`prevision_quantum_nn.dataset.dataset`¶

Dataset module

contains the classes to handle datasets

Module Contents¶

Classes¶

DataSet.

class prevision_quantum_nn.dataset.dataset.DataSet¶

DataSet.

Abstraction to handle user data

num_features¶

number of features in the dataset

Type: int

num_categories¶

number of categories in the case of multiclassification

Type: int

from_numpy(self, train_features, train_labels, val_features=None, val_labels=None)¶

Creates a DataSet from a set of numpy arrays.

Parameters

train_features (numpy array) – array of features for the training phase
train_labels (numpy array) – array of labels for the training phase
val_features (numpy array) – array of features for the validation phase
val_labels (numpy array) – array of labels for the validation phase

Returns

DataSet

Return type

a dataset

from_pandas(self, train_data_frame, targets, val_data_frame)¶

Creates a DataSet from a set of pandas dataframes.

Parameters

train_data_frame (pandas DataFrame) – dataframe for the training phase
targets (list) – list of targets columns in input data frames
val_data_frame (pandas DataFrame) – dataframe for the validation phase

Returns

DataSet

Return type

a dataset

to_numpy(self)¶

Returns the dataset in the numpy format.

Returns

numpy array: training features
self.train_labels: numpy array: training labels
self.val_features: numpy array: validation features
self.val_labels: numpy array: validation labels

Return type

self.train_features