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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], 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"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=119,\n n_jobs=None, oob_score=False, random_state=None,\n verbose=0, warm_start=False)", "params": "{'n_estimators': [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 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], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "576"}} \ No newline at end of file diff --git a/cachedir/joblib/run/GridSearchForModels/ef9a593cce41dd71bdac1d445edc2a58/output.pkl b/cachedir/joblib/run/GridSearchForModels/ef9a593cce41dd71bdac1d445edc2a58/output.pkl deleted file mode 100644 index 1d77cd3a6..000000000 Binary files a/cachedir/joblib/run/GridSearchForModels/ef9a593cce41dd71bdac1d445edc2a58/output.pkl and /dev/null differ diff --git a/cachedir/joblib/run/GridSearchForModels/func_code.py b/cachedir/joblib/run/GridSearchForModels/func_code.py index 32c047996..826154555 100644 --- a/cachedir/joblib/run/GridSearchForModels/func_code.py +++ b/cachedir/joblib/run/GridSearchForModels/func_code.py @@ -1,6 +1,6 @@ -# first line: 465 +# first line: 466 @memory.cache -def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd): +def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd): # instantiate spark session spark = ( @@ -45,30 +45,13 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm # copy and filter in order to get only the metrics metrics = df_cv_results_classifiers.copy() - metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard']) - - # control the factors - sumperModel = [] - for index, row in metrics.iterrows(): - rowSum = 0 - lengthFactors = len(scoring) - for loop,elements in enumerate(row): - lengthFactors = lengthFactors - 1 + factors[loop] - rowSum = elements*factors[loop] + rowSum - if lengthFactors is 0: - sumperModel = 0 - else: - sumperModel.append(rowSum/lengthFactors) - - # summarize all models metrics - summarizedMetrics = pd.DataFrame(sumperModel) - summarizedMetrics.rename(columns={0:'sum'}) + + metrics = metrics.filter(['mean_test_accuracy','mean_test_neg_mean_absolute_error','mean_test_neg_root_mean_squared_error','mean_test_precision_micro','mean_test_precision_macro','mean_test_precision_weighted','mean_test_recall_micro','mean_test_recall_macro','mean_test_recall_weighted','mean_test_roc_auc_ovo_weighted']) # concat parameters and performance - parameters = pd.DataFrame(df_cv_results_classifiers['params']) - parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1) + parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params']) parametersPerformancePerModel = parametersPerformancePerModel.to_json() - + parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy() Models = [] for index, items in enumerate(parametersLocal): @@ -81,13 +64,29 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm PerFeatureAccuracyAll = [] PerClassMetric = [] perModelProb = [] + resultsMicro = [] + resultsMacro = [] + resultsWeighted = [] + resultsCorrCoef = [] + resultsMicroBeta5 = [] + resultsMacroBeta5 = [] + resultsWeightedBeta5 = [] + resultsMicroBeta1 = [] + resultsMacroBeta1 = [] + resultsWeightedBeta1 = [] + resultsMicroBeta2 = [] + resultsMacroBeta2 = [] + resultsWeightedBeta2 = [] + resultsLogLoss = [] + + loop = 10 for eachModelParameters in parametersLocalNew: clf.set_params(**eachModelParameters) perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData) permList.append(perm.feature_importances_) - + n_feats = XData.shape[1] PerFeatureAccuracy = [] for i in range(n_feats): @@ -101,6 +100,47 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm yPredictProb = clf.predict_proba(XData) perModelProb.append(yPredictProb.tolist()) + resultsMicro.append(geometric_mean_score(yData, yPredict, average='micro')) + resultsMacro.append(geometric_mean_score(yData, yPredict, average='macro')) + resultsWeighted.append(geometric_mean_score(yData, yPredict, average='weighted')) + + resultsCorrCoef.append(matthews_corrcoef(yData, yPredict)) + + resultsMicroBeta5.append(fbeta_score(yData, yPredict, average='micro', beta=0.5)) + resultsMacroBeta5.append(fbeta_score(yData, yPredict, average='macro', beta=0.5)) + resultsWeightedBeta5.append(fbeta_score(yData, yPredict, average='weighted', beta=0.5)) + + resultsMicroBeta1.append(fbeta_score(yData, yPredict, average='micro', beta=1)) + resultsMacroBeta1.append(fbeta_score(yData, yPredict, average='macro', beta=1)) + resultsWeightedBeta1.append(fbeta_score(yData, yPredict, average='weighted', beta=1)) + + 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, yPredict, normalize = True)) + + + metrics.insert(loop,'geometric_mean_score_micro',resultsMicro) + metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro) + metrics.insert(loop+2,'geometric_mean_score_weighted',resultsWeighted) + + metrics.insert(loop+3,'matthews_corrcoef',resultsCorrCoef) + + metrics.insert(loop+4,'f5_micro',resultsMicroBeta5) + metrics.insert(loop+5,'f5_macro',resultsMacroBeta5) + metrics.insert(loop+6,'f5_weighted',resultsWeightedBeta5) + + metrics.insert(loop+7,'f1_micro',resultsMicroBeta1) + metrics.insert(loop+8,'f1_macro',resultsMacroBeta1) + metrics.insert(loop+9,'f1_weighted',resultsWeightedBeta1) + + metrics.insert(loop+10,'f2_micro',resultsMicroBeta2) + metrics.insert(loop+11,'f2_macro',resultsMacroBeta2) + metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2) + + metrics.insert(loop+13,'log_loss',resultsLogLoss) + perModelProbPandas = pd.DataFrame(perModelProb) perModelProbPandas = perModelProbPandas.to_json() diff --git a/frontend/package-lock.json b/frontend/package-lock.json old mode 100755 new mode 100644 index d900d77be..8eaac1e51 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -3057,6 +3057,13 @@ "d3-array": "1", "d3-collection": "1", "d3-shape": "^1.2.0" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "@plotly/d3-sankey-circular": { @@ -3068,6 +3075,13 @@ "d3-collection": "^1.0.4", "d3-shape": "^1.2.0", "elementary-circuits-directed-graph": "^1.0.4" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "@sindresorhus/is": { @@ -9102,12 +9116,19 @@ "d3-transition": "1", "d3-voronoi": "1", "d3-zoom": "1" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "d3-array": { - "version": "1.2.4", - "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", - "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + "version": "2.4.0", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-2.4.0.tgz", + "integrity": "sha512-KQ41bAF2BMakf/HdKT865ALd4cgND6VcIztVQZUTt0+BH3RWy6ZYnHghVXf6NFjt2ritLr8H1T8LreAAlfiNcw==" }, "d3-axis": { "version": "1.0.12", @@ -9115,9 +9136,9 @@ "integrity": "sha512-ejINPfPSNdGFKEOAtnBtdkpr24c4d4jsei6Lg98mxf424ivoDP2956/5HDpIAtmHo85lqT4pruy+zEgvRUBqaQ==" }, "d3-brush": { - "version": "1.0.6", - "resolved": "https://registry.npmjs.org/d3-brush/-/d3-brush-1.0.6.tgz", - "integrity": "sha512-lGSiF5SoSqO5/mYGD5FAeGKKS62JdA1EV7HPrU2b5rTX4qEJJtpjaGLJngjnkewQy7UnGstnFd3168wpf5z76w==", + "version": "1.1.5", + "resolved": "https://registry.npmjs.org/d3-brush/-/d3-brush-1.1.5.tgz", + "integrity": "sha512-rEaJ5gHlgLxXugWjIkolTA0OyMvw8UWU1imYXy1v642XyyswmI1ybKOv05Ft+ewq+TFmdliD3VuK0pRp1VT/5A==", "requires": { "d3-dispatch": "1", "d3-drag": "1", @@ -9133,6 +9154,13 @@ "requires": { "d3-array": "1", "d3-path": "1" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "d3-collection": { @@ -9151,6 +9179,13 @@ "integrity": "sha512-hoPp4K/rJCu0ladiH6zmJUEz6+u3lgR+GSm/QdM2BBvDraU39Vr7YdDCicJcxP1z8i9B/2dJLgDC1NcvlF8WCg==", "requires": { "d3-array": "^1.1.1" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "d3-delaunay": { @@ -9162,9 +9197,9 @@ } }, "d3-dispatch": { - "version": "1.0.5", - "resolved": "https://registry.npmjs.org/d3-dispatch/-/d3-dispatch-1.0.5.tgz", - 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"https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "d3-heatmap": { @@ -9242,7 +9291,7 @@ }, "dependencies": { "d3": { - "version": , + "version": "3.5.17", "resolved": "https://registry.npmjs.org/d3/-/d3-3.5.17.tgz", "integrity": "sha1-vEZ0gAQ3iyGjYMn8fPUjF5B2L7g=" } @@ -9281,6 +9330,13 @@ "d3-collection": "^1.0.4", "d3-interpolate": "^1.1.5", "d3-path": "^1.0.5" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "d3-path": { @@ -9314,6 +9370,13 @@ "d3-interpolate": "1", "d3-time": "1", "d3-time-format": "2" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + 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"resolved": "https://registry.npmjs.org/once/-/once-1.4.0.tgz", + "integrity": "sha1-WDsap3WWHUsROsF9nFC6753Xa9E=", "optional": true, "requires": { "wrappy": "1" @@ -12787,17 +12897,20 @@ }, "os-homedir": { "version": "1.0.2", - "bundled": true, + "resolved": "https://registry.npmjs.org/os-homedir/-/os-homedir-1.0.2.tgz", + "integrity": "sha1-/7xJiDNuDoM94MFox+8VISGqf7M=", "optional": true }, "os-tmpdir": { "version": "1.0.2", - "bundled": true, + "resolved": "https://registry.npmjs.org/os-tmpdir/-/os-tmpdir-1.0.2.tgz", + "integrity": "sha1-u+Z0BseaqFxc/sdm/lc0VV36EnQ=", "optional": true }, "osenv": { "version": "0.1.5", - "bundled": true, + "resolved": "https://registry.npmjs.org/osenv/-/osenv-0.1.5.tgz", + "integrity": "sha512-0CWcCECdMVc2Rw3U5w9ZjqX6ga6ubk1xDVKxtBQPK7wis/0F2r9T6k4ydGYhecl7YUBxBVxhL5oisPsNxAPe2g==", "optional": true, "requires": { "os-homedir": "^1.0.0", @@ -12806,17 +12919,20 @@ }, "path-is-absolute": { "version": "1.0.1", - "bundled": true, + "resolved": "https://registry.npmjs.org/path-is-absolute/-/path-is-absolute-1.0.1.tgz", + "integrity": "sha1-F0uSaHNVNP+8es5r9TpanhtcX18=", "optional": true }, "process-nextick-args": { "version": "2.0.0", - "bundled": true, + "resolved": "https://registry.npmjs.org/process-nextick-args/-/process-nextick-args-2.0.0.tgz", + "integrity": "sha512-MtEC1TqN0EU5nephaJ4rAtThHtC86dNN9qCuEhtshvpVBkAW5ZO7BASN9REnF9eoXGcRub+pFuKEpOHE+HbEMw==", "optional": true }, "rc": { "version": "1.2.8", - "bundled": true, + "resolved": "https://registry.npmjs.org/rc/-/rc-1.2.8.tgz", + "integrity": "sha512-y3bGgqKj3QBdxLbLkomlohkvsA8gdAiUQlSBJnBhfn+BPxg4bc62d8TcBW15wavDfgexCgccckhcZvywyQYPOw==", "optional": true, "requires": { "deep-extend": "^0.6.0", @@ -12827,14 +12943,16 @@ "dependencies": { "minimist": { "version": "1.2.0", - "bundled": true, + "resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz", + "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=", "optional": true } } }, "readable-stream": { "version": "2.3.6", - "bundled": true, + "resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz", + "integrity": "sha512-tQtKA9WIAhBF3+VLAseyMqZeBjW0AHJoxOtYqSUZNJxauErmLbVm2FW1y+J/YA9dUrAC39ITejlZWhVIwawkKw==", "optional": true, "requires": { "core-util-is": "~1.0.0", @@ -12848,7 +12966,8 @@ }, "rimraf": { "version": "2.6.3", - "bundled": true, + "resolved": "https://registry.npmjs.org/rimraf/-/rimraf-2.6.3.tgz", + "integrity": "sha512-mwqeW5XsA2qAejG46gYdENaxXjx9onRNCfn7L0duuP4hCuTIi/QO7PDK07KJfp1d+izWPrzEJDcSqBa0OZQriA==", "optional": true, "requires": { "glob": "^7.1.3" @@ -12856,37 +12975,44 @@ }, "safe-buffer": { "version": "5.1.2", - "bundled": true, + "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz", + "integrity": "sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g==", "optional": true }, "safer-buffer": { "version": "2.1.2", - "bundled": true, + "resolved": 