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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 notS3
, 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")
- colored graphics for
Python
classMTS
# !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") obj_MTS3.plot("heater") obj_MTS3.plot("ice cream")
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