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