quadrant2 = []
|
|
quadrant3 = []
|
|
quadrant4 = []
|
|
quadrant5 = []
|
|
|
|
probabilityPredictions = []
|
|
|
|
for index, value in enumerate(getCorrectPrediction):
|
|
if (value > 50 and value > thresholdsPos):
|
|
quadrant1.append(index)
|
|
elif (value > 50 and value <= thresholdsPos):
|
|
quadrant2.append(index)
|
|
elif (value <= 50 and value > thresholdsNeg):
|
|
quadrant3.append(index)
|
|
else:
|
|
quadrant4.append(index)
|
|
quadrant5.append(index)
|
|
probabilityPredictions.append(value)
|
|
|
|
# Main Features
|
|
DataRows1 = XData.iloc[quadrant1, :]
|
|
DataRows2 = XData.iloc[quadrant2, :]
|
|
DataRows3 = XData.iloc[quadrant3, :]
|
|
DataRows4 = XData.iloc[quadrant4, :]
|
|
DataRows5 = XData.iloc[quadrant5, :]
|
|
|
|
|
|
Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5)
|
|
|
|
corrMatrix1 = DataRows1.corr()
|
|
corrMatrix1 = corrMatrix1.abs()
|
|
corrMatrix2 = DataRows2.corr()
|
|
corrMatrix2 = corrMatrix2.abs()
|
|
corrMatrix3 = DataRows3.corr()
|
|
corrMatrix3 = corrMatrix3.abs()
|
|
corrMatrix4 = DataRows4.corr()
|
|
corrMatrix4 = corrMatrix4.abs()
|
|
corrMatrix5 = DataRows5.corr()
|
|
corrMatrix5 = corrMatrix5.abs()
|
|
|
|
DataRows1 = DataRows1.reset_index(drop=True)
|
|
DataRows2 = DataRows2.reset_index(drop=True)
|
|
DataRows3 = DataRows3.reset_index(drop=True)
|
|
DataRows4 = DataRows4.reset_index(drop=True)
|
|
DataRows5 = DataRows5.reset_index(drop=True)
|
|
|
|
targetRows1 = [yData[i] for i in quadrant1]
|
|
targetRows2 = [yData[i] for i in quadrant2]
|
|
targetRows3 = [yData[i] for i in quadrant3]
|
|
targetRows4 = [yData[i] for i in quadrant4]
|
|
targetRows5 = [yData[i] for i in quadrant5]
|
|
|
|
targetRows1Arr = np.array(targetRows1)
|
|
targetRows2Arr = np.array(targetRows2)
|
|
targetRows3Arr = np.array(targetRows3)
|
|
targetRows4Arr = np.array(targetRows4)
|
|
targetRows5Arr = np.array(targetRows5)
|
|
|
|
uniqueTarget1 = unique(targetRows1)
|
|
uniqueTarget2 = unique(targetRows2)
|
|
uniqueTarget3 = unique(targetRows3)
|
|
uniqueTarget4 = unique(targetRows4)
|
|
uniqueTarget5 = unique(targetRows5)
|
|
|
|
if (len(targetRows1Arr) > 0):
|
|
onehotEncoder1 = OneHotEncoder(sparse=False)
|
|
targetRows1Arr = targetRows1Arr.reshape(len(targetRows1Arr), 1)
|
|
onehotEncoder1 = onehotEncoder1.fit_transform(targetRows1Arr)
|
|
hotEncoderDF1 = pd.DataFrame(onehotEncoder1)
|
|
concatDF1 = pd.concat([DataRows1, hotEncoderDF1], axis=1)
|
|
corrMatrixComb1 = concatDF1.corr()
|
|
corrMatrixComb1 = corrMatrixComb1.abs()
|
|
corrMatrixComb1 = corrMatrixComb1.iloc[:,-len(uniqueTarget1):]
|
|
X1 = add_constant(DataRows1.dropna())
|
|
VIF1 = pd.Series([variance_inflation_factor(X1.values, i)
|
|
for i in range(X1.shape[1])],
|
|
index=X1.columns)
|
|
VIF1 = VIF1.replace(np.nan, 0)
|
|
VIF1 = VIF1.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF1 = VIF1.replace(np.inf, 1.7976931348623157e+308)
|
|
if (len(targetRows1Arr) > 2):
|
|
MI1 = mutual_info_classif(DataRows1, targetRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI1List = MI1.tolist()
|
|
else:
|
|
MI1List = []
|
|
else:
|
|
