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@ -29,11 +29,13 @@ from mlxtend.feature_selection import ColumnSelector |
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from sklearn.metrics import matthews_corrcoef |
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from sklearn.metrics import log_loss |
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from imblearn.metrics import geometric_mean_score |
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from sklearn.metrics import classification_report, accuracy_score, make_scorer, confusion_matrix |
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from sklearn.manifold import MDS |
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from sklearn.manifold import TSNE |
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import umap |
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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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@ -48,6 +50,11 @@ cors = CORS(app, resources={r"/data/*": {"origins": "*"}}) |
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@app.route('/data/Reset', methods=["GET", "POST"]) |
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def reset(): |
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global PerClassResultsClass0 |
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PerClassResultsClass0 = [] |
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global PerClassResultsClass1 |
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PerClassResultsClass1 = [] |
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global Results |
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Results = [] |
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global ResultsCM |
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@ -61,15 +68,9 @@ def reset(): |
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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 dataSpacePointsIDs |
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dataSpacePointsIDs = [] |
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global previousStateActive |
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previousStateActive = [] |
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global RANDOM_SEED |
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RANDOM_SEED = 42 |
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@ -177,6 +178,10 @@ def reset(): |
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global target_namesLoc |
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target_namesLoc = [] |
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global names_labels |
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names_labels = [] |
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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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@ -191,9 +196,6 @@ def retrieveFileName(): |
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fileName = request.get_data().decode('utf8').replace("'", '"') |
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data = json.loads(fileName) |
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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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@ -233,9 +235,6 @@ def retrieveFileName(): |
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global previousState |
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previousState = [] |
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global previousStateActive |
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previousStateActive = [] |
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global yData |
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yData = [] |
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@ -331,11 +330,16 @@ def retrieveFileName(): |
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target_namesLoc = [] |
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global names_labels |
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names_labels = [] |
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DataRawLength = -1 |
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DataRawLengthTest = -1 |
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if data['fileName'] == 'HeartC': |
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CollectionDB = mongo.db.HeartC.find() |
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names_labels.append('Healthy') |
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names_labels.append('Diseased') |
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elif data['fileName'] == 'StanceC': |
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StanceTest = True |
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CollectionDB = mongo.db.StanceC.find() |
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@ -1027,9 +1031,6 @@ def EnsembleModel (Models, keyRetrieved): |
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global XDataTest, yDataTest |
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global scores |
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global previousState |
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global previousStateActive |
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global keySpec |
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global keySpecInternal |
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global crossValidation |
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global keyData |
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scores = [] |
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@ -1049,169 +1050,193 @@ def EnsembleModel (Models, keyRetrieved): |
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global numberIDRFGlob |
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global numberIDGradBGlob |
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all_classifiers = [] |
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columnsInit = [] |
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columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData] |
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temp = allParametersPerformancePerModel[1] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamKNN = pd.DataFrame.from_dict(tempDic) |
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dfParamKNNFilt = dfParamKNN.iloc[:,0] |
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for eachelem in numberIDKNNGlob: |
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arg = dfParamKNNFilt[eachelem] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), KNeighborsClassifier().set_params(**arg))) |
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temp = allParametersPerformancePerModel[5] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamLR = pd.DataFrame.from_dict(tempDic) |
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dfParamLRFilt = dfParamLR.iloc[:,0] |
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for eachelem in numberIDLRGlob: |
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arg = dfParamLRFilt[eachelem-LRModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LogisticRegression(random_state=RANDOM_SEED).set_params(**arg))) |
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temp = allParametersPerformancePerModel[9] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamMLP = pd.DataFrame.from_dict(tempDic) |
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dfParamMLPFilt = dfParamMLP.iloc[:,0] |
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for eachelem in numberIDMLPGlob: |
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arg = dfParamMLPFilt[eachelem-MLPModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), MLPClassifier(random_state=RANDOM_SEED).set_params(**arg))) |
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temp = allParametersPerformancePerModel[13] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamRF = pd.DataFrame.from_dict(tempDic) |
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dfParamRFFilt = dfParamRF.iloc[:,0] |
