pjankiewicz

#![allow(dead_code)]

use std::collections::HashMap;

#[derive(PartialEq)]
struct Click {
    item_id: String,
}

#[derive(PartialEq)]

pjankiewicz

import numpy as np
import pandas as pd
from ludwig import LudwigModel
from sklearn.model_selection import train_test_split

df = pd.read_csv("input/train.tsv", sep="\t")
df["log_price"] = np.log1p(df["price"])

model = LudwigModel({
    "input_features": [

pjankiewicz

import os;

os.environ['OMP_NUM_THREADS'] = '1'
from contextlib import contextmanager
from functools import partial
from operator import itemgetter
from multiprocessing.pool import ThreadPool
import time
from typing import List, Dict

pjankiewicz

Flow Field, Step 1

I'm building a flow field

Inspired by Coding Challenge #24: Perlin Noise Flow Field by Daniel Shiffman: https://www.youtube.com/watch?v=BjoM9oKOAKY

Using noise lib by Seph Gentle: https://github.com/josephg/noisejs (put in another Pen and linked to by this Pen).

A Pen by Paweł Jankiewicz on CodePen.

pjankiewicz

import gzip
import base64
import os
import sys
sys.path.append('/kaggle/working')

# this is base64 encoded source code
file_data = {...}

os.system('mkdir mercari')

pjankiewicz

import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin, clone
from collections import defaultdict
 
 
class HClassifier(BaseEstimator, ClassifierMixin):
    ROOT = object()
 
    def __init__(self, base_estimator, min_obs=None, max_level=None):
        self.base_estimator = base_estimator

pjankiewicz

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

pjankiewicz

package main

import (
	"fmt"
	"net/http"
)

type storageData struct {
	data   string
}

pjankiewicz

#dataX - variables
#datay - labels

rows = dataX.shape[0]

out_of_fold_predictions = zeros(rows) # this is our out of fold prediction vector

for tr, te in cross_validation(10):
    model = fit(dataX[tr,], datay[tr])

pjankiewicz

; this is a comment

; this is a double function
(defn doublen [x] (* x 2))

; basic collections (lists, vectors, maps)
(def a-list '(1 2 3))
(def a-vector [1 2 3])
(def a-map {:key1 val1 :key2 val2})