MarcoGorelli

import scipy.stats
import matplotlib.pyplot as plt
import seaborn as sns

fig, ax = plt.subplots(figsize=(14, 8))
sns.kdeplot(scipy.stats.beta(292 + 1, 1000 - 292 + 1).rvs(1000), ax=ax)

MarcoGorelli

import ast
from ast import NodeVisitor
import subprocess

class Visitor(NodeVisitor):
    def __init__(self, file) -> None:
        self.file = file
    
    def visit_Call(self, node: ast.Call) -> None:
        if (

MarcoGorelli

import ast
import os
from ast import NodeVisitor
import subprocess

class Visitor(NodeVisitor):
    def __init__(self, file) -> None:
        self.file = file
    
    def visit_FunctionDef(self, node: ast.FunctionDef) -> None:

MarcoGorelli

import functools

import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import numpyro.distributions as dist
import pandas as pd
import seaborn as sns
from matplotlib import patches

MarcoGorelli

samples = distribution.sample(rng_key, sample_shape=(1000,))
pushforward_samples = jax.vmap(g)(samples)

MarcoGorelli

def inv_g(x_tilde):
    """Inverse of `g`."""
    return jnp.asarray([jax.scipy.special.logit(x_tilde[0]), jnp.log(x_tilde[1])])
  
  
x_tilde = jnp.column_stack(
    [jnp.linspace(0.001, 0.999, 1000), jnp.linspace(0.001, 3, 1000)]
)
pre_x_tilde = jax.vmap(inv_g)(x_tilde)

MarcoGorelli

fig, axes = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False)
axes = axes.flatten()

data = pd.DataFrame(samples, columns=["x_0", "x_1"])
sns.kdeplot(data=data, x="x_0", y="x_1", ax=axes[0])

data = pd.DataFrame(pushforward_samples, columns=["x_tilde_0", "x_tilde_1"])
sns.kdeplot(data=data, x="x_tilde_0", y="x_tilde_1", ax=axes[1])

xyA = [2.5, -0.6]

MarcoGorelli

import numpy as np
import pandas as pd
from lightgbm import LGBMRegressor, log_evaluation
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold

data = load_breast_cancer()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)

MarcoGorelli

import bornly as bns
import numpy as np
import pandas as pd
from pmdarima import auto_arima
from statsmodels.tsa.statespace.sarimax import SARIMAX

flights = bns.load_dataset("flights")
flights["t"] = np.arange(len(flights))
PERIOD = 12
n_steps = 12

MarcoGorelli

import sys
import subprocess
import re
import shlex
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('command')
parser.add_argument('action', choices=['pull', 'push'])
args = parser.parse_args()