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Add example
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‎.gitignore

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# Custom
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datasets/**/**.csv
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results/**/**.npy
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results/**/**.npy
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reports/**/**.html
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!datasets/example/voice.csv
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!reports/example/voice.html
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!results/example/voice.npy

‎datasets/example/voice.csv

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‎get_max_score.py

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for key, value in amount.items():
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print(str(key) + ' : ' + str(value) + ' , ' + str(round(value/total, 4)))
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array = np.load('results/brazil_dataset_french_clean.npy', allow_pickle=True)
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max_score = 0
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max_score_method = None

‎get_max_scores.py

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import numpy as np
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import pandas as pd
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import sys
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import os
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def getMaxScoreMethod(label, arr):
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max_score = 0
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max_score_method = None
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for experiment in arr:
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if (experiment[label] >= max_score):
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max_score = experiment[label]
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max_score_method = experiment
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return max_score_method
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def renderMaxScoreMethod(label, arr):
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d = getMaxScoreMethod(label, arr)
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html = "<h3>Method for: maximum of " + label + "</h3>"
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html += "<p><ul>"
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for key, value in d.items():
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if (key == 'oversampler' or key == 'classifier'):
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value = removeRandomStateParam(value)
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html += "<li>" + key + " "+str(value)+"</li>"
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html+= "</ul></p>"
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return html
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def removeRandomStateParam(model):
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if (model != None):
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d=model.get_params()
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d['random_state'] = None
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model.set_params(**d)
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return model
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def renderHTML(name,arr,dataset):
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if (len(arr) == 0):
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return "Cannot render"
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prettyName = os.path.basename(os.path.normpath(name))
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html = "<html><head><meta name='viewport' content='width'>\
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<link rel='stylesheet' href='https://unpkg.com/marx-css/css/marx.min.css'><title>"
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html += prettyName
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html += " dataset</title></head><body><h1>"
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html += prettyName
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html += " dataset</h1>"
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unique, counts = np.unique(dataset.iloc[:, -1], return_counts=True)
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amount = dict(zip(unique, counts))
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total = sum(counts)
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html += "<p>Total: " + str(total)
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for key, value in amount.items():
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html += "<br>" + str(key) + ": " + str(value) + " , " + str(round(value/total, 4))
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html += "</p>"
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for key, value in arr[0].items():
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if (key != "classifier" and key != "oversampler" and key != "cm"):
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html += renderMaxScoreMethod(key, arr)
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html += "</body></html>"
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return html
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def writeHTML(name, arr, dataset):
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f = open("reports/" + name + ".html", "w")
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f.write(renderHTML(name, arr, dataset))
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f.close()
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if __name__ == "__main__":
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default_path = ''
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if (len(sys.argv) != 1):
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default_path = sys.argv[1]
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if (default_path == ''):
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exit('No dataset specified.')
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dataset = pd.read_csv('datasets/' + default_path + '.csv')
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array = np.load('results/' + default_path + '.npy', allow_pickle=True)
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writeHTML(default_path, array, dataset)

