For an array of label predictions and the respective true labels, shows confusion matrix, accuracy, recall, precision etc: Input:

Build mini-batch tensors from a list of (X, mask) tuples. Mask input. Create Args: data: len(batch_size) list of tuples (X, y).

Build mini-batch tensors from a list of (X, mask) tuples. Mask input. Create Args: data: len(batch_size) list of tuples (X, y).

Build mini-batch tensors from a list of (X, mask) tuples. Mask input. Create Args: data: len(batch_size) list of tuples (X, mask).

Compensate feature vectors after masking values, in a way that the matrix product W @ X would not be affected on average. If p is the proport

Args: metrics: list of len(num_batches), each element: list of len(num_metrics), each element: (num_active_in_batch,) metric per element

Perform a single, one-off evaluation on an evaluator object (initialized with a dataset)

Evaluate forecasts using M4 test dataset. :param forecast: Forecasts. Shape: timeseries, time. :return: sMAPE and OWA groupe

Returns a string of a report for given metric arrays (array length equals the number of classes). Called internally by `analyze_class

Get average recall and precision, using class frequencies as weights, optionally excluding specified classes

Wraps a list of metric functions, i.e precison or recall, by ingoring predictions under the specified threshold for a specific class.

Loads datasets from csv files contained in `root_dir` into a dataframe, optionally choosing from `pattern` Args: root_dir

Loads datasets from csv files contained in `root_dir` into a dataframe, optionally choosing from `pattern` Args: root_dir

Loads datasets from csv files contained in `root_dir` into a dataframe, optionally choosing from `pattern` Args: root_dir

Loads datasets from csv files contained in `root_path` into a dataframe, optionally choosing from `pattern` Args: root_pa

Loads datasets from csv files contained in `root_path` into a dataframe, optionally choosing from `pattern` Args: root_pa

Loads datasets from csv files contained in `root_path` into a dataframe, optionally choosing from `pattern` Args: root_pa

Creates a random boolean mask of the same shape as X, with 0s at places where a feature should be masked. Args: X: (seq_length, feat_

Args: df: input dataframe Returns: df: normalized dataframe

Args: df: input dataframe Returns: df: normalized dataframe

Used to mask padded positions: creates a (batch_size, max_len) boolean mask from a tensor of sequence lengths, where 1 means keep element at

Used to mask padded positions: creates a (batch_size, max_len) boolean mask from a tensor of sequence lengths, where 1 means keep element at

Used to mask padded positions: creates a (batch_size, max_len) boolean mask from a tensor of sequence lengths, where 1 means keep element at

Make a histogram with the distribution of classes with respect to precision and recall

Prepare training session: read configuration from file (takes precedence), create directories. Input: args: arguments object from argparse

Splits dataset (i.e. the global datasets indices) into a test set and a training/validation set. The training/validation set is used to produ

Input: test_ratio: ratio of test set with respect to the entire dataset. Should result in an absolute number of s

Returns: train_indices: iterable of n_splits (num. of folds) numpy arrays, each array containing the global datas

If a given Series is longer than `limit`, returns subsampled sequence by the specified integer factor

SpeedUp version of Autocorrelation (a batch-normalization style design) This is for the inference phase.

SpeedUp version of Autocorrelation (a batch-normalization style design) This is for the training phase.

Returns a list of time features that will be appropriate for the given frequency string. Parameters ---------- freq_str Frequ

Returns a list of time features that will be appropriate for the given frequency string. Parameters ---------- freq_str Frequ

Returns a list of time features that will be appropriate for the given frequency string. Parameters ---------- freq_str Frequ

Returns a list of time features that will be appropriate for the given frequency string. Parameters ---------- freq_str Frequ

Returns a list of time features that will be appropriate for the given frequency string. Parameters ---------- freq_str Frequ

Returns a list of time features that will be appropriate for the given frequency string. Parameters ---------- freq_str Frequ