From 585622e6319a39e86b95900e18869167dcc26bea Mon Sep 17 00:00:00 2001 From: Angelos Chatzimparmpas Date: Mon, 27 Jul 2020 13:44:16 +0200 Subject: [PATCH] fixed the CM process --- __pycache__/run.cpython-37.pyc | Bin 28873 -> 29504 bytes .../output.pkl | Bin 0 -> 6352972 bytes .../output.pkl | Bin 1071787 -> 0 bytes .../output.pkl | Bin 0 -> 3173178 bytes .../metadata.json | 1 - .../metadata.json | 2 +- .../metadata.json | 1 + .../metadata.json | 1 - .../output.pkl | Bin 0 -> 3173288 bytes .../output.pkl | Bin 2150308 -> 0 bytes .../output.pkl | Bin 3201140 -> 3201308 bytes .../metadata.json | 1 + .../metadata.json | 1 + .../metadata.json | 1 + .../metadata.json | 1 - .../output.pkl | Bin 1597458 -> 1597458 bytes cachedir/joblib/run/randomSearch/func_code.py | 2 +- frontend/src/components/Ensemble.vue | 2 +- .../src/components/HyperParameterSpace.vue | 2 +- frontend/src/components/Main.vue | 3 +- frontend/src/components/Predictions.vue | 3 +- frontend/src/components/PredictionsCM.vue | 4 +- run.py | 103 +++++++++++++----- 23 files changed, 90 insertions(+), 38 deletions(-) create mode 100644 cachedir/joblib/run/randomSearch/0a7800f040057f3aab13a59ebd153437/output.pkl delete mode 100644 cachedir/joblib/run/randomSearch/15c1a1c6505bfd383b2231a984466199/output.pkl create mode 100644 cachedir/joblib/run/randomSearch/33864c0fe6e12968caff785cfea6870b/output.pkl delete mode 100644 cachedir/joblib/run/randomSearch/37316eae58a8c74b9165423658201b28/metadata.json rename cachedir/joblib/run/randomSearch/{02f4b76834d076f3b2e8a3a6d6a6d0ad => 8dd8c905a5a57cf1d9b467f291dd14b2}/metadata.json (69%) create mode 100644 cachedir/joblib/run/randomSearch/8ee66eaa63ddb9f764dde4060234b1f9/metadata.json delete mode 100644 cachedir/joblib/run/randomSearch/9d85ea1f37edb91533a828737a6caa01/metadata.json create mode 100644 cachedir/joblib/run/randomSearch/a4fb34c288b3df59f6e95b0829e85cf8/output.pkl delete mode 100644 cachedir/joblib/run/randomSearch/b05bc4dd718bb370a35dbc60bdff86aa/output.pkl create mode 100644 cachedir/joblib/run/randomSearch/b7a3520563829a2c45169ede4c18dce6/metadata.json create mode 100644 cachedir/joblib/run/randomSearch/db2d0ed5e07bad06c380972b056d9c63/metadata.json create mode 100644 cachedir/joblib/run/randomSearch/e4dca276cc6a9475d2aee588523b478e/metadata.json delete mode 100644 cachedir/joblib/run/randomSearch/eb45ad56de8e367dade6cb77617a3d36/metadata.json diff --git a/__pycache__/run.cpython-37.pyc b/__pycache__/run.cpython-37.pyc index c4745865b123f4c1cdd2ab76123a43755d3dfd2b..af482ba8bfbf15dcd8dc9ea89698f7937ae3ca86 100644 GIT binary patch delta 10845 zcmd5?3v^t?dA@V^p}P<5uAZya+j>}*ElaX2TQZjQuxx`ZOMXbk^4hX=y>~6IwAz(s zuZ;{>EW{zW1c=CRXhLm}Ap{o^9#x@%76J+7v?VPk2hO40(?WAnsBKKZX_^F+LjQku zwYyr`5OGeFF&p-d{KmLNe^d7NoOiwop@K^al@!;?)Puq?g$g}LE zp|e=g%T~o8r^DMeEXa0v9Pnholc^BaX3lMeq)Rc%`S31)r%*{#B*mnd6^mk3(iNLx zKPAaUibF02Y6(20@GOC+44!hf%6NfPu)?&PGTgSVo}TWY;`N6*gGU3Zo1IN7DmKcB z0{IaGytnU;*gQz;@cW|$XJ1L%PmJ54>vTJ-G;Jf>*tn@!r_<}$H%%)^4|~p3D_($( 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a/cachedir/joblib/run/randomSearch/func_code.py +++ b/cachedir/joblib/run/randomSearch/func_code.py @@ -1,4 +1,4 @@ -# first line: 501 +# first line: 518 @memory.cache def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): diff --git a/frontend/src/components/Ensemble.vue b/frontend/src/components/Ensemble.vue index a6e2fc0..830989b 100644 --- a/frontend/src/components/Ensemble.vue +++ b/frontend/src/components/Ensemble.vue @@ -89,7 +89,7 @@ export default { this.clean(parameters[i]) stringParameters.push(JSON.stringify(parameters[i]).replace(/,/gi, '
      ')) } - + // fix that! var classifiersInfoProcessing = [] for (let i = 0; i < modelId.length; i++) { if (i < 100) { diff --git a/frontend/src/components/HyperParameterSpace.vue b/frontend/src/components/HyperParameterSpace.vue index 8659a71..6662f55 100644 --- a/frontend/src/components/HyperParameterSpace.vue +++ b/frontend/src/components/HyperParameterSpace.vue @@ -73,7 +73,7 @@ export default { this.clean(parameters[i]) stringParameters.push(JSON.stringify(parameters[i]).replace(/,/gi, '