‎reports/.gitkeep

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‎reports/example/voice.html

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<html><head><meta name='viewport' content='width'> <link rel='stylesheet' href='https://unpkg.com/marx-css/css/marx.min.css'><title>voice dataset</title></head><body><h1>voice dataset</h1><p>Total: 3168<br>0: 1584 , 0.5<br>1: 1584 , 0.5</p><h3>Method for: maximum of train_score</h3><p><ul><li>train_score 1.0</li><li>test_score 0.9772870662460568</li><li>classifier RandomForestClassifier(criterion='entropy', max_features='log2',
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n_estimators=200)</li><li>oversampler BorderlineSMOTE(k_neighbors=20, kind='borderline-2')</li><li>cm [[305. 6.9]
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[ 7.5 314.6]]</li><li>recall_score 0.9767642400937866</li><li>roc_auc_score 0.977244181476375</li><li>precision_score 0.9786422056161875</li><li>f1_score 0.9776969602541465</li><li>f1_score_macro 0.977250275353066</li><li>f1_score_weighted 0.9772867960003525</li></ul></p><h3>Method for: maximum of test_score</h3><p><ul><li>train_score 0.999960722702278</li><li>test_score 0.9853312302839117</li><li>classifier RandomForestClassifier(criterion='entropy', max_features='log2')</li><li>oversampler SMOTE(k_neighbors=3)</li><li>cm [[304.6 4.7]
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[ 4.6 320.1]]</li><li>recall_score 0.9857921852691428</li><li>roc_auc_score 0.9852988467694319</li><li>precision_score 0.9855472263856319</li><li>f1_score 0.9856502927885332</li><li>f1_score_macro 0.9853132217519034</li><li>f1_score_weighted 0.9853309441448725</li></ul></p><h3>Method for: maximum of recall_score</h3><p><ul><li>train_score 1.0</li><li>test_score 0.9813880126182966</li><li>classifier GradientBoostingClassifier(n_estimators=200)</li><li>oversampler None</li><li>cm [[312.2 8.3]
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[ 3.5 310. ]]</li><li>recall_score 0.9888748276576491</li><li>roc_auc_score 0.9815830301137695</li><li>precision_score 0.9739028313866314</li><li>f1_score 0.9812525514595002</li><li>f1_score_macro 0.9813704251179102</li><li>f1_score_weighted 0.9813899553288561</li></ul></p><h3>Method for: maximum of roc_auc_score</h3><p><ul><li>train_score 0.999960722702278</li><li>test_score 0.9853312302839117</li><li>classifier RandomForestClassifier(criterion='entropy', max_features='log2')</li><li>oversampler SMOTE(k_neighbors=3)</li><li>cm [[304.6 4.7]
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[ 4.6 320.1]]</li><li>recall_score 0.9857921852691428</li><li>roc_auc_score 0.9852988467694319</li><li>precision_score 0.9855472263856319</li><li>f1_score 0.9856502927885332</li><li>f1_score_macro 0.9853132217519034</li><li>f1_score_weighted 0.9853309441448725</li></ul></p><h3>Method for: maximum of precision_score</h3><p><ul><li>train_score 0.999960722702278</li><li>test_score 0.9853312302839117</li><li>classifier RandomForestClassifier(criterion='entropy', max_features='log2')</li><li>oversampler SMOTE(k_neighbors=3)</li><li>cm [[304.6 4.7]
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[ 4.6 320.1]]</li><li>recall_score 0.9857921852691428</li><li>roc_auc_score 0.9852988467694319</li><li>precision_score 0.9855472263856319</li><li>f1_score 0.9856502927885332</li><li>f1_score_macro 0.9853132217519034</li><li>f1_score_weighted 0.9853309441448725</li></ul></p><h3>Method for: maximum of f1_score</h3><p><ul><li>train_score 0.999960722702278</li><li>test_score 0.9853312302839117</li><li>classifier RandomForestClassifier(criterion='entropy', max_features='log2')</li><li>oversampler SMOTE(k_neighbors=3)</li><li>cm [[304.6 4.7]
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[ 4.6 320.1]]</li><li>recall_score 0.9857921852691428</li><li>roc_auc_score 0.9852988467694319</li><li>precision_score 0.9855472263856319</li><li>f1_score 0.9856502927885332</li><li>f1_score_macro 0.9853132217519034</li><li>f1_score_weighted 0.9853309441448725</li></ul></p><h3>Method for: maximum of f1_score_macro</h3><p><ul><li>train_score 0.999960722702278</li><li>test_score 0.9853312302839117</li><li>classifier RandomForestClassifier(criterion='entropy', max_features='log2')</li><li>oversampler SMOTE(k_neighbors=3)</li><li>cm [[304.6 4.7]
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[ 4.6 320.1]]</li><li>recall_score 0.9857921852691428</li><li>roc_auc_score 0.9852988467694319</li><li>precision_score 0.9855472263856319</li><li>f1_score 0.9856502927885332</li><li>f1_score_macro 0.9853132217519034</li><li>f1_score_weighted 0.9853309441448725</li></ul></p><h3>Method for: maximum of f1_score_weighted</h3><p><ul><li>train_score 0.999960722702278</li><li>test_score 0.9853312302839117</li><li>classifier RandomForestClassifier(criterion='entropy', max_features='log2')</li><li>oversampler SMOTE(k_neighbors=3)</li><li>cm [[304.6 4.7]
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[ 4.6 320.1]]</li><li>recall_score 0.9857921852691428</li><li>roc_auc_score 0.9852988467694319</li><li>precision_score 0.9855472263856319</li><li>f1_score 0.9856502927885332</li><li>f1_score_macro 0.9853132217519034</li><li>f1_score_weighted 0.9853309441448725</li></ul></p></body></html>

‎results/example/voice.npy

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