corrMatrixComb1 = pd.DataFrame()
|
|
VIF1 = pd.Series()
|
|
MI1List = []
|
|
|
|
if (len(targetRows2Arr) > 0):
|
|
onehotEncoder2 = OneHotEncoder(sparse=False)
|
|
targetRows2Arr = targetRows2Arr.reshape(len(targetRows2Arr), 1)
|
|
onehotEncoder2 = onehotEncoder2.fit_transform(targetRows2Arr)
|
|
hotEncoderDF2 = pd.DataFrame(onehotEncoder2)
|
|
concatDF2 = pd.concat([DataRows2, hotEncoderDF2], axis=1)
|
|
corrMatrixComb2 = concatDF2.corr()
|
|
corrMatrixComb2 = corrMatrixComb2.abs()
|
|
corrMatrixComb2 = corrMatrixComb2.iloc[:,-len(uniqueTarget2):]
|
|
X2 = add_constant(DataRows2.dropna())
|
|
VIF2 = pd.Series([variance_inflation_factor(X2.values, i)
|
|
for i in range(X2.shape[1])],
|
|
index=X2.columns)
|
|
VIF2 = VIF2.replace(np.nan, 0)
|
|
VIF2 = VIF2.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF2 = VIF2.replace(np.inf, 1.7976931348623157e+308)
|
|
if (len(targetRows2Arr) > 2):
|
|
MI2 = mutual_info_classif(DataRows2, targetRows2Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI2List = MI2.tolist()
|
|
else:
|
|
MI2List = []
|
|
else:
|
|
corrMatrixComb2 = pd.DataFrame()
|
|
VIF2 = pd.Series()
|
|
MI2List = []
|
|
|
|
if (len(targetRows3Arr) > 0):
|
|
onehotEncoder3 = OneHotEncoder(sparse=False)
|
|
targetRows3Arr = targetRows3Arr.reshape(len(targetRows3Arr), 1)
|
|
onehotEncoder3 = onehotEncoder3.fit_transform(targetRows3Arr)
|
|
hotEncoderDF3 = pd.DataFrame(onehotEncoder3)
|
|
concatDF3 = pd.concat([DataRows3, hotEncoderDF3], axis=1)
|
|
corrMatrixComb3 = concatDF3.corr()
|
|
corrMatrixComb3 = corrMatrixComb3.abs()
|
|
corrMatrixComb3 = corrMatrixComb3.iloc[:,-len(uniqueTarget3):]
|
|
X3 = add_constant(DataRows3.dropna())
|
|
VIF3 = pd.Series([variance_inflation_factor(X3.values, i)
|
|
for i in range(X3.shape[1])],
|
|
index=X3.columns)
|
|
VIF3 = VIF3.replace(np.nan, 0)
|
|
VIF3 = VIF3.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF3 = VIF3.replace(np.inf, 1.7976931348623157e+308)
|
|
if (len(targetRows3Arr) > 2):
|
|
MI3 = mutual_info_classif(DataRows3, targetRows3Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI3List = MI3.tolist()
|
|
else:
|
|
MI3List = []
|
|
else:
|
|
corrMatrixComb3 = pd.DataFrame()
|
|
VIF3 = pd.Series()
|
|
MI3List = []
|
|
|
|
if (len(targetRows4Arr) > 0):
|
|
onehotEncoder4 = OneHotEncoder(sparse=False)
|
|
targetRows4Arr = targetRows4Arr.reshape(len(targetRows4Arr), 1)
|
|
onehotEncoder4 = onehotEncoder4.fit_transform(targetRows4Arr)
|
|
hotEncoderDF4 = pd.DataFrame(onehotEncoder4)
|
|
concatDF4 = pd.concat([DataRows4, hotEncoderDF4], axis=1)
|
|
corrMatrixComb4 = concatDF4.corr()
|
|
corrMatrixComb4 = corrMatrixComb4.abs()
|
|
corrMatrixComb4 = corrMatrixComb4.iloc[:,-len(uniqueTarget4):]
|
|
X4 = add_constant(DataRows4.dropna())
|
|
VIF4 = pd.Series([variance_inflation_factor(X4.values, i)
|
|
for i in range(X4.shape[1])],
|
|
index=X4.columns)
|
|
VIF4 = VIF4.replace(np.nan, 0)
|
|
VIF4 = VIF4.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF4 = VIF4.replace(np.inf, 1.7976931348623157e+308)
|
|
if (len(targetRows4Arr) > 2):
|
|
