adannealing 0.1.32

A package doing simulated and quantum annealing

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AdAnnealing

A package doing simulated annealing

Installation

git clone https://github.com/pcotteadvestis/adannealing
cd adannealing
pip install .

Usage

Simple usage :

from adannealing import Annealer

def loss_func_2d(w) -> float:
    x = w[0]
    y = w[1]
    return (x - 5) * (x - 2) * (x - 1) * x + 10 * y ** 2

init_states, bounds, acceptance = (3.0, 0.5), ((0, 5), (-1, 1)), 0.01

ann = Annealer(
    loss=loss_func_2d,
    weights_step_size=0.1,
    init_states=init_states,  # Optionnal
    bounds=bounds,
    verbose=True
)

# Weights of local minimum, and loss at local minimum
w0, lmin, _, _, _, _ = ann.fit(stopping_limit=acceptance)

Use multiple initial states in parallel runs and get one output per init states :

from adannealing import Annealer

Annealer.set_parallel()


def loss_func_2d(w) -> float:
    x = w[0]
    y = w[1]
    return (x - 5) * (x - 2) * (x - 1) * x + 10 * y ** 2

bounds, acceptance, n = ((0, 5), (-1, 1)), 0.01, 5

ann = Annealer(
    loss=loss_func_2d,
    weights_step_size=0.1,
    bounds=bounds,
    verbose=True
)

# Iterable of n weights of local minimum and loss at local minimum
results = ann.fit(npoints=n, stopping_limit=acceptance)
for w0, lmin, _, _, _, _ in results:
    """do something"""

Use multiple initial states in parallel runs and get the result with the smallest loss :

from adannealing import Annealer

Annealer.set_parallel()


def loss_func_2d(w) -> float:
    x = w[0]
    y = w[1]
    return (x - 5) * (x - 2) * (x - 1) * x + 10 * y ** 2


bounds, acceptance, n = ((0, 5), (-1, 1)), 0.01, 5

ann = Annealer(
    loss=loss_func_2d,
    weights_step_size=0.1,
    bounds=bounds,
    verbose=True
)

# Weights of the best local minimum and loss at the best local minimum
w0, lmin, _, _, _, _ = ann.fit(npoints=n, stopping_limit=acceptance, stop_at_first_found=True)

One can save the history of the learning by giving a path :

from adannealing import Annealer

Annealer.set_parallel()


def loss_func_2d(w) -> float:
    x = w[0]
    y = w[1]
    return (x - 5) * (x - 2) * (x - 1) * x + 10 * y ** 2


bounds, acceptance, n = ((0, 5), (-1, 1)), 0.01, 5

ann = Annealer(
    loss=loss_func_2d,
    weights_step_size=0.1,
    bounds=bounds,
    verbose=True
)

# Weights of the best local minimum and loss at the best local minimum
w0, lmin, _, _, _, _ = ann.fit(
    npoints=n,
    stopping_limit=acceptance,
    history_path="logs"
)

In this example, calling fit will produce n directories in logs, each containing 2 files: history.csv and returns.csv. The first is the entier history of the fit, the second is only the iteration that found the local minimum. If only one point is asked (either by using npoints=1 or stop_at_first_found=True), will produce history.csv and returns.csv directly in logs, and will delete the subfolders of the runs that did not produce the local minimum.

One can plot the result of a fit by doing

from adannealing import plot

# figure will be saved in logs/annealing.pdf
fig = plot("logs", nweights=2, weights_names=["A", "B", "C"])