chemicalchecker.util.splitter.neighborpair.NeighborPairTraintest

class NeighborPairTraintest(hdf5_file, split, nr_neig=10, replace_nan=None)

Bases: object

NeighborPairTraintest class.

Initialize a NeighborPairTraintest instnace.

We assume the file is containing diffrent splits. e.g. “x_train”, “y_train”, “x_test”, …

Methods

close	Close the HDF5.
create	Create the HDF5 file with validation splits.
generator_fn	Return the generator function that we can query for batches.
get_all_p	Get full X.
get_all_x	Get full X.
get_all_y	Get full Y.
get_p	Get a batch of X.
get_py	Get a batch of X and Y.
get_py_shapes	Return the shpaes of X an Y.
get_split_indeces	Get random indeces for different splits.
get_split_names	Return the name of the splits.
get_xy_shapes	Return the shpaes of X an Y.
get_y	Get a batch of Y.
open	Open the HDF5.

close()

Close the HDF5.

static create(X, out_file, neigbors_matrix=None, pos_neighbors=10, neg_neighbors=100, scaler_dest=None, mean_center_x=True, shuffle=True, check_distances=True, split_names=['train', 'test'], split_fractions=[0.8, 0.2], x_dtype=<class 'numpy.float32'>, y_dtype=<class 'numpy.float32'>, debug_test=False)

Create the HDF5 file with validation splits.

Parameters:

• X (numpy.ndarray) – features to train from.

• out_file (str) – path of the h5 file to write.

• neigbors_matrix (numpy.ndarray) – matrix for computing neighbors.

• neigbors (int) – Number of positive neighbors to include.

• mean_center_x (bool) – center each feature on its mean?

• shuffle (bool) – Shuffle positive and negatives.

• split_names (list(str)) – names for the split of data.

• split_fractions (list(float)) – fraction of data in each split.

• x_dtype (type) – numpy data type for X.

• y_dtype (type) – numpy data type for Y (np.float32 for regression, np.int32 for classification.

static generator_fn(file_name, split, batch_size=None, only_x=False, replace_nan=None, augment_scale=1, augment_fn=None, augment_kwargs={}, mask_fn=None, shuffle=True, sharedx=None)

Return the generator function that we can query for batches.

file_name(str): The H5 generated via create split(str): One of ‘train_train’, ‘train_test’, or ‘test_test’ batch_size(int): Size of a batch of data. only_x(bool): Usually when predicting only X are useful. replace_nan(bool): Value used for NaN replacement. augment_scale(int): Augment the train size by this factor. augment_fn(func): Function to augment data. augment_kwargs(dict): Parameters for the aument functions.

get_all_p()

Get full X.

get_all_x()

Get full X.

get_all_y()

Get full Y.

get_p(beg_idx, end_idx)

Get a batch of X.

get_py(beg_idx, end_idx)

Get a batch of X and Y.

get_py_shapes()

Return the shpaes of X an Y.

static get_split_indeces(rows, fractions)

Get random indeces for different splits.

get_split_names()

Return the name of the splits.

get_xy_shapes()

Return the shpaes of X an Y.

get_y(beg_idx, end_idx)

Get a batch of Y.

open()

Open the HDF5.