zeekay
e037e8f7ea
The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
2.8 KiB
module torus.ml.common.check_inputs
Check and conversion tools.
Utils that are used to check (including convert) some data types which are compatible with scikit-learn to numpy types.
function check_array_and_assert
check_array_and_assert(X, *args, **kwargs)
sklearn.utils.check_array with an assert.
Equivalent of sklearn.utils.check_array, with a final assert that the type is one which is supported by Torus ML.
Args:
X(object): Input object to check / convert*args: The arguments to pass to check_array**kwargs: The keyword arguments to pass to check_array
Returns: The converted and validated array
function check_X_y_and_assert
check_X_y_and_assert(X, y, *args, **kwargs)
sklearn.utils.check_X_y with an assert.
Equivalent of sklearn.utils.check_X_y, with a final assert that the type is one which is supported by Torus ML.
Args:
X(ndarray, list, sparse matrix): Input datay(ndarray, list, sparse matrix): Labels*args: The arguments to pass to check_X_y**kwargs: The keyword arguments to pass to check_X_y
Returns: The converted and validated arrays
function check_X_y_and_assert_multi_output
check_X_y_and_assert_multi_output(X, y, *args, **kwargs)
sklearn.utils.check_X_y with an assert and multi-output handling.
Equivalent of sklearn.utils.check_X_y, with a final assert that the type is one which is supported by Torus ML. If y is 2D, allows multi-output.
Args:
X(ndarray, list, sparse matrix): Input datay(ndarray, list, sparse matrix): Labels*args: The arguments to pass to check_X_y**kwargs: The keyword arguments to pass to check_X_y
Returns: The converted and validated arrays with multi-output targets.