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        "author": "Cyril Wendl",
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        "description": "# Density Forest \nThis library was developed within an EPFL Master Project, Spring Semester 2018.\n\nGitHub repository: https://github.com/CyrilWendl/SIE-Master\n\n\n## \ud83d\udcd6 Usage of the `DensityForest` class:\n#### Fitting a Density Forest\nSuppose you have your training data `X_train` and test data `X_test`, in `[N, D]` with `N` data points in `D` dimensions:\n\n```python\nfrom density_forest.density_forest import DensityForest\n\nclf_df = DensityForest(**params)         # create new class instance, put hyperparameters here\nclf_df.fit(X_train)                      # fit to a training set\nconf = clf_df.decision_function(X_test)  # get confidence values for test set\noutliers = clf_df.predict(X_test)        # predict whether a point is an outlier (-1 for outliers 1, for inliers)\n```\n\nHyperparameters are documented in the docstring. To find the optimal hyperparameters, consider the section below.\n\n#### Finding Hyperparameters\nTo find the optimal hyperparameters, use the `ParameterSearch` from `helpers.cross_validator`, which allows CV, and hyperparameter search.\n\n```python\nfrom helpers.cross_validator import ParameterSearch\n\n# define hyperparameters to test\ntuned_params = [{'max_depth':[2, 3, 4], 'n_trees': [10, 20]}] # optionally add non-default arguments as single-element arrays\ndefault_params = [{'verbose':0, ...}]  # other default parameters \n# do parameter search\nps = ParameterSearch(DensityForest, tuned_parameters, X_train, X_train_all, y_true_tr, f_scorer, n_iter=2, verbosity=0, n_jobs=1, default_params=default_params)\nps.fit()\n\n# get model with the best parameters, as above\nclf_df = DensityForest(**ps.best_params, **default_params)  # create new class instance with best hyperparameters\n...  # continue as above\n```\nCheck the docstrings for more detailed documentation af the `ParameterSearch` class.\n\n\n## \ud83d\uddc2 File Structure\n\n### \ud83d\udc7e Code\nAll libraries for density forests, helper libraries for semantic segmentation and for baselines. \n#### `density_forest/`\nPackage for implementation of Decision Trees, Random Forests, Density Trees and Density Forests\n- `create_data.py`: functions for generating labelled and unlabelled data\n- `decision_tree.py`: data structure for decision tree nodes\n- `decision_tree_create.py`: functions for generating decision trees\n- `decision_tree_traverse.py`: functions for traversing a decision tree and predicting labels\n- `density_forest.py`: functions for creating density forests\n- `density_tree.py`: data struture for density tree nodes\n- `density_tree_create.py`: functions for generating a density tree\n- `density_tree_traverse.py`: functions for descending a density tree and retrieving its cluster parameters\n- `helper.py`: various helper functions\n- `random_forests.py`: functions for creating random forests\n\n#### `helpers/`: \nGeneral helpers library for semantic segmentation\n- `data_augment.py`: custom data augmentation methods applied to both the image and the ground truth\n- `data_loader.py`: PyTorch data loader for Zurich dataset\n- `helpers.py`: functions for importing, cropping, padding images and other related image tranformations\n- `parameter_search.py`: functions for finding optimal hyperparameters for Density Forest, OC-SVM and GMM (explained above)\n- `plots.py`:  Generic plotter functions for labelled and unlabelled 2D and 3D plots, used for t-SNE and PCA plots\n\n#### `baselines/`:\nHelper functions for confidence estimation baselines MSR, margin, entropy and MC-Dropout\n\n#### `keras_helpers/`\nHelper functions for Keras\n- `helpers.py`: get activations\n- `callbacks.py`: callbacks to be evaluated after each epoch\n- `unet.py`: UNET model for training of network on Zurich dataset\n\n### \ud83d\uddfe Visualizations\n#### `density_forest/`: \nVisualizations of basic decision tree and density tree\n- `Decision Forest.ipynb`: Decision Trees and Random Forest on randomly generated labelled data\n- `Density Forest.ipynb`: Density Trees on randomly generated unlabelled data\n\n## \ud83c\udf93 Supervisors:\n- Prof. Devis Tuia, University of Wageningen\n- Diego Marcos Gonz\u00e1lez, University of Wageningen\n- Prof. Fran\u00e7ois Golay, EPFL\n\nCyril Wendl, 2018\n\n\n",
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