      ')) } - + // fix that! var classifiersInfoProcessing = [] for (let i = 0; i < modelId.length; i++) { if (i < 100) { diff --git a/frontend/src/components/Main.vue b/frontend/src/components/Main.vue index 43d33c5..1bce59b 100755 --- a/frontend/src/components/Main.vue +++ b/frontend/src/components/Main.vue @@ -323,7 +323,7 @@ export default Vue.extend({ } else { EventBus.$emit('emittedEventCallingCrossoverMutation', this.OverviewResults) EventBus.$emit('emittedEventCallingGridCrossoverMutation', this.OverviewResults) - EventBus.$emit('emittedEventCallingGridSelectionGridCrossoverMutation', this.OverviewResults) + EventBus.$emit('emittedEventCallingGridSelectionCrossoverMutation', this.OverviewResults) //this.getFinalResults() } }) @@ -349,7 +349,6 @@ export default Vue.extend({ EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResultsCM) EventBus.$emit('emittedEventCallingGrid', this.OverviewResultsCM) EventBus.$emit('emittedEventCallingGridSelection', this.OverviewResultsCM) - //this.getFinalResults() }) .catch(error => { console.log(error) diff --git a/frontend/src/components/Predictions.vue b/frontend/src/components/Predictions.vue index 5747f07..885297b 100644 --- a/frontend/src/components/Predictions.vue +++ b/frontend/src/components/Predictions.vue @@ -61,6 +61,7 @@ export default { var KNNPred = predictions[0] var LRPred = predictions[1] var PredAver = predictions[2] + console.log(PredAver) var dataAver = [] var dataAverGetResults = [] @@ -124,7 +125,7 @@ export default { } var classStore = [].concat.apply([], classArray); - + console.log(classStore) // === Set up canvas === // var width = 1200, diff --git a/frontend/src/components/PredictionsCM.vue b/frontend/src/components/PredictionsCM.vue index 3a40f80..5ca2474 100644 --- a/frontend/src/components/PredictionsCM.vue +++ b/frontend/src/components/PredictionsCM.vue @@ -431,8 +431,8 @@ export default { EventBus.$on('emittedEventCallingGridCrossoverMutation', data => { this.GetResultsAllCM = data; }) EventBus.$on('emittedEventCallingGridCrossoverMutation', this.Grid) - EventBus.$on('emittedEventCallingGridSelectionGridCrossoverMutation', data => { this.GetResultsSelectionCM = data; }) - EventBus.$on('emittedEventCallingGridSelectionGridCrossoverMutation', this.GridSelection) + EventBus.$on('emittedEventCallingGridSelectionCrossoverMutation', data => { this.GetResultsSelectionCM = data; }) + EventBus.$on('emittedEventCallingGridSelectionCrossoverMutation', this.GridSelection) EventBus.$on('SendSelectedPointsToServerEventCM', data => { this.predictSelectionCM = data; }) EventBus.$on('SendSelectedPointsToServerEventCM', this.GridSelection) diff --git a/run.py b/run.py index a23f385..b139c2c 100644 --- a/run.py +++ b/run.py @@ -76,6 +76,15 @@ def reset(): global yData yData = [] + global addKNN + addKNN = 0 + + global addLR + addLR = 100 + + global countAllModels + countAllModels = 0 + global XDataStored XDataStored = [] global yDataStored @@ -197,6 +206,12 @@ def retrieveFileName(): global detailsParams detailsParams = [] + global addKNN + addKNN = 0 + + global addLR + addLR = 100 + # Initializing models global RetrieveModelsList @@ -475,6 +490,7 @@ def retrieveModel(): global XData global yData global LRModelsCount + global countAllModels # loop through the algorithms global allParametersPerformancePerModel @@ -484,11 +500,12 @@ def retrieveModel(): if (eachAlgor) == 'KNN': clf = KNeighborsClassifier() params = {'n_neighbors': list(range(1, 100)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']} - AlgorithmsIDsEnd = KNNModelsCount + AlgorithmsIDsEnd = countAllModels else: clf = LogisticRegression(random_state=RANDOM_SEED) params = {'C': list(np.arange(1,100,1)), 'max_iter': list(np.arange(50,500,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']} - AlgorithmsIDsEnd = LRModelsCount + AlgorithmsIDsEnd = countAllModels + countAllModels = countAllModels + 100 allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd) HistoryPreservation = allParametersPerformancePerModel.copy() # call the function that sends the results to the frontend @@ -652,6 +669,7 @@ def PreprocessingParam(): dfLR = dfLR.T df_params = pd.concat([dfKNN, dfLR]) + df_params = df_params.reset_index(drop=True) return