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"https://registry.npmjs.org/signal-exit/-/signal-exit-3.0.2.tgz", + "integrity": "sha1-tf3AjxKH6hF4Yo5BXiUTK3NkbG0=", "optional": true }, "string-width": { "version": "1.0.2", - "bundled": true, + "resolved": "https://registry.npmjs.org/string-width/-/string-width-1.0.2.tgz", + "integrity": "sha1-EYvfW4zcUaKn5w0hHgfisLmxB9M=", "optional": true, "requires": { "code-point-at": "^1.0.0", @@ -12896,7 +13022,8 @@ }, "string_decoder": { "version": "1.1.1", - "bundled": true, + "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.1.1.tgz", + "integrity": "sha512-n/ShnvDi6FHbbVfviro+WojiFzv+s8MPMHBczVePfUpDJLwoLT0ht1l4YwBCbi8pJAveEEdnkHyPyTP/mzRfwg==", "optional": true, "requires": { "safe-buffer": "~5.1.0" @@ -12904,7 +13031,8 @@ }, "strip-ansi": { "version": "3.0.1", - "bundled": true, + "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz", + "integrity": "sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=", "optional": true, "requires": { "ansi-regex": "^2.0.0" @@ -12912,12 +13040,14 @@ }, "strip-json-comments": { "version": "2.0.1", - "bundled": true, + "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz", + "integrity": "sha1-PFMZQukIwml8DsNEhYwobHygpgo=", "optional": true }, "tar": { "version": "4.4.8", - "bundled": true, + "resolved": "https://registry.npmjs.org/tar/-/tar-4.4.8.tgz", + "integrity": "sha512-LzHF64s5chPQQS0IYBn9IN5h3i98c12bo4NCO7e0sGM2llXQ3p2FGC5sdENN4cTW48O915Sh+x+EXx7XW96xYQ==", "optional": true, "requires": { "chownr": "^1.1.1", @@ -12931,12 +13061,14 @@ }, "util-deprecate": { "version": "1.0.2", - "bundled": true, + "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", + "integrity": "sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=", "optional": true }, "wide-align": { "version": "1.1.3", - "bundled": true, + "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.3.tgz", + "integrity": "sha512-QGkOQc8XL6Bt5PwnsExKBPuMKBxnGxWWW3fU55Xt4feHozMUhdUMaBCk290qpm/wG5u/RSKzwdAC4i51YigihA==", "optional": true, "requires": { "string-width": "^1.0.2 || 2" @@ -12944,12 +13076,14 @@ }, "wrappy": { "version": "1.0.2", - "bundled": true, + "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", + "integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=", "optional": true }, "yallist": { "version": "3.0.3", - "bundled": true, + "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.0.3.tgz", + "integrity": "sha512-S+Zk8DEWE6oKpV+vI3qWkaK+jSbIK86pCwe2IF/xwIpQ8jEuxpw9NyaGjmp9+BoJv5FV2piqCDcoCtStppiq2A==", "optional": true } } @@ -17208,6 +17342,11 @@ "readable-stream": "^2.0.1" } }, + "merge": { + "version": "1.2.1", + "resolved": "https://registry.npmjs.org/merge/-/merge-1.2.1.tgz", + "integrity": "sha512-VjFo4P5Whtj4vsLzsYBu5ayHhoHJ0UqNm7ibvShmbmoz7tGi0vXaoJbGdB+GmDMLUdg8DpQXEIeVDAe8MaABvQ==" + }, "merge-deep": { "version": "3.0.2", "resolved": "https://registry.npmjs.org/merge-deep/-/merge-deep-3.0.2.tgz", @@ -19201,6 +19340,13 @@ "d3-transition": "^1.1.1", "requestanimationframe": "0.0.23", "sylvester-es6": "0.0.2" + }, + "dependencies": { + "d3-array": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz", + "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw==" + } } }, "parent-module": { @@ -19724,6 +19870,7 @@ "d3-force": "^1.0.6", "d3-hierarchy": "^1.1.8", "d3-interpolate": "1", + "d3-svg-legend": "^1.13.0", "delaunay-triangulate": "^1.1.6", "es6-promise": "^3.0.2", "fast-isnumeric": "^1.1.3", @@ -19774,9 +19921,24 @@ }, "dependencies": { "d3": { - "version": , + "version": "3.5.17", "resolved": "https://registry.npmjs.org/d3/-/d3-3.5.17.tgz", - "integrity": "sha1-vEZ0gAQ3iyGjYMn8fPUjF5B2L7g=" + "integrity": "sha1-vEZ0gAQ3iyGjYMn8fPUjF5B2L7g=", + "requires": { + "d3-svg-legend": "^1.13.0" + }, + "dependencies": { + "d3-svg-legend": { + "version": "1.13.0", + "resolved": "https://registry.npmjs.org/d3-svg-legend/-/d3-svg-legend-1.13.0.tgz", + "integrity": "sha1-YhdHjJrdnWLLMzYX4ZYTEaQaTbM=" + } + } + }, + "d3-svg-legend": { + "version": "1.13.0", + "resolved": "https://registry.npmjs.org/d3-svg-legend/-/d3-svg-legend-1.13.0.tgz", + "integrity": "sha1-YhdHjJrdnWLLMzYX4ZYTEaQaTbM=" } } }, diff --git a/frontend/package.json b/frontend/package.json index d68ec7d54..72f9d750a 100755 --- a/frontend/package.json +++ b/frontend/package.json @@ -33,12 +33,16 @@ "clean-webpack-plugin": "^3.0.0", "colorbrewer": "^1.3.0", "d3": "^5.15.0", + "d3-array": "^2.4.0", + "d3-brush": "^1.1.5", + "d3-dispatch": "^1.0.6", "d3-drag": "^1.2.5", "d3-heatmap": "^1.2.1", "d3-lasso": "0.0.5", "d3-loom": "^1.0.2", "d3-selection": "^1.4.1", "d3-selection-multi": "^1.0.1", + "d3-svg-legend": "^1.13.0", "d3_exploding_boxplot": "^0.2.1", "file-saver": "^2.0.2", "file-system": "^2.2.2", @@ -49,6 +53,7 @@ "interactjs": "^1.8.2", "jquery": "^3.4.1", "mdbvue": "^6.3.0", + "merge": "^1.2.1", "mini-css-extract-plugin": "^0.9.0", "npm-check-updates": "^4.0.1", "papaparse": "^5.1.1", diff --git a/frontend/src/components/AlgorithmHyperParam.vue b/frontend/src/components/AlgorithmHyperParam.vue index 7692ba980..315b5c0ea 100644 --- a/frontend/src/components/AlgorithmHyperParam.vue +++ b/frontend/src/components/AlgorithmHyperParam.vue @@ -19,7 +19,12 @@ export default { ModelsPerformance: 0, selAlgorithm: 0, pc: 0, - factors: [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 + ], KNNModels: 576 //KNN models } }, @@ -42,9 +47,29 @@ 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 - sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg1['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg1['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg1['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['mean_test_jaccard'])[j]) + sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg1['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg1['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg1['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['geometric_mean_score_macro'])[j]) + + (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)))) Mc1.push((sum/divide)*100) } @@ -52,7 +77,11 @@ export default { const performanceAlg2 = JSON.parse(this.ModelsPerformance[14]) for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) { let sum2 - sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg2['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg2['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg2['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['mean_test_jaccard'])[j]) + sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg2['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg2['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg2['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['geometric_mean_score_macro'])[j]) + + (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)))) Mc2.push((sum2/divide)*100) } @@ -64,7 +93,8 @@ export default { Combined = JSON.parse(this.ModelsPerformance[9]) colorGiv = colors[1] } - var valuesPerf = Object.values(Combined['0']) + var valuesPerf = Object.values(Combined['params']) + var ObjectsParams = Combined['params'] var newObjectsParams = [] var newObjectsParams2 = [] diff --git a/frontend/src/components/Algorithms.vue b/frontend/src/components/Algorithms.vue index 7a3451952..e9d8f8b0a 100644 --- a/frontend/src/components/Algorithms.vue +++ b/frontend/src/components/Algorithms.vue @@ -28,7 +28,12 @@ export default { parameters: [], algorithm1: [], algorithm2: [], - factors: [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 + ], chart: '', flagEmpty: 0, ActiveModels: [], @@ -53,11 +58,32 @@ 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 - sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg1['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg1['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg1['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['mean_test_jaccard'])[j]) + sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg1['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg1['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg1['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['geometric_mean_score_macro'])[j]) + + (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)))) Mc1.push((sum/divide)*100) } @@ -65,7 +91,11 @@ export default { const performanceAlg2 = JSON.parse(this.PerformanceAllModels[14]) for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) { let sum2 - sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg2['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg2['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg2['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['mean_test_jaccard'])[j]) + sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg2['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg2['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg2['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['geometric_mean_score_macro'])[j]) + + (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)))) Mc2.push((sum2/divide)*100) } @@ -78,12 +108,12 @@ export default { this.algorithm2 = [] this.parameters = [] - for (var i = 0; i < Object.keys(PerformAlgor1['0']).length; i++) { - this.algorithm1.push({'Performance Metrics': Mc1[i],Algorithm:'KNN',Model:'Model ' + Algor1IDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance Metrics ',ModelID:Algor1IDs[i]}) + for (var i = 0; i < Object.keys(PerformAlgor1['params']).length; i++) { + this.algorithm1.push({'Performance (%)': Mc1[i],Algorithm:'KNN',Model:'Model ' + Algor1IDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance (%) ',ModelID:Algor1IDs[i]}) this.parameters.push(JSON.stringify(Object.values(PerformAlgor1['params'])[i])) } - for (let j = 0; j < Object.keys(PerformAlgor2['0']).length; j++) { - this.algorithm2.push({'Performance Metrics': Mc2[j],Algorithm:'RF',Model:'Model ' + Algor2IDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance Metrics ',ModelID:Algor2IDs[j]}) + for (let j = 0; j < Object.keys(PerformAlgor2['params']).length; j++) { + this.algorithm2.push({'Performance (%)': Mc2[j],Algorithm:'RF',Model:'Model ' + Algor2IDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance (%) ',ModelID:Algor2IDs[j]}) this.parameters.push(JSON.stringify(Object.values(PerformAlgor2['params'])[j])) } @@ -97,7 +127,7 @@ export default { // group : how to group data on x axis // color : color of the point / boxplot // label : displayed text in toolbox - this.chart = exploding_boxplot(data, {y:'Performance Metrics',group:'Algorithm',color:'Algorithm',label:'Model'}) + this.chart = exploding_boxplot(data, {y:'Performance (%)',group:'Algorithm',color:'Algorithm',label:'Model'}) this.chart.width(this.WH[0]*3) // interactive visualization this.chart.height(this.WH[1]*0.9) // interactive visualization //call chart on a div @@ -181,7 +211,7 @@ export default { algorithm = this.algorithm2 } for (let k = 0; k < allPoints.length; k++) { - if (algorithm[k]['Performance Metrics'] < limiter[0] && algorithm[k]['Performance Metrics'] > limiter[1]) { + if (algorithm[k]['Performance (%)'] < limiter[0] && algorithm[k]['Performance (%)'] > limiter[1]) { modelsActive.push(algorithm[k].ModelID) } } diff --git a/frontend/src/components/DataSpace.vue b/frontend/src/components/DataSpace.vue index 7bc7b5725..c75504a4a 100644 --- a/frontend/src/components/DataSpace.vue +++ b/frontend/src/components/DataSpace.vue @@ -28,7 +28,7 @@ {{ removeData }} - Provenance Controller: