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--git a/cachedir/joblib/run/GridSearchForModels/ab217966ac94ecfcb1f16130f136aadd/metadata.json b/cachedir/joblib/run/GridSearchForModels/ab217966ac94ecfcb1f16130f136aadd/metadata.json new file mode 100644 index 000000000..e156448c0 --- /dev/null +++ b/cachedir/joblib/run/GridSearchForModels/ab217966ac94ecfcb1f16130f136aadd/metadata.json @@ -0,0 +1 @@ +{"duration": 316.6607279777527, "input_args": {"XData": " sepal_l sepal_w petal_l petal_w\n0 6.3 3.3 6.0 2.5\n1 7.1 3.0 5.9 2.1\n2 5.8 2.7 5.1 1.9\n3 6.3 2.9 5.6 1.8\n4 7.6 3.0 6.6 2.1\n.. ... ... ... ...\n145 5.1 3.8 1.6 0.2\n146 5.0 3.5 1.6 0.6\n147 5.1 3.4 1.5 0.2\n148 4.6 3.2 1.4 0.2\n149 4.8 3.0 1.4 0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=None, n_neighbors=24, p=2,\n weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}} \ No newline at end of file diff --git a/cachedir/joblib/run/GridSearchForModels/ac4f6dc4ccc2d978f1c3dd9edd67372a/output.pkl b/cachedir/joblib/run/GridSearchForModels/ac4f6dc4ccc2d978f1c3dd9edd67372a/output.pkl new file mode 100644 index 000000000..f60e944f5 Binary files /dev/null and b/cachedir/joblib/run/GridSearchForModels/ac4f6dc4ccc2d978f1c3dd9edd67372a/output.pkl differ diff --git a/cachedir/joblib/run/GridSearchForModels/ad0a720d75812a4bedd57fc40900baac/metadata.json b/cachedir/joblib/run/GridSearchForModels/ad0a720d75812a4bedd57fc40900baac/metadata.json new file mode 100644 index 000000000..9a0776ffd --- /dev/null +++ b/cachedir/joblib/run/GridSearchForModels/ad0a720d75812a4bedd57fc40900baac/metadata.json @@ -0,0 +1 @@ +{"duration": 703.3166279792786, "input_args": {"XData": " sepal_l sepal_w petal_l petal_w\n0 6.3 3.3 6.0 2.5\n1 7.1 3.0 5.9 2.1\n2 5.8 2.7 5.1 1.9\n3 6.3 2.9 5.6 1.8\n4 7.6 3.0 6.6 2.1\n.. ... ... ... ...\n145 5.1 3.8 1.6 0.2\n146 5.0 3.5 1.6 0.6\n147 5.1 3.4 1.5 0.2\n148 4.6 3.2 1.4 0.2\n149 4.8 3.0 1.4 0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n criterion='entropy', max_depth=None, max_features='auto',\n max_leaf_nodes=None, max_samples=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, n_estimators=139,\n n_jobs=None, oob_score=False, random_state=None,\n verbose=0, warm_start=False)", "params": "{'n_estimators': [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "2876"}} \ No newline at end of file diff --git a/cachedir/joblib/run/GridSearchForModels/bbe4c1e011dd495e308dfa7307fddae5/output.pkl b/cachedir/joblib/run/GridSearchForModels/bbe4c1e011dd495e308dfa7307fddae5/output.pkl new file mode 100644 index 000000000..8b6813c3f Binary files /dev/null and b/cachedir/joblib/run/GridSearchForModels/bbe4c1e011dd495e308dfa7307fddae5/output.pkl differ diff --git a/cachedir/joblib/run/GridSearchForModels/31fad5c3f9344179dd43d9923faff1f5/output.pkl b/cachedir/joblib/run/GridSearchForModels/c50ec33a65037c5e16dedd3fb0a5c417/output.pkl similarity index 53% rename from cachedir/joblib/run/GridSearchForModels/31fad5c3f9344179dd43d9923faff1f5/output.pkl rename to cachedir/joblib/run/GridSearchForModels/c50ec33a65037c5e16dedd3fb0a5c417/output.pkl index 0e4376002..f896beb4e 100644 Binary files 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b/cachedir/joblib/run/GridSearchForModels/ee3fd6e8cf51a42bf549fd674a22ea3c/output.pkl new file mode 100644 index 000000000..e4b664ea6 Binary files /dev/null and b/cachedir/joblib/run/GridSearchForModels/ee3fd6e8cf51a42bf549fd674a22ea3c/output.pkl differ diff --git a/cachedir/joblib/run/GridSearchForModels/func_code.py b/cachedir/joblib/run/GridSearchForModels/func_code.py index 826154555..380d69acb 100644 --- a/cachedir/joblib/run/GridSearchForModels/func_code.py +++ b/cachedir/joblib/run/GridSearchForModels/func_code.py @@ -1,4 +1,4 @@ -# first line: 466 +# first line: 510 @memory.cache def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): @@ -78,6 +78,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): resultsMacroBeta2 = [] resultsWeightedBeta2 = [] resultsLogLoss = [] + resultsLogLossFinal = [] loop = 