jacopotagliabue

import bauplan
import boto3
from datetime import datetime

def main():
    try:
        # Initialize clients
        bpln = bauplan.Client()
        username = bpln.info().user.username
        

jacopotagliabue

from typing import List, Optional, Set

class Column:
    def __init__(self, name: str):
        self.name = name

    def __repr__(self):
        return f"Column(name={self.name})"

jacopotagliabue

"""

Standalone script to write the same dataframe in different storage formats and then
measure the time it takes to read it into the user process.

Setup:

* create a virtualenv with pyarrow==17.0.0, boto3==1.35.7, posix-ipc==1.1.1
* make sure you have an S3 bucket and appropriate AWS credentials to write / read from it

jacopotagliabue

SELECT
  c_count,
  count(*) AS custdist
FROM (
  SELECT
    c_custkey,
    count(o_orderkey)
  FROM
    read_parquet('s3://bucket/customer.parquet') AS customer
  LEFT OUTER JOIN

jacopotagliabue

from reclist.datasets import CoveoDataset
from reclist.recommenders.prod2vec import CoveoP2VRecModel
from reclist.reclist import CoveoCartRecList

if __name__ == "__main__":

    # get the coveo data challenge dataset as a RecDataset object
    coveo_dataset = CoveoDataset()

    # re-use a skip-gram model from reclist to train a latent product space, to be used

jacopotagliabue

class MyRecList(RecList):

    @rec_test(test_type='stats')
    def basic_stats(self):
        """
        Basic statistics on training, test and prediction data
        """
        from reclist.metrics.standard_metrics import statistics
        return statistics(self._x_train,
            self._y_train,

jacopotagliabue

// max expression length (i.e. how many atomic facts can be considered)
var MAX_EXP_LENGTH = 5;

var TARGET_EXP = {
           'q': 1000000000,
           'd': 'target',
           'u': '$'
    }

var topK = 100;

jacopotagliabue

// specify how much data to use
var N = 25; // you can try 25, 50, 100
var dataType = 'exp'; // linear, constant, exp

///fold: load observed data

print("Using " + N + " data points for data type: " + dataType);
var dataString = {
                    'exp': '[{"x": 8.528104691935328, "y": 369.4385603078311}, {"x": 5.800314416734446, "y": 56.17361673332166}, {"x": 6.957475968211479, "y": 124.0710506473754}, {"x": 9.481786398402916, "y": 715.0458977557804}, {"x": 8.735115980299934, "y": 426.23403384951274}, {"x": 3.045444240247178, "y": 8.356747238145411}, {"x": 6.9001768350511785, "y": 119.71324924833438}, {"x": 4.697285583404605, "y": 25.034180007597616}, {"x": 4.793562296412884, "y": 27.708924544629504}, {"x": 5.821197003876745, "y": 56.65940024103008}, {"x": 5.288087142321756, "y": 38.57248324353289}, {"x": 7.908547013925951, "y": 241.1127051063566}, {"x": 6.522075450293987, "y": 91.98603466343532}, {"x": 5.243350032985656, "y": 38.66132447348228}, {"x": 5.887726465490851, "y": 58.81299495311647}, {"x": 5.

jacopotagliabue

var unaryOps = ['log', 'round', 'sqrt'];
var binaryOps = ['add', 'sub', 'mul', 'div'];
var constants = ['x', 'z', 'u', 'q'];

var randomNumber = function() {
  return [uniformDraw(_.range(10))];
}

var randomConstant = function() {
  return [uniformDraw(constants)];

jacopotagliabue

var drawLines = function(drawObj, lines, strokeW){
  var line = lines[0];
  drawObj.line(line[0], line[1], line[2], line[3], strokeW);
  if (lines.length > 1) {
    drawLines(drawObj, lines.slice(1), strokeW);
  }
}
 
var drawSamples = function(sam){
  drawLines(Draw(50, 50, true), sam);