if in dropdown
35 | if (pos >= 0) {
36 | var menu_anchor = $(links[pos]);
37 | menu_anchor.parent().addClass("active");
38 | menu_anchor.closest("li.dropdown").addClass("active");
39 | }
40 | });
41 |
42 | function paths(pathname) {
43 | var pieces = pathname.split("/");
44 | pieces.shift(); // always starts with /
45 |
46 | var end = pieces[pieces.length - 1];
47 | if (end === "index.html" || end === "")
48 | pieces.pop();
49 | return(pieces);
50 | }
51 |
52 | // Returns -1 if not found
53 | function prefix_length(needle, haystack) {
54 | if (needle.length > haystack.length)
55 | return(-1);
56 |
57 | // Special case for length-0 haystack, since for loop won't run
58 | if (haystack.length === 0) {
59 | return(needle.length === 0 ? 0 : -1);
60 | }
61 |
62 | for (var i = 0; i < haystack.length; i++) {
63 | if (needle[i] != haystack[i])
64 | return(i);
65 | }
66 |
67 | return(haystack.length);
68 | }
69 |
70 | /* Clipboard --------------------------*/
71 |
72 | function changeTooltipMessage(element, msg) {
73 | var tooltipOriginalTitle=element.getAttribute('data-original-title');
74 | element.setAttribute('data-original-title', msg);
75 | $(element).tooltip('show');
76 | element.setAttribute('data-original-title', tooltipOriginalTitle);
77 | }
78 |
79 | if(ClipboardJS.isSupported()) {
80 | $(document).ready(function() {
81 | var copyButton = "";
82 |
83 | $("div.sourceCode").addClass("hasCopyButton");
84 |
85 | // Insert copy buttons:
86 | $(copyButton).prependTo(".hasCopyButton");
87 |
88 | // Initialize tooltips:
89 | $('.btn-copy-ex').tooltip({container: 'body'});
90 |
91 | // Initialize clipboard:
92 | var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
93 | text: function(trigger) {
94 | return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
95 | }
96 | });
97 |
98 | clipboardBtnCopies.on('success', function(e) {
99 | changeTooltipMessage(e.trigger, 'Copied!');
100 | e.clearSelection();
101 | });
102 |
103 | clipboardBtnCopies.on('error', function() {
104 | changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
105 | });
106 | });
107 | }
108 | })(window.jQuery || window.$)
109 |
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/docs/pkgdown.yml:
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1 | pandoc: 2.14.0.3
2 | pkgdown: 2.0.1
3 | pkgdown_sha: ~
4 | articles:
5 | data-prepare-rnn: data-prepare-rnn.html
6 | naming-convention: naming-convention.html
7 | parsnip-api: parsnip-api.html
8 | torchts-api: torchts-api.html
9 | torchts_formula: torchts_formula.html
10 | last_built: 2022-01-03T22:22Z
11 |
12 |
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1 |
2 |
23 |
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/man/as.vector.torch_tensor.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/as-vector.R
3 | \name{as.vector.torch_tensor}
4 | \alias{as.vector.torch_tensor}
5 | \title{Convert \code{torch_tensor} to a vector}
6 | \usage{
7 | \method{as.vector}{torch_tensor}(x, mode = "any")
8 | }
9 | \arguments{
10 | \item{x}{(\code{torch_tensor}) A \code{torch} tensor}
11 |
12 | \item{mode}{(\code{character}) A character string with one of possible vector modes:
13 | "any", "list", "expression" or other basic types like "character", "integer" etc.}
14 | }
15 | \value{
16 | A vector of desired type.
17 | All attributes are removed from the result if it is of an atomic mode,
18 | but not in general for a list result.
19 | }
20 | \description{
21 | \code{as.vector.torch_tensor} attempts to coerce a \code{torch_tensor} into a vector of
22 | mode \code{mode} (the default is to coerce to whichever vector mode is most convenient):
23 | if the result is atomic all attributes are removed.
24 | }
25 | \seealso{
26 | \link[base:vector]{base::as.vector}
27 | }
28 |
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/man/as_ts_dataloader.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/as-ts-dataloder.R
3 | \name{as_ts_dataloader}
4 | \alias{as_ts_dataloader}
5 | \title{Quick shortcut to create a torch dataloader based on the given dataset}
6 | \usage{
7 | as_ts_dataloader(
8 | data,
9 | formula,
10 | index = NULL,
11 | key = NULL,
12 | predictors = NULL,
13 | outcomes = NULL,
14 | categorical = NULL,
15 | timesteps,
16 | horizon = 1,
17 | sample_frac = 1,
18 | batch_size,
19 | shuffle = FALSE,
20 | jump = 1,
21 | drop_last = TRUE,
22 | ...
23 | )
24 | }
25 | \arguments{
26 | \item{data}{(\code{data.frame}) An input data.frame object with.