MI4 = mutual_info_classif(DataRows4, targetRows4Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI4List = MI4.tolist()
|
|
else:
|
|
MI4List = []
|
|
else:
|
|
corrMatrixComb4 = pd.DataFrame()
|
|
VIF4 = pd.Series()
|
|
MI4List = []
|
|
|
|
if (len(targetRows5Arr) > 0):
|
|
onehotEncoder5 = OneHotEncoder(sparse=False)
|
|
targetRows5Arr = targetRows5Arr.reshape(len(targetRows5Arr), 1)
|
|
onehotEncoder5 = onehotEncoder5.fit_transform(targetRows5Arr)
|
|
hotEncoderDF5 = pd.DataFrame(onehotEncoder5)
|
|
concatDF5 = pd.concat([DataRows5, hotEncoderDF5], axis=1)
|
|
corrMatrixComb5 = concatDF5.corr()
|
|
corrMatrixComb5 = corrMatrixComb5.abs()
|
|
corrMatrixComb5 = corrMatrixComb5.iloc[:,-len(uniqueTarget5):]
|
|
X5 = add_constant(DataRows5.dropna())
|
|
VIF5 = pd.Series([variance_inflation_factor(X5.values, i)
|
|
for i in range(X5.shape[1])],
|
|
index=X5.columns)
|
|
VIF5 = VIF5.replace(np.nan, 0)
|
|
VIF5 = VIF5.replace(-np.inf, 1.7976931348623157e-308)
|
|
VIF5 = VIF5.replace(np.inf, 1.7976931348623157e+308)
|
|
if (len(targetRows5Arr) > 2):
|
|
MI5 = mutual_info_classif(DataRows5, targetRows5Arr, n_neighbors=3, random_state=RANDOM_SEED)
|
|
MI5List = MI5.tolist()
|
|
else:
|
|
MI5List = []
|
|
else:
|
|
corrMatrixComb5 = pd.DataFrame()
|
|
VIF5 = pd.Series()
|
|
MI5List = []
|
|
|
|
targetRows1ArrDF = pd.DataFrame(targetRows1Arr)
|
|
targetRows2ArrDF = pd.DataFrame(targetRows2Arr)
|
|
targetRows3ArrDF = pd.DataFrame(targetRows3Arr)
|
|
targetRows4ArrDF = pd.DataFrame(targetRows4Arr)
|
|
targetRows5ArrDF = pd.DataFrame(targetRows5Arr)
|
|
|
|
concatAllDF1 = pd.concat([DataRows1, targetRows1ArrDF], axis=1)
|
|
concatAllDF2 = pd.concat([DataRows2, targetRows2ArrDF], axis=1)
|
|
concatAllDF3 = pd.concat([DataRows3, targetRows3ArrDF], axis=1)
|
|
concatAllDF4 = pd.concat([DataRows4, targetRows4ArrDF], axis=1)
|
|
concatAllDF5 = pd.concat([DataRows5, targetRows5ArrDF], axis=1)
|
|
|
|
corrMatrixCombTotal1 = concatAllDF1.corr()
|
|
corrMatrixCombTotal1 = corrMatrixCombTotal1.abs()
|
|
corrMatrixCombTotal2 = concatAllDF2.corr()
|
|
corrMatrixCombTotal2 = corrMatrixCombTotal2.abs()
|
|
corrMatrixCombTotal3 = concatAllDF3.corr()
|
|
corrMatrixCombTotal3 = corrMatrixCombTotal3.abs()
|
|
corrMatrixCombTotal4 = concatAllDF4.corr()
|
|
corrMatrixCombTotal4 = corrMatrixCombTotal4.abs()
|
|
corrMatrixCombTotal5 = concatAllDF5.corr()
|
|
corrMatrixCombTotal5 = corrMatrixCombTotal5.abs()
|
|
|
|
corrMatrixCombTotal1 = pd.concat([corrMatrixCombTotal1.tail(1)])
|
|
corrMatrixCombTotal2 = pd.concat([corrMatrixCombTotal2.tail(1)])
|
|
corrMatrixCombTotal3 = pd.concat([corrMatrixCombTotal3.tail(1)])
|
|
corrMatrixCombTotal4 = pd.concat([corrMatrixCombTotal4.tail(1)])
|
|
corrMatrixCombTotal5 = pd.concat([corrMatrixCombTotal5.tail(1)])
|
|
|
|
global packCorr
|
|
packCorr = []
|
|
packCorr.append(json.dumps(columnsNewGen))
|
|
packCorr.append(json.dumps(target_names))
|
|
packCorr.append(json.dumps(probabilityPredictions))
|
|
|
|
packCorr.append(corrMatrix1.to_json())
|
|
packCorr.append(corrMatrix2.to_json())
|
|
packCorr.append(corrMatrix3.to_json())
|
|