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for eachelem in numberIDRFGlob: |
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arg = dfParamRFFilt[eachelem-RFModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier(random_state=RANDOM_SEED).set_params(**arg))) |
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temp = allParametersPerformancePerModel[17] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamGradB = pd.DataFrame.from_dict(tempDic) |
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dfParamGradBFilt = dfParamGradB.iloc[:,0] |
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for eachelem in numberIDGradBGlob: |
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arg = dfParamGradBFilt[eachelem-GradBModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GradientBoostingClassifier(random_state=RANDOM_SEED).set_params(**arg))) |
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global sclf |
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sclf = 0 |
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sclf = EnsembleVoteClassifier(clfs=all_classifiers, |
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voting='soft') |
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global PerClassResultsClass0 |
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PerClassResultsClass0 = [] |
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global PerClassResultsClass1 |
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PerClassResultsClass1 = [] |
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nested_score = model_selection.cross_val_score(sclf, X=XData, y=yData, cv=crossValidation, scoring=make_scorer(classification_report_with_accuracy_score)) |
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PerClassResultsClass0Con = pd.concat(PerClassResultsClass0, axis=1, sort=False) |
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PerClassResultsClass1Con = pd.concat(PerClassResultsClass1, axis=1, sort=False) |
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averageClass0 = PerClassResultsClass0Con.mean(axis=1) |
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averageClass1 = PerClassResultsClass1Con.mean(axis=1) |
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y_pred = cross_val_predict(sclf, XData, yData, cv=crossValidation) |
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conf_mat = confusion_matrix(yData, y_pred) |
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cm = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis] |
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cm.diagonal() |
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if (keyRetrieved == 0): |
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all_classifiers = [] |
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columnsInit = [] |
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columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData] |
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temp = allParametersPerformancePerModel[1] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamKNN = pd.DataFrame.from_dict(tempDic) |
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dfParamKNNFilt = dfParamKNN.iloc[:,0] |
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for eachelem in numberIDKNNGlob: |
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arg = dfParamKNNFilt[eachelem] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), KNeighborsClassifier().set_params(**arg))) |
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temp = allParametersPerformancePerModel[5] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamLR = pd.DataFrame.from_dict(tempDic) |
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dfParamLRFilt = dfParamLR.iloc[:,0] |
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for eachelem in numberIDLRGlob: |
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arg = dfParamLRFilt[eachelem-LRModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LogisticRegression(random_state=RANDOM_SEED).set_params(**arg))) |
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temp = allParametersPerformancePerModel[9] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamMLP = pd.DataFrame.from_dict(tempDic) |
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dfParamMLPFilt = dfParamMLP.iloc[:,0] |
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for eachelem in numberIDMLPGlob: |
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arg = dfParamMLPFilt[eachelem-MLPModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), MLPClassifier(random_state=RANDOM_SEED).set_params(**arg))) |
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temp = allParametersPerformancePerModel[13] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamRF = pd.DataFrame.from_dict(tempDic) |
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dfParamRFFilt = dfParamRF.iloc[:,0] |
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for eachelem in numberIDRFGlob: |
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arg = dfParamRFFilt[eachelem-RFModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier(random_state=RANDOM_SEED).set_params(**arg))) |
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temp = allParametersPerformancePerModel[17] |
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temp = temp['params'] |
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temp = {int(k):v for k,v in temp.items()} |
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tempDic = { |
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'params': temp |
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} |
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dfParamGradB = pd.DataFrame.from_dict(tempDic) |
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dfParamGradBFilt = dfParamGradB.iloc[:,0] |
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for eachelem in numberIDGradBGlob: |
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arg = dfParamGradBFilt[eachelem-GradBModelsCount] |
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all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GradientBoostingClassifier(random_state=RANDOM_SEED).set_params(**arg))) |
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global sclf |
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sclf = 0 |
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sclf = EnsembleVoteClassifier(clfs=all_classifiers, |
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voting='soft') |
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keySpec = 0 |
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if (keySpec == 0 or keySpec == 1): |
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num_cores = multiprocessing.cpu_count() |
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inputsSc = ['accuracy','precision','recall','f1'] |
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flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc)) |
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scores = [item for sublist in flat_results for item in sublist] |
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if (keySpec == 0): |
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previousState = [] |
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scores.append(cm[0][0]) |
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scores.append(cm[1][1]) |
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scores.append(cm[0][0]) |
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scores.append(cm[1][1]) |
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scores.append(averageClass0.precision) |
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scores.append(averageClass1.precision) |
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scores.append(averageClass0.precision) |
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scores.append(averageClass1.precision) |
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scores.append(averageClass0.recall) |
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scores.append(averageClass1.recall) |