10 @@ -117,9 +118,13 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): resultsMicroBeta2.append(fbeta_score(yData, yPredict, average='micro', beta=2)) resultsMacroBeta2.append(fbeta_score(yData, yPredict, average='macro', beta=2)) resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2)) + + resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True)) - resultsLogLoss.append(log_loss(yData, yPredict, normalize = True)) - + maxLog = max(resultsLogLoss) + minLog = min(resultsLogLoss) + for each in resultsLogLoss: + resultsLogLossFinal.append((each-minLog)/(maxLog-minLog)) metrics.insert(loop,'geometric_mean_score_micro',resultsMicro) metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro) @@ -139,7 +144,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): metrics.insert(loop+11,'f2_macro',resultsMacroBeta2) metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2) - metrics.insert(loop+13,'log_loss',resultsLogLoss) + metrics.insert(loop+13,'log_loss',resultsLogLossFinal) perModelProbPandas = pd.DataFrame(perModelProb) perModelProbPandas = perModelProbPandas.to_json() diff --git a/frontend/src/components/AlgorithmHyperParam.vue b/frontend/src/components/AlgorithmHyperParam.vue index 315b5c0ea..a56d1a949 100644 --- a/frontend/src/components/AlgorithmHyperParam.vue +++ b/frontend/src/components/AlgorithmHyperParam.vue @@ -35,7 +35,7 @@ export default { PCPView () { d3.selectAll("#PCP > *").remove(); if (this.selAlgorithm != '') { - var colors = ['#8dd3c7','#8da0cb'] + var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928'] var colorGiv = 0 var factorsLocal = this.factors @@ -47,21 +47,6 @@ export default { var Mc1 = [] const performanceAlg1 = JSON.parse(this.ModelsPerformance[6]) - var max - var min - - for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) { - if (j == 0) { - max = Object.values(performanceAlg1['log_loss'])[j] - min = Object.values(performanceAlg1['log_loss'])[j] - } - if (Object.values(performanceAlg1['log_loss'])[j] > max) { - max = Object.values(performanceAlg1['log_loss'])[j] - } - if (Object.values(performanceAlg1['log_loss'])[j] < min) { - min = Object.values(performanceAlg1['log_loss'])[j] - } - } for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) { let sum @@ -69,7 +54,7 @@ export default { + (factorsLocal[5] * Object.values(performanceAlg1['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg1['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg1['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg1['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg1['mean_test_recall_micro'])[j]) + (factorsLocal[10] * Object.values(performanceAlg1['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg1['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg1['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg1['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg1['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg1['f1_micro'])[j]) + (factorsLocal[16] * Object.values(performanceAlg1['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg1['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg1['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg1['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg1['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg1['matthews_corrcoef'])[j]) - + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg1['log_loss'])[j])/(max - min)))) + + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg1['log_loss'])[j])) Mc1.push((sum/divide)*100) } @@ -81,7 +66,7 @@ export default { + (factorsLocal[5] * Object.values(performanceAlg2['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg2['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg2['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg2['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg2['mean_test_recall_micro'])[j]) + (factorsLocal[10] * Object.values(performanceAlg2['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg2['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg2['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg2['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg2['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg2['f1_micro'])[j]) + (factorsLocal[16] * Object.values(performanceAlg2['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg2['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg2['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg2['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg2['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg2['matthews_corrcoef'])[j]) - + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg2['log_loss'])[j])/(max - min)))) + + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg2['log_loss'])[j])) Mc2.push((sum2/divide)*100) } @@ -103,10 +88,10 @@ export default { for (var i = 0; i < valuesPerf.length; i++) { if (this.selAlgorithm === 'KNN') { // There is a problem here! - newObjectsParams.push({model: i,'perf_metrics': Mc1[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights}) + newObjectsParams.push({model: i,'performance (%)': Mc1[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights}) ArrayCombined[i] = newObjectsParams[i] } else { - newObjectsParams2.push({model: this.KNNModels + i,'perf_metrics': Mc2[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion}) + newObjectsParams2.push({model: this.KNNModels + i,'performance (%)': Mc2[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion}) ArrayCombined[i] = newObjectsParams2[i] } } diff --git a/frontend/src/components/Algorithms.vue b/frontend/src/components/Algorithms.vue index e9d8f8b0a..b6e4a6cae 100644 --- a/frontend/src/components/Algorithms.vue +++ b/frontend/src/components/Algorithms.vue @@ -58,24 +58,8 @@ export default { divide = element + divide }); - var max - var min var Mc1 = [] const performanceAlg1 = JSON.parse(this.PerformanceAllModels[6]) - console.log(performanceAlg1) - - for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) { - if (j == 0) { - max = Object.values(performanceAlg1['log_loss'])[j] - min = Object.values(performanceAlg1['log_loss'])[j] - } - if (Object.values(performanceAlg1['log_loss'])[j] > max) { - max = Object.values(performanceAlg1['log_loss'])[j] - } - if (Object.values(performanceAlg1['log_loss'])[j] < min) { - min = Object.values(performanceAlg1['log_loss'])[j] - } - } for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) { let sum @@ -83,7 +67,7 @@ export default { + (factorsLocal[5] * Object.values(performanceAlg1['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg1['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg1['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg1['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg1['mean_test_recall_micro'])[j]) + (factorsLocal[10] * Object.values(performanceAlg1['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg1['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg1['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg1['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg1['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg1['f1_micro'])[j]) + (factorsLocal[16] * Object.values(performanceAlg1['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg1['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg1['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg1['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg1['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg1['matthews_corrcoef'])[j]) - + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg1['log_loss'])[j])/(max - min)))) + + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg1['log_loss'])[j])) Mc1.push((sum/divide)*100) } @@ -95,7 +79,7 @@ export default { + (factorsLocal[5] * Object.values(performanceAlg2['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg2['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg2['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg2['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg2['mean_test_recall_micro'])[j]) + (factorsLocal[10] * Object.values(performanceAlg2['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg2['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg2['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg2['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg2['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg2['f1_micro'])[j]) + (factorsLocal[16] * Object.values(performanceAlg2['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg2['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg2['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg2['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg2['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg2['matthews_corrcoef'])[j]) - + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg2['log_loss'])[j])/(max - min)))) + + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg2['log_loss'])[j])) Mc2.push((sum2/divide)*100) } @@ -134,7 +118,7 @@ export default { this.chart('#exploding_boxplot') // colorscale - const previousColor = ['#8dd3c7','#8da0cb'] + const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928'] // check for brushing var el = document.getElementsByClassName('d3-exploding-boxplot boxcontent') var overall = document.getElementsByClassName('overall') @@ -199,7 +183,7 @@ export default { var limiter = this.chart.returnBrush() var algorithm = [] - const previousColor = ['#8dd3c7','#8da0cb'] + const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928'] var modelsActive = [] for (var j = 0; j < this.AllAlgorithms.length; j++) { algorithm = [] @@ -267,7 +251,7 @@ export default { } else { var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF') } - const previousColor = ['#8dd3c7','#8da0cb'] + const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928'] var modelsActive = [] for (let j = 0; j < this.brushedBoxPl.length; j++) { modelsActive.push(this.brushedBoxPl[j].model) diff --git a/frontend/src/components/BarChart.vue b/frontend/src/components/BarChart.vue index b580cfc17..7792d3794 100644 --- a/frontend/src/components/BarChart.vue +++ b/frontend/src/components/BarChart.vue @@ -20,7 +20,7 @@ export default { modelsSelectedinBar: [], factors: [1,1,1,1,1], KNNModels: 576, //KNN models, - colorsValues: ['#6a3d9a','#b15928','#e31a1c'], + colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc'], WH: [] } }, diff --git a/frontend/src/components/DataSpace.vue b/frontend/src/components/DataSpace.vue index c75504a4a..ad8850dda 100644 --- a/frontend/src/components/DataSpace.vue +++ b/frontend/src/components/DataSpace.vue @@ -64,7 +64,7 @@ export default { restoreData: 'Restore Step', userSelectedFilter: 'mean', responsiveWidthHeight: [], - colorsValues: ['#6a3d9a','#b15928','#e31a1c'] + colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc'] } }, methods: { diff --git a/frontend/src/components/FinalResultsLinePlot.vue b/frontend/src/components/FinalResultsLinePlot.vue index 0356c1398..23a68500b 100644 --- a/frontend/src/components/FinalResultsLinePlot.vue +++ b/frontend/src/components/FinalResultsLinePlot.vue @@ -144,7 +144,7 @@ export default { text: '', hoverinfo: 'text', fill: "tozerox", - fillcolor: "rgba(55,126,184)", + fillcolor: "rgba(55,126,184,0)", line: {color: "transparent"}, name: "Active Precision", showlegend: false, diff --git a/frontend/src/components/Main.vue b/frontend/src/components/Main.vue index f4322ebd0..ad981a9ee 100755 --- a/frontend/src/components/Main.vue +++ b/frontend/src/components/Main.vue @@ -210,7 +210,7 @@ export default Vue.extend({ ClassifierIDsList: '', SelectedFeaturesPerClassifier: '', FinalResults: 0, - Algorithms: ['KNN','RF'], + Algorithms: ['GPC','KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','BaggingClassifier','AdaB','GradB'], selectedAlgorithm: '', PerformancePerModel: '', PerformanceCheck: '', diff --git a/frontend/src/components/Parameters.vue b/frontend/src/components/Parameters.vue index 9ef21ffd9..5698451c2 100644 --- a/frontend/src/components/Parameters.vue +++ b/frontend/src/components/Parameters.vue @@ -455,7 +455,7 @@ export default { ////////////////////////////////////////////////////////////// var color = d3.scale.ordinal() - .range(["#b3cde3","#fbb4ae"]); + .range(["#808000","#008080"]); var radarChartOptions = { w: width, diff --git a/frontend/src/components/PredictionsSpace.vue b/frontend/src/components/PredictionsSpace.vue index 8d6d7db1a..359dbf582 100644 --- a/frontend/src/components/PredictionsSpace.vue +++ b/frontend/src/components/PredictionsSpace.vue @@ -26,7 +26,7 @@ export default { UpdatedData: '', representationDef: 'mds', representationSelection: 'mds', - colorsValues: ['#6a3d9a','#b15928','#e31a1c'], + colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc'], WH: [] } }, diff --git a/frontend/src/components/Provenance.vue b/frontend/src/components/Provenance.vue index c23734757..185b4f863 100644 --- a/frontend/src/components/Provenance.vue +++ b/frontend/src/components/Provenance.vue @@ -85,7 +85,7 @@ export default { let isotypes = Stardust.mark.create(isotype, this.platform); let isotypeHeight = 18; - let colors = [[141,211,199], [141,160,203]]; + let colors = [[166,206,227], [31,120,180], [178,223,138], [51,160,44], [251,154,153], [227,26,28], [253,191,111], [255,127,0], [202,178,214], [106,61,154], [255,255,153], [177,89,40]]; colors = colors.map(x => [x[0] / 255, x[1] / 255, x[2] / 255, 1]); let pScale = Stardust.scale.custom(` diff --git a/frontend/src/components/ScatterPlot.vue b/frontend/src/components/ScatterPlot.vue index 8de80a437..70e6813ae 100644 --- a/frontend/src/components/ScatterPlot.vue +++ b/frontend/src/components/ScatterPlot.vue @@ -60,6 +60,7 @@ export default { ScatterPlotView () { Plotly.purge('OverviewPlotly') var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0]) + console.log(colorsforScatterPlot) var MDSData = JSON.parse(this.ScatterPlotResults[1]) var parameters = JSON.parse(this.ScatterPlotResults[2]) var TSNEData = JSON.parse(this.ScatterPlotResults[12]) diff --git a/run.py b/run.py index c19dce430..052bf4e0a 100644 --- a/run.py +++ b/run.py @@ -15,11 +15,14 @@ from joblib import Memory from itertools import chain import ast -from sklearn.linear_model import LogisticRegression -from sklearn.neighbors import KNeighborsClassifier -from yellowbrick.regressor import CooksDistance -from sklearn.naive_bayes import GaussianNB -from sklearn.ensemble import RandomForestClassifier +from sklearn.neighbors import KNeighborsClassifier # 1 neighbors +from sklearn.svm import SVC # 1 svm +from sklearn.naive_bayes import GaussianNB # 1 naive bayes +from sklearn.neural_network import MLPClassifier # 1 neural network +from sklearn.linear_model import LogisticRegression # 1 linear model +from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis # 2 discriminant analysis +from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier # 5 ensemble models +from sklearn.calibration import CalibratedClassifierCV from sklearn.pipeline import make_pipeline from sklearn import model_selection from sklearn.manifold import MDS @@ -66,7 +69,7 @@ def Reset(): RANDOM_SEED = 42 global factors - factors = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + factors = [1,1,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1] global XData XData = [] @@ -444,14 +447,55 @@ def RetrieveModel(): # loop through the algorithms global allParametersPerformancePerModel for eachAlgor in algorithms: + print(eachAlgor) if (eachAlgor) == 'KNN': clf = KNeighborsClassifier() - params = {'n_neighbors': list(range(1, 25)), 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']} + params = {'n_neighbors': list(range(1, 25)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']} AlgorithmsIDsEnd = 0 - else: - clf = RandomForestClassifier() - params = {'n_estimators': list(range(40, 120)), 'criterion': ['gini', 'entropy']} + elif (eachAlgor) == 'SVC': + clf = SVC(probability=True) + params = {'C': list(np.arange(0.1,4.43,0.11)), 