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No-code Machine Learning Cross-validation and Interpretability in techtonique.net

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I’ve added a new feature to techtonique.net: No-code Machine Learning Cross-validation and Interpretability for tabular data (supervised learning,regression and classification). techtonique.net will remain free to use until December 24, December 30, 2024. As many others have already done, give it a try!

To use this new feature, you’ll need to navigate to https://www.techtonique.net/mlcvexplain, and select a file from Techtonique/datasets. The files to be used are those with names ending with a “2”, in folders /classification or /regression, i.e tables with a training set index. This means: you can create similar files with your own data, and add a training set index as an additional column for predictive purposes.

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Once the file is uploaded, you can click on the “Submit” button to see (work in progress):

As a reminder of techtonique.net features:

You can simulate predictive scenarios using R, Python, and Excel, by using Techtonique API, available at https://www.techtonique.net. Input csv files used in the examples are available in Techtonique/datasets.

The Excel version can be found in the Excel file https://github.com/thierrymoudiki/techtonique-excel-server/VBA-Web.xlsm (in ‘Sheet3’). Behind the scenes, I’m using Visual Basic for Applications (VBA) to send requests to the API. All you need to do to see it in action is get a token and press a button. Remember to enable macros in Excel when asked to do so (this is safe).

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Here’s the Python version, which relies on forecastingapi Python package:

import forecastingapi as fapi
import numpy as np
import pandas as pd 
from time import time
import matplotlib.pyplot as plt
import ast 

# examples in https://github.com/Techtonique/datasets/tree/main/time_series        
path_to_file = '/Users/t/Documents/datasets/time_series/univariate/AirPassengers.csv' 
    
start = time() 
res_get_forecast = fapi.get_forecast(path_to_file,     
base_model="RidgeCV",
n_hidden_features=5,
lags=25,
type_pi='scp2-kde',
replications=10,
h=10)
print(f"Elapsed: {time() - start} seconds \n")

print(res_get_forecast)

# Convert lists to numpy arrays for easier handling
mean = np.asarray(ast.literal_eval(res_get_forecast['mean'])).ravel()
lower = np.asarray(ast.literal_eval(res_get_forecast['lower'])).ravel()
upper = np.asarray(ast.literal_eval(res_get_forecast['upper'])).ravel()
sims = np.asarray(ast.literal_eval(res_get_forecast['sims']))

# Plotting
plt.figure(figsize=(10, 6))

# Plot the simulated lines
for sim in sims:
    plt.plot(sim, color='gray', linestyle='--', alpha=0.6, label='Simulations' if 'Simulations' not in plt.gca().get_legend_handles_labels()[1] else "")

# Plot the mean line
plt.plot(mean, color='blue', linewidth=2, label='Mean')

# Plot the lower and upper bounds as shaded areas
plt.fill_between(range(len(mean)), lower, upper, color='lightblue', alpha=0.2, label='Confidence Interval')

# Labels and title
plt.xlabel('Time Point')
plt.ylabel('Value')
plt.title('Spaghetti Plot of Mean, Bounds, and Simulated Paths')
plt.legend()
plt.show()

The R version relies on forecastingapi R package:

path_to_file <- "/Users/t/Documents/datasets/time_series/univariate/AirPassengers.csv"
forecastingapi::get_forecast(path_to_file)
forecastingapi::get_forecast(path_to_file, type_pi='scp2-kde', h=10L, replications=10L)
sims <- forecastingapi::get_forecast(path_to_file, type_pi="scp2-kde", replications=10L)$sims
matplot(sims, type='l', lwd=2)

In addition, you can obtain insights from your tabular data by chatting with it in techtonique.net. No plotting yet (coming soon), but you can already ask questions like:

You can also run R or Python code interactively in your browser, on www.techtonique.net/consoles.

Techtonique web app is a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. As of September 2024, the tool is in its beta phase (subject to crashes) and will remain completely free to use until December 30, 2024. After registering, you will receive an email. CHECK THE SPAMS.

The tool is built on Techtonique and the powerful Python ecosystem. Both clickable web interfaces and Application Programming Interfaces (APIs, see below) are available.

Currently, the available functionalities include:

As mentioned earlier, this tool includes both clickable web interfaces and Application Programming Interfaces (APIs).
APIs allow you to send requests from your computer to perform specific tasks on given resources. APIs are programming language-agnostic (supporting Python, R, JavaScript, etc.), relatively fast, and require no additional package installation before use. This means you can keep using your preferred programming language or legacy code/tool, as long as it can speak to the internet. What are requests and resources?

In Techtonique/APIs, resources are Statistical/Machine Learning (ML) model predictions or forecasts.
A common type of request might be to obtain sales, weather, or revenue forecasts for the next five weeks. In general, requests for tasks are short, typically involving a verb and a URL path — which leads to a response.

Below is an example. In this case, the resource we want to manage is a list of users.

– Request type (verb): GET

– Request type (verb): POST

– Request type (verb): PUT

– Request type (verb): DELETE

In Techtonique/APIs, a typical resource endpoint would be /MLmodel. Since the resources are predefined and do not need to be updated (PUT) or deleted (DELETE), every request will be a POST request to a /MLmodel, with additional parameters for the ML model.
After reading this, you can proceed to the /howtoapi page.

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