packCorr.append(corrMatrix4.to_json())
|
|
packCorr.append(corrMatrix5.to_json())
|
|
|
|
packCorr.append(corrMatrixComb1.to_json())
|
|
packCorr.append(corrMatrixComb2.to_json())
|
|
packCorr.append(corrMatrixComb3.to_json())
|
|
packCorr.append(corrMatrixComb4.to_json())
|
|
packCorr.append(corrMatrixComb5.to_json())
|
|
|
|
packCorr.append(corrMatrixCombTotal1.to_json())
|
|
packCorr.append(corrMatrixCombTotal2.to_json())
|
|
packCorr.append(corrMatrixCombTotal3.to_json())
|
|
packCorr.append(corrMatrixCombTotal4.to_json())
|
|
packCorr.append(corrMatrixCombTotal5.to_json())
|
|
|
|
packCorr.append(json.dumps(uniqueTarget1))
|
|
packCorr.append(json.dumps(uniqueTarget2))
|
|
packCorr.append(json.dumps(uniqueTarget3))
|
|
packCorr.append(json.dumps(uniqueTarget4))
|
|
packCorr.append(json.dumps(uniqueTarget5))
|
|
|
|
packCorr.append(VIF1.to_json())
|
|
packCorr.append(VIF2.to_json())
|
|
packCorr.append(VIF3.to_json())
|
|
packCorr.append(VIF4.to_json())
|
|
packCorr.append(VIF5.to_json())
|
|
|
|
packCorr.append(json.dumps(MI1List))
|
|
packCorr.append(json.dumps(MI2List))
|
|
packCorr.append(json.dumps(MI3List))
|
|
packCorr.append(json.dumps(MI4List))
|
|
packCorr.append(json.dumps(MI5List))
|
|
|
|
packCorr.append(list(tracker))
|
|
packCorr.append(list(XData.columns.values.tolist()))
|
|
packCorr.append(json.dumps(columnsNames))
|
|
|
|
return 'Everything Okay'
|
|
|
|
@app.route('/data/returnCorrelationsTransformed', methods=["GET", "POST"])
|
|
def SendCorrelTransformed():
|
|
global packCorrTransformed
|
|
|
|
response = {
|
|
'correlResulTranformed': packCorrTransformed
|
|
}
|
|
return jsonify(response)
|
|
|
|
@app.route('/data/returnCorrelations', methods=["GET", "POST"])
|
|
def SendCorrel():
|
|
global packCorr
|
|
|
|
response = {
|
|
'correlResul': packCorr
|
|
}
|
|
return jsonify(response)
|
|
|
|
def unique(list1):
|
|
|
|
# intilize a null list
|
|
unique_list = []
|
|
|
|
# traverse for all elements
|
|
for x in list1:
|
|
# check if exists in unique_list or not
|
|
if x not in unique_list:
|
|
unique_list.append(x)
|
|
return unique_list
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
|
|
@app.route('/data/AddRemFun', methods=["GET", "POST"])
|
|
def ManipulFeat():
|
|
featureProcess = request.get_data().decode('utf8').replace("'", '"')
|
|
featureProcess = json.loads(featureProcess)
|
|
featureProcessExtract = featureProcess['featureAddRem']
|
|
executeModel(featureProcessExtract, 1, '')
|
|
return 'Okay'
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
|
|
@app.route('/data/AddRemGenFun', methods=["GET", "POST"])
|
|
def ManipulFeatGen():
|
|
featureProcess = request.get_data().decode('utf8').replace("'", '"')
|
|
featureProcess = json.loads(featureProcess)
|
|
featureProcessExtract = featureProcess['featureAddRemGen']
|
|
executeModel(featureProcessExtract, 2, '')
|
|
return 'Okay'
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
|
|
@app.route('/data/compareFun', methods=["GET", "POST"])
|
|
def CompareFunPy():
|
|
global featureCompareData
|
|
global columnsKeep
|
|
global XDataGen
|
|