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scores.append(averageClass0.recall) |
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scores.append(averageClass1.recall) |
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scores.append(averageClass0['f1-score']) |
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scores.append(averageClass1['f1-score']) |
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scores.append(averageClass0['f1-score']) |
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scores.append(averageClass1['f1-score']) |
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previousState.append(scores[0]) |
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previousState.append(scores[1]) |
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previousState.append(scores[3]) |
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previousState.append(scores[4]) |
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previousState.append(scores[5]) |
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previousState.append(scores[7]) |
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previousStateActive = [] |
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previousStateActive.append(scores[0]) |
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previousStateActive.append(scores[2]) |
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previousStateActive.append(scores[4]) |
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previousStateActive.append(scores[6]) |
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elif (keySpec == 1): |
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if (keySpecInternal == 1): |
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previousStateActive = [] |
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previousStateActive.append(scores[0]) |
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previousStateActive.append(scores[2]) |
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previousStateActive.append(scores[4]) |
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previousStateActive.append(scores[6]) |
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else: |
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previousStateActive = [] |
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previousStateActive.append(scores[0]) |
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previousStateActive.append(scores[2]) |
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previousStateActive.append(scores[4]) |
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previousStateActive.append(scores[6]) |
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previousState = [] |
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previousState.append(scores[1]) |
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previousState.append(scores[3]) |
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previousState.append(scores[5]) |
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previousState.append(scores[7]) |
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previousState.append(scores[8]) |
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previousState.append(scores[9]) |
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previousState.append(scores[12]) |
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previousState.append(scores[13]) |
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else: |
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scores = [] |
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previousState = [] |
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scores.append(previousStateActive[0]) |
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scores.append(previousStateActive[0]) |
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previousState.append(previousStateActive[0]) |
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scores.append(previousStateActive[2]) |
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scores.append(previousStateActive[2]) |
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previousState.append(previousStateActive[2]) |
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scores.append(previousStateActive[4]) |
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scores.append(previousStateActive[4]) |
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previousState.append(previousStateActive[4]) |
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scores.append(previousStateActive[6]) |
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scores.append(previousStateActive[6]) |
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previousState.append(previousStateActive[6]) |
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print(scores) |
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scores.append(cm[0][0]) |
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scores.append(cm[1][1]) |
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if (cm[0][0] > previousState[0]): |
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scores.append(cm[0][0]) |
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previousState[0] = cm[0][0] |
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else: |
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scores.append(previousState[0]) |
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if (cm[1][1] > previousState[1]): |
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scores.append(cm[1][1]) |
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previousState[1] = cm[1][1] |
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else: |
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scores.append(previousState[1]) |
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scores.append(averageClass0.precision) |
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scores.append(averageClass1.precision) |
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if (averageClass0.precision > previousState[2]): |
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scores.append(averageClass0.precision) |
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previousState[2] = averageClass0.precision |
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else: |
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scores.append(previousState[2]) |
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if (averageClass1.precision > previousState[3]): |
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scores.append(averageClass1.precision) |
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previousState[3] = averageClass1.precision |
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else: |
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scores.append(previousState[3]) |
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scores.append(averageClass0.recall) |
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scores.append(averageClass1.recall) |
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if (averageClass0.recall > previousState[4]): |
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scores.append(averageClass0.recall) |
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previousState[4] = averageClass0.recall |
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else: |
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scores.append(previousState[4]) |
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if (averageClass1.recall > previousState[5]): |
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scores.append(averageClass1.recall) |
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previousState[5] = averageClass1.recall |
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else: |
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scores.append(previousState[5]) |
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scores.append(averageClass0['f1-score']) |
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scores.append(averageClass1['f1-score']) |
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if (averageClass0['f1-score'] > previousState[6]): |
