Version v0.14.0 of nnetsauce for R and Python

Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization

Version v0.14.0 of nnetsauce is now available for R (hopefully a rapid installation) and Python on GitHub, PyPI and conda. It’s been mainly tested on Linux and macOS. For Windows users, you can try to install of course, but if it doesn’t work, please use WSL2.

NEWS

update and align as much as possible with R version (new plotting function for multivariate time series (MTS), plot.MTS, is not S3, but it’s complicated)

# 0 - install packages ----------------------------------------------------

#utils::install.packages("remotes")
remotes::install_github("Techtonique/nnetsauce/R-package", force = TRUE)

# 1 - ENET simulations ----------------------------------------------------

obj <- nnetsauce::sklearn$linear_model$ElasticNet()
obj2 <- nnetsauce::MTS(obj, 
                       start_input = start(fpp::vn), 
                       frequency_input = frequency(fpp::vn),
                       kernel = "gaussian", replications = 100L)
X <- data.frame(fpp::vn)
obj2$fit(X)
obj2$predict(h = 10L)
typeof(obj2)

par(mfrow=c(2, 2))
plot.MTS(obj2, selected_series = "Sydney")
plot.MTS(obj2, selected_series = "Melbourne")
plot.MTS(obj2, selected_series = "NSW")
plot.MTS(obj2, selected_series = "BrisbaneGC")

# 2 - Bayesian Ridge ----------------------------------------------------

obj <- nnetsauce::sklearn$linear_model$BayesianRidge()
obj2 <- nnetsauce::MTS(obj,
                       start_input = start(fpp::vn), 
                       frequency_input = frequency(fpp::vn))
X <- data.frame(fpp::vn)
obj2$fit(X)
obj2$predict(h = 10L, return_std = TRUE)

par(mfrow=c(2, 2))
plot.MTS(obj2, selected_series = "Sydney")
plot.MTS(obj2, selected_series = "Melbourne")
plot.MTS(obj2, selected_series = "NSW")
plot.MTS(obj2, selected_series = "BrisbaneGC")

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colored graphics for Python class MTS

# !pip install nnetsauce —upgrade

import nnetsauce as ns
import numpy as np
import pandas as pd
from sklearn.linear_model import Ridge, BayesianRidge
from sklearn.ensemble import RandomForestRegressor
from time import time

url = "https://raw.githubusercontent.com/thierrymoudiki/mts-data/master/heater-ice-cream/ice_cream_vs_heater.csv"

df = pd.read_csv(url)

# ice cream vs heater (I don't own the copyright)
df.set_index('Month', inplace=True)
df.index.rename('date')

df = df.pct_change().dropna()

idx_train = int(df.shape[0]*0.8)
idx_end = df.shape[0]
df_train = df.iloc[0:idx_train,]

regr3 = Ridge()
obj_MTS3 = ns.MTS(regr3, lags = 4, n_hidden_features=7, #IRL, must be tuned
                  replications=50, kernel='gaussian',
                  seed=24, verbose = 1)
start = time()
obj_MTS3.fit(df_train)
print(f"Elapsed {time()-start} s")

# predict 
obj_MTS3.predict()

obj_MTS3.plot("heater")
obj_MTS3.plot("ice cream")

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