global IDsToCompare
|
|
global columnsNewGen
|
|
|
|
retrieveComparison = request.get_data().decode('utf8').replace("'", '"')
|
|
retrieveComparison = json.loads(retrieveComparison)
|
|
compareMode = retrieveComparison['compareNumber']
|
|
IDsToCompare = retrieveComparison['getIDs']
|
|
XDataGen = XDataStored.copy()
|
|
columns = XData.columns.values.tolist()
|
|
#columnsOriganl = XDataNoRemoval.columns.values.tolist()
|
|
|
|
columnsKeep = []
|
|
columnsKeepNonOrig = []
|
|
columnsKeepID = []
|
|
for indx, col in enumerate(columns):
|
|
if indx in IDsToCompare:
|
|
columnsKeepNonOrig.append(col)
|
|
columnExtracted = re.findall('\d+', col)
|
|
columnsKeep.append(columnsNewGen[int(columnExtracted[0]) - 1])
|
|
columnsKeepID.append(str(col))
|
|
if (compareMode == 1):
|
|
XDataGen = XData[columnsKeepNonOrig]
|
|
feat1 = XDataGen.iloc[:,0]
|
|
feat2 = XDataGen.iloc[:,1]
|
|
XDataGen[columnsKeepID[0]+'+'+columnsKeepID[1]] = feat1 + feat2
|
|
XDataGen['|'+columnsKeepID[0]+'-'+columnsKeepID[1]+'|'] = abs(feat1 - feat2)
|
|
XDataGen[columnsKeepID[0]+'x'+columnsKeepID[1]] = feat1 * feat2
|
|
XDataGen[columnsKeepID[0]+'/'+columnsKeepID[1]] = feat1 / feat2
|
|
XDataGen[columnsKeepID[1]+'/'+columnsKeepID[0]] = feat2 / feat1
|
|
columnsKeep.append(columnsKeepID[0]+'+'+columnsKeepID[1])
|
|
columnsKeep.append('|'+columnsKeepID[0]+'-'+columnsKeepID[1]+'|')
|
|
columnsKeep.append(columnsKeepID[0]+'x'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[0]+'/'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[1]+'/'+columnsKeepID[0])
|
|
elif (compareMode == 2):
|
|
XDataGen = XData[columnsKeepNonOrig]
|
|
feat1 = XDataGen.iloc[:,0]
|
|
feat2 = XDataGen.iloc[:,1]
|
|
feat3 = XDataGen.iloc[:,2]
|
|
XDataGen[columnsKeepID[0]+'+'+columnsKeepID[1]] = feat1 + feat2
|
|
XDataGen[columnsKeepID[1]+'+'+columnsKeepID[2]] = feat2 + feat3
|
|
XDataGen[columnsKeepID[0]+'+'+columnsKeepID[2]] = feat1 + feat3
|
|
XDataGen[columnsKeepID[0]+'+'+columnsKeepID[1]+'+'+columnsKeepID[2]] = feat1 + feat2 + feat3
|
|
XDataGen['|'+columnsKeepID[0]+'-'+columnsKeepID[1]+'|'] = abs(feat1 - feat2)
|
|
XDataGen['|'+columnsKeepID[1]+'-'+columnsKeepID[2]+'|'] = abs(feat2 - feat3)
|
|
XDataGen['|'+columnsKeepID[0]+'-'+columnsKeepID[2]+'|'] = abs(feat1 - feat3)
|
|
XDataGen['|'+columnsKeepID[0]+'-'+columnsKeepID[1]+'-'+columnsKeepID[2]+'|'] = abs(feat1 - feat2 - feat3)
|
|
XDataGen[columnsKeepID[0]+'x'+columnsKeepID[1]] = feat1 * feat2
|
|
XDataGen[columnsKeepID[1]+'x'+columnsKeepID[2]] = feat2 * feat3
|
|
XDataGen[columnsKeepID[0]+'x'+columnsKeepID[2]] = feat1 * feat3
|
|
XDataGen[columnsKeepID[0]+'x'+columnsKeepID[1]+'x'+columnsKeepID[2]] = feat1 * feat2 * feat3
|
|
XDataGen[columnsKeepID[0]+'/'+columnsKeepID[1]] = feat1 / feat2
|
|
XDataGen[columnsKeepID[1]+'/'+columnsKeepID[0]] = feat2 / feat1
|
|
XDataGen[columnsKeepID[1]+'/'+columnsKeepID[2]] = feat2 / feat3
|
|
XDataGen[columnsKeepID[2]+'/'+columnsKeepID[1]] = feat3 / feat2
|
|
XDataGen[columnsKeepID[0]+'/'+columnsKeepID[2]] = feat1 / feat3
|
|
XDataGen[columnsKeepID[2]+'/'+columnsKeepID[0]] = feat3 / feat1
|
|