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scores.append(averageClass0['f1-score']) |
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previousState[6] = averageClass0['f1-score'] |
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else: |
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scores.append(previousState[6]) |
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if (averageClass1['f1-score'] > previousState[7]): |
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scores.append(averageClass1['f1-score']) |
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previousState[7] = averageClass1['f1-score'] |
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else: |
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scores.append(previousState[7]) |
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return 'Okay' |
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def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop): |
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scoresLoc = [] |
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if (keySpec == 0): |
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temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1) |
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scoresLoc.append(temp.mean()) |
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if (keyData == 1): |
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if (loop == 0): |
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scoresLoc.append(previousStateLoc[0]) |
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elif (loop == 1): |
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scoresLoc.append(previousStateLoc[1]) |
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elif (loop == 2): |
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scoresLoc.append(previousStateLoc[2]) |
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else: |
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scoresLoc.append(previousStateLoc[3]) |
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else: |
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scoresLoc.append(temp.mean()) |
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else: |
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if (keySpecInternal == 1): |
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temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1) |
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scoresLoc.append(temp.mean()) |
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if (loop == 0): |
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scoresLoc.append(previousStateLoc[0]) |
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elif (loop == 1): |
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scoresLoc.append(previousStateLoc[1]) |
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elif (loop == 2): |
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scoresLoc.append(previousStateLoc[2]) |
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else: |
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scoresLoc.append(previousStateLoc[3]) |
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else: |
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temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1) |
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scoresLoc.append(temp.mean()) |
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scoresLoc.append(temp.mean()) |
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return scoresLoc |
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# Sending the final results to be visualized as a line plot |
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@app.route('/data/SendFinalResultsBacktoVisualize', methods=["GET", "POST"]) |
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def SendToPlotFinalResults(): |
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global scores |
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response = { |
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'FinalResults': scores |
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} |
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return jsonify(response) |
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def classification_report_with_accuracy_score(y_true, y_pred): |
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global PerClassResultsClass0 |
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global PerClassResultsClass1 |
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PerClassResultsLocal = pd.DataFrame.from_dict(classification_report(y_true, y_pred, output_dict=True)) |
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Filter_PerClassResultsLocal0 = PerClassResultsLocal['0'] |
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Filter_PerClassResultsLocal0 = Filter_PerClassResultsLocal0[:-1] |
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Filter_PerClassResultsLocal1 = PerClassResultsLocal['1'] |
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Filter_PerClassResultsLocal1 = Filter_PerClassResultsLocal1[:-1] |
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PerClassResultsClass0.append(Filter_PerClassResultsLocal0) |
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PerClassResultsClass1.append(Filter_PerClassResultsLocal1) |
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|
return accuracy_score(y_true, y_pred) # return accuracy score |
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|
def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel): |
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|
global Results |
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|
global AllTargets |
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|
global names_labels |
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|
Results = [] |
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|
parametersGen = PreprocessingParam() |
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|
|
@ -1237,6 +1262,7 @@ def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel) |
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Results.append(json.dumps(ModelSpaceTSNE)) |
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|
Results.append(json.dumps(ModelSpaceUMAP)) |
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|
Results.append(json.dumps(PredictionProbSel)) |
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|
Results.append(json.dumps(names_labels)) |
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return Results |
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|
|
@ -2212,6 +2238,7 @@ def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,Predict |
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|
|
ResultsCM.append(json.dumps(ModelSpaceTSNECM)) |
|
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|
ResultsCM.append(json.dumps(ModelSpaceUMAPCM)) |
|
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|
|
ResultsCM.append(json.dumps(PredictionProbSel)) |
|
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|
ResultsCM.append(json.dumps(names_labels)) |
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|
return ResultsCM |
|
|
|
|
|
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|
|
|
@ -2427,4 +2454,15 @@ def SendPredictSelEnsem(): |
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|
|
response = { |
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|
'PredictSelEnsem': ResultsSelPredEnsem |
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|
|
|
} |
|
|
|
|
return jsonify(response) |
|
|
|
|
return jsonify(response) |
|
|
|
|
|
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization']) |
|
|
|
|
@app.route('/data/ServerRequestSelPoin', methods=["GET", "POST"]) |
|
|
|
|
def RetrieveSelClassifiersID(): |
|
|
|
|
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"') |
|
|
|
|
#ComputeMetricsForSel(ClassifierIDsList) |
|
|
|
|
ClassifierIDCleaned = json.loads(ClassifierIDsList) |
|
|
|
|
|
|
|
|
|
EnsembleModel(ClassifierIDsList, 1) |
|
|
|
|
|
|
|
|
|
return 'Everything Okay' |
Angelos Chatzimparmpas
4 years ago