From b37ba796a0e27467e0218f589ce7e93ca79e9d66 Mon Sep 17 00:00:00 2001 From: Angelos Chatzimparmpas Date: Fri, 22 Jan 2021 01:38:34 +0100 Subject: [PATCH] new --- __pycache__/run.cpython-38.pyc | Bin 36849 -> 38292 bytes .../output.pkl | Bin 422 -> 0 bytes .../metadata.json | 1 + .../output.pkl | Bin 0 -> 835 bytes .../output.pkl | Bin 430 -> 0 bytes .../output.pkl | Bin 427 -> 0 bytes .../metadata.json | 1 + .../metadata.json | 1 + .../metadata.json | 1 + .../metadata.json | 1 + .../output.pkl | Bin 0 -> 724 bytes .../metadata.json | 1 + .../metadata.json | 1 - .../output.pkl | Bin 0 -> 428 bytes .../output.pkl | Bin 0 -> 2549 bytes .../output.pkl | Bin 422 -> 0 bytes .../metadata.json | 1 - .../output.pkl | Bin 522 -> 0 bytes .../output.pkl | Bin 0 -> 1352 bytes .../metadata.json | 1 + .../metadata.json | 1 + .../output.pkl | Bin 0 -> 402 bytes .../output.pkl | Bin 404 -> 0 bytes .../output.pkl | Bin 0 -> 1351 bytes .../output.pkl | Bin 0 -> 440 bytes .../output.pkl | Bin 419 -> 0 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set" d3.select("#data").select("input").remove(); // Remove the selection field. EventBus.$emit('SendToServerDataSetConfirmation', this.defaultDataSet) diff --git a/frontend/src/components/Main.vue b/frontend/src/components/Main.vue index d0ac0cd..8a7b728 100755 --- a/frontend/src/components/Main.vue +++ b/frontend/src/components/Main.vue @@ -149,7 +149,7 @@ export default Vue.extend({ DataResults: '', keyNow: 1, instancesImportance: '', - RetrieveValueFile: 'IrisC', // this is for the default data set + RetrieveValueFile: 'BreastC', // this is for the default data set SelectedFeaturesPerClassifier: '', FinalResults: 0, selectedAlgorithm: '', @@ -476,7 +476,7 @@ export default Vue.extend({ EventBus.$emit('SlidersCall') this.keySlider = false } - EventBus.$emit('ConfirmDataSet') // REMOVE THAT! + // EventBus.$emit('ConfirmDataSet') // REMOVE THAT! } else { EventBus.$emit('dataSpace', this.correlResul) EventBus.$emit('quad', this.correlResul) diff --git a/insertMongo.py b/insertMongo.py index fffbe5b..d914455 100644 --- a/insertMongo.py +++ b/insertMongo.py @@ -10,7 +10,7 @@ def import_content(filepath): mng_client = pymongo.MongoClient('localhost', 27017) mng_db = mng_client['mydb'] #collection_name = 'StanceCTest' - collection_name = 'StanceC' + collection_name = 'HeartC' db_cm = mng_db[collection_name] cdir = os.path.dirname(__file__) file_res = os.path.join(cdir, filepath) @@ -21,5 +21,5 @@ def import_content(filepath): db_cm.insert(data_json) if __name__ == "__main__": - filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stance.csv' + filepath = '/Users/anchaa/Documents/Research/FeatureEnVi_code/extra_data_sets/heart.csv' import_content(filepath) \ No newline at end of file diff --git a/run.py b/run.py index 9eb28e9..8af9d14 100644 --- a/run.py +++ b/run.py @@ -14,7 +14,7 @@ import multiprocessing from joblib import Memory -from sklearn.svm import SVC +from xgboost import XGBClassifier from sklearn import model_selection from bayes_opt import BayesianOptimization from sklearn.model_selection import cross_validate @@ -257,7 +257,7 @@ def retrieveFileName(): global listofTransformations listofTransformations = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"] - print('data set:',fileName) + DataRawLength = -1 DataRawLengthTest = -1 data = json.loads(fileName) @@ -274,8 +274,10 @@ def retrieveFileName(): target_names.append('Biodegradable') elif data['fileName'] == 'BreastC': CollectionDB = mongo.db.breastC.find() - target_names.append('Malignant') - target_names.append('Benign') + elif data['fileName'] == 'DiabetesC': + CollectionDB = mongo.db.diabetesC.find() + target_names.append('Negative') + target_names.append('Positive') else: CollectionDB = mongo.db.IrisC.find() DataResultsRaw = [] @@ -333,7 +335,7 @@ def sendToServerData(): for i, value in enumerate(AllTargets): if (i == 0): previous = value - if (data['fileName'] == 'IrisC'): + if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): target_names.append(value) else: pass @@ -341,7 +343,7 @@ def sendToServerData(): AllTargetsFloatValues.append(Class) else: Class = Class + 1 - if (data['fileName'] == 'IrisC'): + if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): target_names.append(value) else: pass @@ -439,7 +441,7 @@ def dataSetSelection(): for i, value in enumerate(AllTargets): if (i == 0): previous = value - if (data['fileName'] == 