Pipelines regroup
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@ -66,13 +66,16 @@ class ManualFeatureSelector(TransformerMixin):
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# =-=-=-=-=-=-= Pipelines =-=-=-=-=-=-=
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# - This pipeline select usefull features -
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pipeline_features_selection = Pipeline([
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('features_remap', ManualFeatureSelector()),
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])
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#pipeline_features_selection = Pipeline([
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# ('features_remap', ManualFeatureSelector()),
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#])
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# - This pipeline use the imputer and scales the values -
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# -- Tried to regroup both pipelines, seems to work for now ... --
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# -- TODO If no issues, don't forget to remove the upper commented pipeline
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pipeline = Pipeline([
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('features_remap', ManualFeatureSelector()),
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('imputer', SimpleImputer(strategy="median")),
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('std_scaler', StandardScaler()),
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])
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@ -95,7 +98,8 @@ saved_model = joblib.load("models/malware_classifier_1.pkl")
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# - Use np.array([['info1', 'info2', 'infox']]) to build the 'line' variable -
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def predict_one_line(model,line):
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X_unknown = full_pipeline(line)
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#X_unknown = full_pipeline(line)
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X_unknown = pipeline.fit_transform(line)
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X_unknown_columns = ['Characteristics', 'DllCharacteristics', 'SectionsMaxEntropy', 'MajorSubsystemVersion', 'Machine', 'Subsystem', 'ResourcesMaxEntropy', 'ResourcesMinEntropy', 'VersionInformationSize', ' MajorOperatingSystemVersion', 'ResourcesMeanEntropy', 'SectionsMeanEntropy']
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X_unknown = pd.DataFrame(X_unknown, columns=X_unknown_columns)
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ans = model.predict(X_unknown)
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