eliorc

import numpy as np
from scipy import stats


def permutation_t_test(sample1: list, sample2: list, n: int =10000) -> float:
    """
    Conduct a permutation T-test using n iterations and p-value. The higher the p-value, the more likely that sample1 
    and sample2 are sampled from the same distribution 

    :param sample1: Sample of continuous observations

eliorc

from itertools import count

import pandas as pd
import numpy as np

def cyclical_encoder(data: pd.DataFrame, 
                     time_column: str, 
                     time_unit: str, 
                     normalize_val: float, 
                     label_suffix: str) -> pd.DataFrame:

eliorc

import tensorflow as tf


class LayerNormalization(tf.keras.layers.Layer):
    """
    Apply layer normalization


    Arguments
    ---------

eliorc

import tensorflow as tf

from tavolo.normalization import LayerNormalization


def test_shapes():
    """ Test input-output shapes """

    # Inputs shape
    input_shape_2d = (56, 10)

eliorc

class DynamicMetaEmbedding(tf.keras.layers.Layer):
    def __init__(self,
                 embedding_matrices: List[tf.keras.layers.Embedding],
                 output_dim: Optional[int] = None,
                 name: str = 'dynamic_meta_embedding',
                 **kwargs):
        """
        :param embedding_matrices: List of embedding layers
        :param output_dim: Dimension of the output embedding
        :param name: Layer name

eliorc

class ContextualDynamicMetaEmbedding(tf.keras.layers.Layer):
    def __init__(self,
                 embedding_matrices: List[tf.keras.layers.Embedding],
                 output_dim: Optional[int] = None,
                 n_lstm_units: int = 2,
                 name: str = 'contextual_dynamic_meta_embedding',
                 **kwargs):
        """
        :param embedding_matrices: List of embedding layers
        :param n_lstm_units: Number of units in each LSTM, (notated as `m` in the original article)

eliorc

class DataMapCallback(tf.keras.callbacks.Callback):
    """
    Gather training dynamics for data map generation. Assumes a binary or multi-class model, no support for multi label.

    Arguments
    ---------

    - `dataset` (``tf.data.: Dataset``): Usually, as the paper suggests, this is the training dataset. It should be:

        1. Non-shuffled, so each iteration over the dataset should yield samples in the same order

eliorc

#!/usr/bin/env sh
# Add current folder to PYTHONPATH
CURRENT_FOLDER=$(pwd)
SITE_PACKAGES_FOLDER="$(ls -d $(poetry env info -p)/lib/python*/site-packages/)project_dir.pth"
echo "$CURRENT_FOLDER" > "$SITE_PACKAGES_FOLDER"

eliorc

# Extracted using: $ unzip -p lib/pycharm.jar com/jetbrains/python/PyBundle.properties | grep -B1 INSP.NAME | grep '^#' | sed 's|Inspection||g' | sed -e 's|#\s\{,1\}|# noinspection |'

# noinspection PyPep8
# noinspection PyPep8Naming
# noinspection PyTypeChecker
# noinspection PyAbstractClass
# noinspection PyArgumentEqualDefault
# noinspection PyArgumentList
# noinspection PyAssignmentToLoopOrWithParameter
# noinspection PyAttributeOutsideInit

eliorc

# Based of Kedro's code, from this example https://docs.kedro.org/en/stable/data/how_to_create_a_custom_dataset.html#the-complete-example
import re
import typing as t
from contextlib import contextmanager
from pathlib import PurePath, PurePosixPath
from urllib.parse import urlsplit

import fsspec

CLOUD_PROTOCOLS = ("s3", "s3n", "s3a", "gcs", "gs", "adl", "abfs", "abfss", "gdrive")