'IrisC'): + if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): target_names.append(value) else: pass @@ -447,7 +449,7 @@ def dataSetSelection(): AllTargetsFloatValues.append(Class) else: Class = Class + 1 - if (data['fileName'] == 'IrisC'): + if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): target_names.append(value) else: pass @@ -484,9 +486,11 @@ def dataSetSelection(): def create_global_function(): global estimator - def estimator(C, gamma): + def estimator(n_estimators, eta, max_depth): # initialize model - model = SVC(C=C, gamma=gamma, degree=1, random_state=RANDOM_SEED) + n_estimators = int(n_estimators) + max_depth = int(max_depth) + model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False) # set in cross-validation result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy') # result is mean of test_score @@ -524,14 +528,14 @@ def executeModel(exeCall, flagEx, nodeTransfName): XData = XDataStored.copy() XDataStoredOriginal = XDataStored.copy() columnsNewGen = keepOriginalFeatures.columns.values.tolist() - # Bayesian Optimization for 50 iterations + # Bayesian Optimization CHANGE INIT_POINTS! if (keyFirstTime): create_global_function() - params = {"C": (0.01, 100), "gamma": (0.01, 100)} - svc_bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED) - svc_bayesopt.maximize(init_points=30, n_iter=20, acq='ucb') - bestParams = svc_bayesopt.max['params'] - estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True, random_state=RANDOM_SEED) + params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "max_depth": (6,12)} + bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED) + bayesopt.maximize(init_points=35, n_iter=15, acq='ucb') + bestParams = bayesopt.max['params'] + estimator = XGBClassifier(n_estimators=int(bestParams.get('n_estimators')), eta=bestParams.get('eta'), max_depth=int(bestParams.get('max_depth')), probability=True, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False) if (len(exeCall) != 0): if (flagEx == 1): @@ -580,21 +584,35 @@ def executeModel(exeCall, flagEx, nodeTransfName): 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, 2.2250738585072014e-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, 2.2250738585072014e-308) + dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308) XData[nodeTransfName] = dfTemp elif (splittedCol[1] == 'e2'): - XData[nodeTransfName] = np.exp2(XData[nodeTransfName]) + 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'): - XData[nodeTransfName] = np.expm1(XData[nodeTransfName]) + 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'): @@ -620,7 +638,7 @@ def executeModel(exeCall, flagEx, nodeTransfName): estimator.fit(XData, yData) yPredict = estimator.predict(XData) yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba') - print(XData) + print('Data set:',XData) num_cores = multiprocessing.cpu_count() inputsSc = ['accuracy','precision_macro','recall_macro'] @@ -636,7 +654,7 @@ def executeModel(exeCall, flagEx, nodeTransfName): 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') + print('Improved!') if (keyFirstTime == False): if ((scoresAct[0]-scoresAct[1]) > (previousState[0]-previousState[1])): @@ -694,15 +712,11 @@ def estimatorFeatureSelection(Data, clf): ImpurityFS = [] RankingFS = [] - rf = RandomForestClassifier(n_estimators = 100, - n_jobs = -1, - random_state = RANDOM_SEED) - rf.fit(Data, yData) - - importances = rf.feature_importances_ + estim = clf.fit(Data, yData) - std = np.std([tree.feature_importances_ for tree in rf.estimators_], - axis=0) + importances = clf.feature_importances_ + # std = np.std([tree.feature_importances_ for tree in estim.feature_importances_], + # axis=0) maxList = max(importances) minList = min(importances) @@ -837,32 +851,36 @@ def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5): 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) + 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) + 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) + 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 = [] @@ -877,204 +895,236 @@ def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5): 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) + 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) + 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) + 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) + 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) + 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) + 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, 