XDataGen[columnsKeepID[0]+'/'+columnsKeepID[1]+'/'+columnsKeepID[2]] = feat1 / feat2 / feat3
|
|
XDataGen[columnsKeepID[0]+'/'+columnsKeepID[2]+'/'+columnsKeepID[1]] = feat1 / feat3 / feat2
|
|
XDataGen[columnsKeepID[1]+'/'+columnsKeepID[2]+'/'+columnsKeepID[0]] = feat2 / feat3 / feat1
|
|
XDataGen[columnsKeepID[1]+'/'+columnsKeepID[0]+'/'+columnsKeepID[2]] = feat2 / feat1 / feat3
|
|
XDataGen[columnsKeepID[2]+'/'+columnsKeepID[0]+'/'+columnsKeepID[1]] = feat3 / feat1 / feat2
|
|
XDataGen[columnsKeepID[2]+'/'+columnsKeepID[1]+'/'+columnsKeepID[0]] = feat3 / feat2 / feat1
|
|
|
|
columnsKeep.append(columnsKeepID[0]+'+'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[1]+'+'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[0]+'+'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[0]+'+'+columnsKeepID[1]+'+'+columnsKeepID[2])
|
|
columnsKeep.append('|'+columnsKeepID[0]+'-'+columnsKeepID[1]+'|')
|
|
columnsKeep.append('|'+columnsKeepID[1]+'-'+columnsKeepID[2]+'|')
|
|
columnsKeep.append('|'+columnsKeepID[0]+'-'+columnsKeepID[2]+'|')
|
|
columnsKeep.append('|'+columnsKeepID[0]+'-'+columnsKeepID[1]+'-'+columnsKeepID[2]+'|')
|
|
columnsKeep.append(columnsKeepID[0]+'x'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[1]+'x'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[0]+'x'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[0]+'x'+columnsKeepID[1]+'x'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[0]+'/'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[1]+'/'+columnsKeepID[0])
|
|
columnsKeep.append(columnsKeepID[1]+'/'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[2]+'/'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[0]+'/'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[2]+'/'+columnsKeepID[0])
|
|
columnsKeep.append(columnsKeepID[0]+'/'+columnsKeepID[1]+'/'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[0]+'/'+columnsKeepID[2]+'/'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[1]+'/'+columnsKeepID[2]+'/'+columnsKeepID[0])
|
|
columnsKeep.append(columnsKeepID[1]+'/'+columnsKeepID[0]+'/'+columnsKeepID[2])
|
|
columnsKeep.append(columnsKeepID[2]+'/'+columnsKeepID[0]+'/'+columnsKeepID[1])
|
|
columnsKeep.append(columnsKeepID[2]+'/'+columnsKeepID[1]+'/'+columnsKeepID[0])
|
|
else:
|
|
pass
|
|
featureCompareData = estimatorFeatureSelection(XDataGen, estimator)
|
|
return 'Okay'
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
|
|
@app.route('/data/storeGeneratedFeatures', methods=["GET", "POST"])
|
|
def storeGeneratedFeat():
|
|
print('Generate')
|
|
executeModel([], 3, '')
|
|
return 'Okay'
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
|
|
@app.route('/data/transformation', methods=["GET", "POST"])
|
|
def transformFeatures():
|
|
print('Transform')
|
|
retrieveTransform = request.get_data().decode('utf8').replace("'", '"')
|
|
retrieveTransform = json.loads(retrieveTransform)
|
|
clickedNodeName = retrieveTransform['nameClicked']
|
|
removeNodeID = retrieveTransform['removeNode']
|
|
executeModel([removeNodeID[1]], 4, clickedNodeName[0])
|
|
return 'Okay' |