df_params def PreprocessingParamSep(): @@ -788,7 +806,6 @@ def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel) Results.append(json.dumps(ModelSpaceTSNE)) Results.append(json.dumps(ModelSpaceUMAP)) Results.append(json.dumps(PredictionProbSel)) - print('mpike') return Results @@ -806,6 +823,9 @@ def CrossoverMutateFun(): global XData global yData global LRModelsCount + global addKNN + global addLR + global countAllModels # loop through the algorithms global allParametersPerfCrossMutr @@ -819,17 +839,16 @@ def CrossoverMutateFun(): countLR = 0 setMaxLoopValue = 5 paramAllAlgs = PreprocessingParam() + KNNIntIndex = [] LRIntIndex = [] localCrossMutr = [] allParametersPerfCrossMutrKNNC = [] - while countKNN < setMaxLoopValue: for dr in KNNIDs: KNNIntIndex.append(int(re.findall('\d+', dr)[0])) KNNPickPair = random.sample(KNNIntIndex,2) - pairDF = paramAllAlgs.iloc[KNNPickPair] crossoverDF = pd.DataFrame() for column in pairDF: @@ -843,11 +862,13 @@ def CrossoverMutateFun(): else: clf = KNeighborsClassifier() params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]} - AlgorithmsIDsEnd = 200 + countKNN + AlgorithmsIDsEnd = countAllModels + countKNN localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN', AlgorithmsIDsEnd) countKNN += 1 crossoverDF = pd.DataFrame() + countAllModels = countAllModels + 5 + for loop in range(setMaxLoopValue - 1): localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) @@ -888,11 +909,13 @@ def CrossoverMutateFun(): else: clf = KNeighborsClassifier() params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]} - AlgorithmsIDsEnd = 205 + countKNN + AlgorithmsIDsEnd = countAllModels + countKNN localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN', AlgorithmsIDsEnd) countKNN += 1 crossoverDF = pd.DataFrame() + countAllModels = countAllModels + 5 + for loop in range(setMaxLoopValue - 1): localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) @@ -927,11 +950,13 @@ def CrossoverMutateFun(): else: clf = LogisticRegression(random_state=RANDOM_SEED) params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]} - AlgorithmsIDsEnd = 210 + countLR + AlgorithmsIDsEnd = countAllModels + countLR localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR', AlgorithmsIDsEnd) countLR += 1 crossoverDF = pd.DataFrame() + countAllModels = countAllModels + 5 + for loop in range(setMaxLoopValue - 1): localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) @@ -972,11 +997,13 @@ def CrossoverMutateFun(): else: clf = LogisticRegression(random_state=RANDOM_SEED) params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]} - AlgorithmsIDsEnd = 215 + countLR + AlgorithmsIDsEnd = countAllModels + countLR localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR', AlgorithmsIDsEnd) countLR += 1 crossoverDF = pd.DataFrame() + countAllModels = countAllModels + 5 + for loop in range(setMaxLoopValue - 1): localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4] localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True) @@ -990,15 +1017,15 @@ def CrossoverMutateFun(): HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrLRM + localCrossMutr.clear() + allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNC + allParametersPerfCrossMutrKNNM + allParametersPerfCrossMutrLRC + allParametersPerfCrossMutrLRM - allParametersPerformancePerModel[0] = allParametersPerformancePerModel[0] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0] - - allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNC[1]], ignore_index=True) - allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNM[1]], ignore_index=True) - - allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNC[2]], ignore_index=True) - allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNM[2]], ignore_index=True) + allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0] + allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNC[1]], ignore_index=True) + allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNM[1]], ignore_index=True) + allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrKNNC[2]], ignore_index=True) + allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrKNNM[2]], ignore_index=True) allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNC[3]], ignore_index=True) allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNM[3]], ignore_index=True) @@ -1008,14 +1035,15 @@ def CrossoverMutateFun(): allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrLRC[1]], ignore_index=True) allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrLRM[1]], ignore_index=True) - allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrLRC[2]], ignore_index=True) allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrLRM[2]], ignore_index=True) allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRC[3]], ignore_index=True) allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRM[3]], ignore_index=True) - print(allParametersPerformancePerModel[7]) + addKNN = addLR + + addLR = addLR + 10 # KNNIntIndex = [] # for dr in KNNIDs: @@ -1038,7 +1066,6 @@ def CrossoverMutateFun(): return 'Everything Okay' def crossoverMutation(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): - search = GridSearchCV( estimator=clf, param_grid=params, cv=crossValidation, refit='accuracy', scoring=scoring, verbose=0, n_jobs=-1) @@ -1134,7 +1161,6 @@ def PreprocessingIDsCM(): dicLRM = allParametersPerfCrossMutr[12] df_concatIDs = dicKNNC + dicKNNM + dicLRC + dicLRM - return df_concatIDs def PreprocessingMetricsCM(): @@ -1163,13 +1189,28 @@ def PreprocessingPredCM(): dfLRC = pd.DataFrame.from_dict(dicLRC) dfLRM = pd.DataFrame.from_dict(dicLRM) + dfKNN = pd.concat([dfKNNC, dfKNNM]) + + dfLR = pd.concat([dfLRC, dfLRM]) + df_concatProbs = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM]) + + predictionsKNN = [] + for column, content in dfKNN.items(): + el = [sum(x)/len(x) for x in zip(*content)] + predictionsKNN.append(el) + + predictionsLR = [] + for column, content in dfLR.items(): + el = [sum(x)/len(x) for x in zip(*content)] + predictionsLR.append(el) + predictions = [] for column, content in df_concatProbs.items(): el = [sum(x)/len(x) for x in zip(*content)] predictions.append(el) - return predictions + return [predictionsKNN, predictionsLR, predictions] def PreprocessingParamCM(): dicKNNC = allParametersPerfCrossMutr[1] @@ -1198,6 +1239,7 @@ def PreprocessingParamCM(): dfLRM = dfLRM.T df_params = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM]) + df_params = df_params.reset_index(drop=True) return df_params def PreprocessingParamSepCM(): @@ -1326,30 +1368,37 @@ def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,Predict ResultsCM.append(json.dumps(ModelSpaceUMAPCM)) ResultsCM.append(json.dumps(PredictionProbSel)) - return Results + return ResultsCM def PreprocessingPredSel(SelectedIDs): + global addKNN + global addLR + numberIDKNN = [] numberIDLR = [] + print(SelectedIDs) for el in SelectedIDs: match = re.match(r"([a-z]+)([0-9]+)", el, re.I) if match: items = match.groups() if (items[0] == 'KNN'): - numberIDKNN.append(int(items[1])) + numberIDKNN.append(int(items[1]) - addKNN) else: - numberIDLR.append(int(items[1]) - 100) - + numberIDLR.append(int(items[1]) - addLR) + print(numberIDKNN) dicKNN = allParametersPerformancePerModel[3] dicLR = allParametersPerformancePerModel[7] dfKNN = pd.DataFrame.from_dict(dicKNN) + print(dfKNN) dfKNN = dfKNN.loc[numberIDKNN] dfLR = pd.DataFrame.from_dict(dicLR) dfLR = dfLR.loc[numberIDLR] - dfLR.index += 100 + print(dfLR) + dfLR.index += addKNN df_concatProbs = pd.concat([dfKNN, dfLR]) + print(df_concatProbs) predictionsKNN = [] for column, content in dfKNN.items(): @@ -1360,6 +1409,7 @@ def PreprocessingPredSel(SelectedIDs): for column, content in dfLR.items(): el = [sum(x)/len(x) for x in zip(*content)] predictionsLR.append(el) + predictions = [] for column, content in df_concatProbs.items(): el = [sum(x)/len(x) for x in zip(*content)] @@ -1375,6 +1425,7 @@ def RetrieveSelIDsPredict(): RetrieveIDsSelection = request.get_data().decode('utf8').replace("'", '"') RetrieveIDsSelection = json.loads(RetrieveIDsSelection) RetrieveIDsSelection = RetrieveIDsSelection['predictSelectionIDs'] + ResultsSelPred = PreprocessingPredSel(RetrieveIDsSelection) return 'Everything Okay'

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