2.2250738585072014e-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) + 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) + 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) + 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) + 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, 2.2250738585072014e-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) + 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) + 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() - XDataNumericCopy[i] = np.exp2(XDataNumericCopy[i]) + 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) + 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) + 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() - XDataNumericCopy[i] = np.expm1(XDataNumericCopy[i]) + 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) + 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) + 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) + 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) + 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) + 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) + 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) + 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): - - print(DataRows1) - print(DataRows2) - print(DataRows3) - print(DataRows4) - print(DataRows5) +def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count, flagInf): corrMatrix1 = DataRows1.corr() corrMatrix1 = corrMatrix1.abs() @@ -1129,9 +1179,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, 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): - MI1 = mutual_info_classif(DataRows1, targetRows1Arr) + 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: @@ -1154,9 +1207,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, 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): - MI2 = mutual_info_classif(DataRows2, targetRows2Arr) + 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: @@ -1179,9 +1235,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, 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): - MI3 = mutual_info_classif(DataRows3, targetRows3Arr) + 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: @@ -1204,9 +1263,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, 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): - MI4 = mutual_info_classif(DataRows4, targetRows4Arr) + 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: @@ -1229,9 +1291,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, 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): - MI5 = mutual_info_classif(DataRows5, targetRows5Arr) + 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: @@ -1241,11 +1306,26 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, VIF5 = pd.Series() MI5List = [] - corrMatrixComb1 = corrMatrixComb1.loc[[feature]] - corrMatrixComb2 = corrMatrixComb2.loc[[feature]] - corrMatrixComb3 = corrMatrixComb3.loc[[feature]] - corrMatrixComb4 = corrMatrixComb4.loc[[feature]] - corrMatrixComb5 = corrMatrixComb5.loc[[feature]] + 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) @@ -1362,6 +1442,7 @@ def Seperation(): DataRows4 = XData.iloc[quadrant4, :] DataRows5 = XData.iloc[quadrant5, :] + Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5) corrMatrix1 = DataRows1.corr() @@ -1412,8 +1493,11 @@ def Seperation(): 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) + MI1 = mutual_info_classif(DataRows1, targetRows1Arr, n_neighbors=3, random_state=RANDOM_SEED) MI1List = MI1.tolist() else: MI1List = [] @@ -1435,8 +1519,11 @@ def Seperation(): 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) + MI2 = mutual_info_classif(DataRows2, targetRows2Arr, n_neighbors=3, random_state=RANDOM_SEED) MI2List = MI2.tolist() else: MI2List = [] @@ -1458,8 +1545,11 @@ def Seperation(): 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) + MI3 = mutual_info_classif(DataRows3, targetRows3Arr, n_neighbors=3, random_state=RANDOM_SEED) MI3List = MI3.tolist() else: MI3List = [] @@ -1481,8 +1571,11 @@ def Seperation(): 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) + MI4 = mutual_info_classif(DataRows4, targetRows4Arr, n_neighbors=3, random_state=RANDOM_SEED) MI4List = MI4.tolist() else: MI4List = [] @@ -1504,8 +1597,11 @@ def Seperation(): 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) + MI5 = mutual_info_classif(DataRows5, targetRows5Arr, n_neighbors=3, random_state=RANDOM_SEED) MI5List = MI5.tolist() else: MI5List = []