'kernel': ['rbf','linear', 'poly', 'sigmoid']} AlgorithmsIDsEnd = 576 + elif (eachAlgor) == 'GausNB': + clf = GaussianNB() + params = {'var_smoothing': list(np.arange(0.00000000001,0.0000001,0.0000000001))} + AlgorithmsIDsEnd = 736 + elif (eachAlgor) == 'MLP': + clf = MLPClassifier() + params = {'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0005)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']} + AlgorithmsIDsEnd = 1736 + elif (eachAlgor) == 'LR': + clf = LogisticRegression() + params = {'C': list(np.arange(0.5,2,0.075)), 'max_iter': list(np.arange(50,250,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']} + AlgorithmsIDsEnd = 1816 + elif (eachAlgor) == 'LDA': + clf = LinearDiscriminantAnalysis() + params = {'shrinkage': list(np.arange(0,1,0.018)), 'solver': ['lsqr', 'eigen']} + AlgorithmsIDsEnd = 2536 + elif (eachAlgor) == 'QDA': + clf = QuadraticDiscriminantAnalysis() + params = {'reg_param': list(range(1, 50)), 'tol': list(np.arange(0.00001,0.001,0.0005))} + AlgorithmsIDsEnd = 2716 + elif (eachAlgor) == 'RF': + clf = RandomForestClassifier() + params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']} + AlgorithmsIDsEnd = 2876 + elif (eachAlgor) == 'ExtraT': + clf = ExtraTreesClassifier() + params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']} + AlgorithmsIDsEnd = 3036 + elif (eachAlgor) == 'BagC': + clf = BaggingClassifier() + params = {'n_estimators': list(range(90,110)), 'base_estimator': ['KNeighborsClassifier()', 'DummyClassifier()', 'DecisionTreeClassifier()', 'SVC()', 'BernoulliNB()', 'LogisticRegression()', 'Ridge()', 'Perceptron()', 'LDA()','QDA()']} + AlgorithmsIDsEnd = 1896 + elif (eachAlgor) == 'AdaB': + clf = AdaBoostClassifier() + params = {'n_estimators': list(range(40, 80)), 'learning_rate': list(np.arange(0.1,2.3,1.1)), 'algorithm': ['SAMME.R', 'SAMME']} + AlgorithmsIDsEnd = 3196 + else: + clf = GradientBoostingClassifier() + params = {'n_estimators': list(range(90, 110)), 'learning_rate': list(np.arange(0.01,0.34,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']} + AlgorithmsIDsEnd = 3356 allParametersPerformancePerModel = GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd) # call the function that sends the results to the frontend @@ -542,6 +586,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): resultsMacroBeta2 = [] resultsWeightedBeta2 = [] resultsLogLoss = [] + resultsLogLossFinal = [] loop = 10 @@ -581,9 +626,13 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): resultsMicroBeta2.append(fbeta_score(yData, yPredict, average='micro', beta=2)) resultsMacroBeta2.append(fbeta_score(yData, yPredict, average='macro', beta=2)) resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2)) + + resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True)) - resultsLogLoss.append(log_loss(yData, yPredict, normalize = True)) - + maxLog = max(resultsLogLoss) + minLog = min(resultsLogLoss) + for each in resultsLogLoss: + resultsLogLossFinal.append((each-minLog)/(maxLog-minLog)) metrics.insert(loop,'geometric_mean_score_micro',resultsMicro) metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro) @@ -603,7 +652,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): metrics.insert(loop+11,'f2_macro',resultsMacroBeta2) metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2) - metrics.insert(loop+13,'log_loss',resultsLogLoss) + metrics.insert(loop+13,'log_loss',resultsLogLossFinal) perModelProbPandas = pd.DataFrame(perModelProb) perModelProbPandas = perModelProbPandas.to_json() @@ -890,30 +939,52 @@ def preProcessFeatSc(): def preProcsumPerMetric(factors): sumPerClassifier = [] loopThroughMetrics = PreprocessingMetrics() - print(loopThroughMetrics) for row in loopThroughMetrics.iterrows(): rowSum = 0 - lengthFactors = len(scoring) name, values = row for loop, elements in enumerate(values): - lengthFactors = lengthFactors - 1 + factors[loop] rowSum = elements*factors[loop] + rowSum - if lengthFactors is 0: + if sum(factors) is 0: sumPerClassifier = 0 else: - sumPerClassifier.append(rowSum/lengthFactors) + sumPerClassifier.append(rowSum/sum(factors)) return sumPerClassifier def preProcMetricsAllAndSel(): loopThroughMetrics = PreprocessingMetrics() + global factors metricsPerModelColl = [] metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'].sum()/loopThroughMetrics['mean_test_accuracy'].count()) metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_mean_absolute_error'].sum()/loopThroughMetrics['mean_test_neg_mean_absolute_error'].count()) metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_root_mean_squared_error'].sum()/loopThroughMetrics['mean_test_neg_root_mean_squared_error'].count()) + metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_micro'].sum()/loopThroughMetrics['geometric_mean_score_micro'].count()) + metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'].sum()/loopThroughMetrics['geometric_mean_score_macro'].count()) + metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_weighted'].sum()/loopThroughMetrics['geometric_mean_score_weighted'].count()) metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_micro'].sum()/loopThroughMetrics['mean_test_precision_micro'].count()) - metricsPerModelColl.append(loopThroughMetrics['mean_test_jaccard'].sum()/loopThroughMetrics['mean_test_jaccard'].count()) + metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_macro'].sum()/loopThroughMetrics['mean_test_precision_macro'].count()) + metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_weighted'].sum()/loopThroughMetrics['mean_test_precision_weighted'].count()) + metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_micro'].sum()/loopThroughMetrics['mean_test_recall_micro'].count()) + metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_macro'].sum()/loopThroughMetrics['mean_test_recall_macro'].count()) + metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_weighted'].sum()/loopThroughMetrics['mean_test_recall_weighted'].count()) + metricsPerModelColl.append(loopThroughMetrics['f5_micro'].sum()/loopThroughMetrics['f5_micro'].count()) + metricsPerModelColl.append(loopThroughMetrics['f5_macro'].sum()/loopThroughMetrics['f5_macro'].count()) + metricsPerModelColl.append(loopThroughMetrics['f5_weighted'].sum()/loopThroughMetrics['f5_weighted'].count()) + metricsPerModelColl.append(loopThroughMetrics['f1_micro'].sum()/loopThroughMetrics['f1_micro'].count()) + metricsPerModelColl.append(loopThroughMetrics['f1_macro'].sum()/loopThroughMetrics['f1_macro'].count()) + metricsPerModelColl.append(loopThroughMetrics['f1_weighted'].sum()/loopThroughMetrics['f1_weighted'].count()) + metricsPerModelColl.append(loopThroughMetrics['f2_micro'].sum()/loopThroughMetrics['f2_micro'].count()) + metricsPerModelColl.append(loopThroughMetrics['f2_macro'].sum()/loopThroughMetrics['f2_macro'].count()) + metricsPerModelColl.append(loopThroughMetrics['f2_weighted'].sum()/loopThroughMetrics['f2_weighted'].count()) + metricsPerModelColl.append(loopThroughMetrics['matthews_corrcoef'].sum()/loopThroughMetrics['matthews_corrcoef'].count()) + metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo_weighted'].sum()/loopThroughMetrics['mean_test_roc_auc_ovo_weighted'].count()) + metricsPerModelColl.append(loopThroughMetrics['log_loss'].sum()/loopThroughMetrics['log_loss'].count()) for index, metric in enumerate(metricsPerModelColl): - metricsPerModelColl[index] = metric*factors[index] + if (index == 1 or index == 2): + metricsPerModelColl[index] = (metric + 1)*factors[index] + elif (index == 23): + metricsPerModelColl[index] = (1 - metric)*factors[index] + else: + metricsPerModelColl[index] = metric*factors[index] return metricsPerModelColl def preProceModels(): @@ -932,7 +1003,7 @@ def FunTsne (data): return tsne def FunUMAP (data): - trans = umap.UMAP(n_neighbors=5, random_state=RANDOM_SEED).fit(data) + trans = umap.UMAP(n_neighbors=15, random_state=RANDOM_SEED).fit(data) Xpos = trans.embedding_[:, 0].tolist() Ypos = trans.embedding_[:, 1].tolist() return [Xpos,Ypos]