Merge branch 'feature/GenerateData' into develop

.gitignore

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+# project data
+data
+data_svm
+saved_models
+
+.python-version
+__pycache__
+
+# by default avoid model files and png files
+*.h5
+*.png
+!saved_models/*.h5
+!saved_models/*.png
+.vscode

LICENCE

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+MIT License
+Copyright (c) <year> <copyright holders>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+

README.md

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+# Noise detection using SVM
+
+## Requirements
+
+```
+pip install -r requirements.txt
+```
+
+## How to use
+
+Generate data .csv files (run only once time or clean data folder before) :
+
+```
+python generate_data.py
+```
+
+## Models
+
+*Documentation will be implemented later...*
+
+## How to contribute
+
+This git project uses [git-flow](https://danielkummer.github.io/git-flow-cheatsheet/) implementation. You are free to contribute to it.

ensemble_model_train.py

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+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier, VotingClassifier
+
+import sklearn.svm as svm
+from sklearn.externals import joblib
+
+import numpy as np
+
+
+import pandas as pd
+from sklearn.metrics import accuracy_score
+
+import sys, os, getopt
+
+output_model_folder = './saved_models/'
+
+def get_best_model(X_train, y_train):
+    parameters = {'kernel':['rbf'], 'C': np.arange(1, 20)}
+    svc = svm.SVC(gamma="scale", probability=True)
+    clf = GridSearchCV(svc, parameters, cv=5, scoring='accuracy', verbose=10)
+
+    clf.fit(X_train, y_train)
+
+    model = clf.best_estimator_
+
+    return model
+
+
+def main():
+
+    if len(sys.argv) <= 1:
+        print('Run with default parameters...')
+        print('python smv_model_train.py --data xxxx --output xxxx')
+        sys.exit(2)
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], "hd:o", ["help=", "data=", "output="])
+    except getopt.GetoptError:
+        # print help information and exit:
+        print('python smv_model_train.py --data xxxx --output xxxx')
+        sys.exit(2)
+    for o, a in opts:
+        if o == "-h":
+            print('python smv_model_train.py --data xxxx --output xxxx')
+            sys.exit()
+        elif o in ("-d", "--data"):
+            p_data_file = a
+        elif o in ("-o", "--output"):
+            p_output = a
+        else:
+            assert False, "unhandled option"
+
+    if not os.path.exists(output_model_folder):
+        os.makedirs(output_model_folder)
+
+    # get and split data
+    dataset = pd.read_csv(p_data_file, header=None, sep=";")
+
+    y_dataset = dataset.ix[:,0]
+    x_dataset = dataset.ix[:,1:]
+
+    X_train, X_test, y_train, y_test = train_test_split(x_dataset, y_dataset, test_size=0.3333, random_state=42)
+
+    svm_model = get_best_model(X_train, y_train)
+
+    lr_model = LogisticRegression(solver='lbfgs', multi_class='multinomial', random_state=1)
+    rf_model = RandomForestClassifier(n_estimators=100, random_state=1)
+
+    ensemble_model = VotingClassifier(estimators=[
+       ('svm', svm_model), ('lr', lr_model), ('rf', rf_model)],
+       voting='soft', weights=[1,1,1])
+
+    ensemble_model.fit(X_train, y_train)
+
+    y_pred = ensemble_model.predict(X_test)
+
+    print(str(accuracy_score(y_test, y_pred)) + '\n')
+
+    joblib.dump(ensemble_model, output_model_folder + p_output + '.joblib') 
+
+if __name__== "__main__":
+    main()

fichiersSVD/Appart1opt02/config

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+appartAopt_00900.png
+appartAopt_
+00020
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fichiersSVD/Appart1opt02/zone00/config

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+appartAopt_00900.png
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+00020
+00900
+10

fichiersSVD/Appart1opt02/zone00/seuilExpe

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fichiersSVD/Appart1opt02/zone01/config

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+appartAopt_
+00020
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fichiersSVD/Appart1opt02/zone01/seuilExpe

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fichiersSVD/Appart1opt02/zone02/config

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fichiersSVD/Appart1opt02/zone02/seuilExpe

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fichiersSVD/Appart1opt02/zone03/config

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fichiersSVD/Appart1opt02/zone03/seuilExpe

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fichiersSVD/Appart1opt02/zone04/config

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fichiersSVD/Appart1opt02/zone04/seuilExpe

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fichiersSVD/Appart1opt02/zone05/config

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fichiersSVD/Appart1opt02/zone05/seuilExpe

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fichiersSVD/Appart1opt02/zone06/config

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fichiersSVD/Appart1opt02/zone06/seuilExpe

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fichiersSVD/Appart1opt02/zone07/config

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fichiersSVD/Appart1opt02/zone07/seuilExpe

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fichiersSVD/Appart1opt02/zone08/config

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+appartAopt_00900.png
+appartAopt_
+00020
+00900
+10

fichiersSVD/Appart1opt02/zone08/seuilExpe

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fichiersSVD/Appart1opt02/zone09/config

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+appartAopt_00900.png
+appartAopt_
+00020
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fichiersSVD/Appart1opt02/zone09/seuilExpe

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fichiersSVD/Appart1opt02/zone10/config

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+00020
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fichiersSVD/Appart1opt02/zone10/seuilExpe

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