ThomasParistech

import numpy as np
from scipy.stats import norm

def weighted_kde(x_data: np.ndarray, x_prediction: np.ndarray) -> np.ndarray:
    h = silverman_bandwidth(x_data)  # Required to evaluate CDF
    area_values = norm.cdf(1.0, x_prediction, h) - norm.cdf(0.0, x_prediction, h)
    basic_densities = basic_kde(x_data, x_prediction, h)
    return basic_densities / area_values

ThomasParistech

import numpy as np

def reflective_kde(x_data: np.ndarray, x_prediction: np.ndarray) -> np.ndarray:
    h = silverman_bandwidth(x_data)  # Compute before adding reflected data
    x_data_augmented = np.stack((-x_data, x_data, 2-x_data))
    reflective_densities = basic_kde(x_data_augmented, x_prediction, h)
    # Discard left and right reflected samples and normalize density by 1/3
    return 3 * reflective_densities 

ThomasParistech

import numpy as np
from scipy.special import logit

def transformed_kde(x_data: np.ndarray, x_prediction: np.ndarray) -> np.ndarray:
    x_data_logit = logit(x_data)
    x_prediction_logit = logit(x_prediction)
    densities_logit = basic_kde(x_data_logit, x_prediction_logit)
    return densities_logit / (x_prediction * (1.0-x_prediction))

ThomasParistech

import numpy as np
from scipy.stats import norm


def silverman_bandwidth(x_data: np.ndarray) -> float:
    return (4/(3*x_data.shape[0]))**0.2 * np.std(x_data)


def basic_kde(x_data: np.ndarray, x_prediction: np.ndarray) -> np.ndarray:
    """Perform Gaussian Kernel Density Estimation.

ThomasParistech

import time
from multiprocessing.sharedctypes import Synchronized
from multiprocessing.synchronize import Event as EventClass

import nvidia_smi  # nvidia-ml-py3

from medium_utils import get_pid_ram_used_bytes
from medium_utils import get_total_ram_available_bytes
from medium_utils import get_total_ram_used_bytes

ThomasParistech

import inspect
import os
from functools import wraps
from typing import Any
from typing import Callable
from typing import cast
from typing import TypeVar


def get_function_name(func: Callable) -> str:

ThomasParistech

import os
from multiprocessing import Event
from multiprocessing import Process
from multiprocessing import Value
from multiprocessing.sharedctypes import Synchronized
from typing import Optional


class MemoryScope:
    """Use as context `with MemoryScope(name):`."""

ThomasParistech

def bytes2human(number: int, decimal_unit: bool = True) -> str:
    """Convert number of bytes in a human readable string.

    >>> bytes2human(10000, True)
    '10.00 KB'
    >>> bytes2human(10000, False)
    '9.77 KiB'

    Args:
        number (int): Number of bytes

ThomasParistech

from functools import lru_cache
import nvidia_smi


@lru_cache()
def is_gpu_available() -> bool:
    """Check if nvidia-smi is available."""
    try:
        nvidia_smi.nvmlInit()
        nvidia_smi.nvmlShutdown()

ThomasParistech

import nvidia_smi


nvidia_smi.nvmlInit()

assert nvidia_smi.nvmlDeviceGetCount() == 1  # Assume a single GPU
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
gpu_info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
total_vram = int(gpu_info.total)
used_vram = int(gpu_info.used)