*Fork of*: SUOD: Accelerating Large-scare Unsupervised Heterogeneous Outlier Detection
===========================================================================
Please refer to the [original package](https://github.com/yzhao062/SUOD) for more information about the base functionalities.
This fork forces SUOD to use pre-selected axis-parallel subspaces, such as those obtained after Feature Bagging or Feature Selection. These subspaces must be declared as a `np.array`, and can take any structure such that the operation `X[:, subspace]` yields the desired projected dataset.
It uses the same class declaration as base SUOD, only adding a new variable: `subspaces`, and changing the class name to sel_SUOD.
This fork additionally contains a number of QOL additions, like:
- During initialization, if base_estimators is an array of length 1, it will sklearn.clone() the estimator once per each subspace.
- During initialization, it will automatically check whether the number of detectors and estimators coincide.
- It will, by default, not run approximation on any method unless the global flag for approximation is manually turned to true.
There should be no conflict between SUOD and sel_SUOD.
Take a look at the following code for a practical example:
```
base_estimators = [LOF()] #The class sel_SUOD automatically initizializes itself with subspaces.shape[0] clones of this array if len < 2.
#Creating exemplary subspaces
subspaces = [True]*20
subspaces.append(False)
subspaces = np.array([subspaces, subspaces])
subspaces[1][4] = False
model = sel_SUOD(base_estimators=base_estimators, subspaces=subspaces,
n_jobs=6, bps_flag=True,
contamination=contamination, approx_flag_global=True)
model.fit(X_train) # fit all models with X
predicted_scores = model.decision_function(X_test) # predict scores
```
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"description": "\n*Fork of*: SUOD: Accelerating Large-scare Unsupervised Heterogeneous Outlier Detection\n===========================================================================\n\n\nPlease refer to the [original package](https://github.com/yzhao062/SUOD) for more information about the base functionalities.\nThis fork forces SUOD to use pre-selected axis-parallel subspaces, such as those obtained after Feature Bagging or Feature Selection. These subspaces must be declared as a `np.array`, and can take any structure such that the operation `X[:, subspace]` yields the desired projected dataset. \nIt uses the same class declaration as base SUOD, only adding a new variable: `subspaces`, and changing the class name to sel_SUOD.\nThis fork additionally contains a number of QOL additions, like:\n\n - During initialization, if base_estimators is an array of length 1, it will sklearn.clone() the estimator once per each subspace.\n - During initialization, it will automatically check whether the number of detectors and estimators coincide. \n - It will, by default, not run approximation on any method unless the global flag for approximation is manually turned to true.\n\nThere should be no conflict between SUOD and sel_SUOD.\nTake a look at the following code for a practical example: \n\n```\nbase_estimators = [LOF()] #The class sel_SUOD automatically initizializes itself with subspaces.shape[0] clones of this array if len < 2.\n\n#Creating exemplary subspaces\nsubspaces = [True]*20\nsubspaces.append(False)\nsubspaces = np.array([subspaces, subspaces])\nsubspaces[1][4] = False\n\nmodel = sel_SUOD(base_estimators=base_estimators, subspaces=subspaces,\n n_jobs=6, bps_flag=True,\n contamination=contamination, approx_flag_global=True)\nmodel.fit(X_train) # fit all models with X\npredicted_scores = model.decision_function(X_test) # predict scores\n```\n",
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