27 | For now only \strong{single} data frames are handled with no categorical features.}
28 |
29 | \item{formula}{(\code{formula}) A formula describing, how to use the data}
30 |
31 | \item{index}{(\code{character}) The index column name.}
32 |
33 | \item{key}{(\code{character}) The key column name(s). Use only if formula was not specified.}
34 |
35 | \item{predictors}{(\code{character}) Input variable names. Use only if formula was not specified.}
36 |
37 | \item{outcomes}{(\code{character}) Target variable names. Use only if formula was not specified.}
38 |
39 | \item{categorical}{(\code{character}) Categorical features.}
40 |
41 | \item{timesteps}{(\code{integer}) The time series chunk length.}
42 |
43 | \item{horizon}{(\code{integer}) Forecast horizon.}
44 |
45 | \item{sample_frac}{(\code{numeric}) Sample a fraction of rows (default: 1, i.e.: all the rows).}
46 |
47 | \item{batch_size}{(\code{numeric}) Batch size.}
48 |
49 | \item{shuffle}{(\code{logical}) Shuffle examples.}
50 |
51 | \item{drop_last}{(\code{logical}) Set to TRUE to drop the last incomplete batch,
52 | if the dataset size is not divisible by the batch size.
53 | If FALSE and the size of dataset is not divisible by the batch size,
54 | then the last batch will be smaller. (default: TRUE)}
55 | }
56 | \description{
57 | Quick shortcut to create a torch dataloader based on the given dataset
58 | }
59 | \examples{
60 | library(rsample)
61 | library(dplyr, warn.conflicts = FALSE)
62 |
63 | suwalki_temp <-
64 | weather_pl \%>\%
65 | filter(station == "SWK") \%>\%
66 | select(date, temp = tmax_daily)
67 |
68 | # Splitting on training and test
69 | data_split <- initial_time_split(suwalki_temp)
70 |
71 | train_dl <-
72 | training(data_split) \%>\%
73 | as_ts_dataloader(temp ~ date, timesteps = 20, horizon = 10, batch_size = 32)
74 |
75 | train_dl
76 |
77 | dataloader_next(dataloader_make_iter(train_dl))
78 |
79 | }
80 |
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/man/as_ts_dataset.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/as-ts-dataset.R
3 | \name{as_ts_dataset}
4 | \alias{as_ts_dataset}
5 | \title{Create a torch dataset for time series data from a \code{data.frame}-like object}
6 | \usage{
7 | as_ts_dataset(
8 | data,
9 | formula,
10 | timesteps,
11 | horizon = 1,
12 | sample_frac = 1,
13 | jump = 1,
14 | ...
15 | )
16 | }
17 | \arguments{
18 | \item{data}{(\code{data.frame}) An input data.frame object with.
19 | For now only \strong{single} data frames are handled with no categorical features.}
20 |
21 | \item{formula}{(\code{formula}) A formula describing, how to use the data}
22 |
23 | \item{timesteps}{(\code{integer}) The time series chunk length.}
24 |
25 | \item{horizon}{(\code{integer}) Forecast horizon.}
26 |
27 | \item{sample_frac}{(\code{numeric}) Sample a fraction of rows (default: 1, i.e.: all the rows).}
28 |
29 | \item{index}{(\code{character}) The index column name.}
30 |
31 | \item{key}{(\code{character}) The key column name(s). Use only if formula was not specified.}
32 |
33 | \item{predictors}{(\code{character}) Input variable names. Use only if formula was not specified.}
34 |
35 | \item{outcomes}{(\code{character}) Target variable names. Use only if formula was not specified.}
36 |
37 | \item{categorical}{(\code{character}) Categorical features.}
38 |
39 | \item{scale}{(\code{logical} or \code{list}) Scale feature columns. Logical value or two-element list.
40 | with values (mean, std)}
41 | }
42 | \description{
43 | Create a torch dataset for time series data from a \code{data.frame}-like object
44 | }
45 | \note{
46 | If \code{scale} is TRUE, only the input variables are scale and not the outcome ones.
47 |
48 | See: \href{https://stats.stackexchange.com/questions/111467/is-it-necessary-to-scale-the-target-value-in-addition-to-scaling-features-for-re}{Is it necessary to scale the target value in addition to scaling features for regression analysis? (Cross Validated)}
49 | }
50 | \examples{
51 | library(rsample)
52 | library(dplyr, warn.conflicts = FALSE)
53 |
54 | suwalki_temp <-
55 | weather_pl \%>\%
56 | filter(station == "SWK")
57 |
58 | debugonce(as_ts_dataset.data.frame)
59 |
60 | # Splitting on training and test
61 | data_split <- initial_time_split(suwalki_temp)
62 |
63 | train_ds <-
64 | training(data_split) \%>\%
65 | as_ts_dataset(tmax_daily ~ date + tmax_daily + rr_type,
66 | timesteps = 20, horizon = 1)
67 |
68 | train_ds[1]
69 |
70 | train_ds <-
71 | training(data_split) \%>\%
72 | as_ts_dataset(tmax_daily ~ date + tmax_daily + rr_type + lead(rr_type),
73 | timesteps = 20, horizon = 1)
74 |
75 | train_ds[1]
76 |
77 | train_ds <-
78 | training(data_split) \%>\%
79 | as_ts_dataset(tmax_daily ~ date + tmax_daily + rr_type + lead(tmin_daily),
80 | timesteps = 20, horizon = 1)
81 |
82 | train_ds[1]
83 |
84 | }
85 |
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/man/call_optim.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils-internal.R
3 | \name{call_optim}
4 | \alias{call_optim}
5 | \title{An auxilliary function to call optimizer}
6 | \usage{
7 | call_optim(optim, learn_rate, params)
8 | }
9 | \description{
10 | An auxilliary function to call optimizer
11 | }
12 |
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/man/check_is_complete.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/checks.R
3 | \name{check_is_complete}
4 | \alias{check_is_complete}
5 | \title{Check if input data contains no NAs.
6 | Otherwise, return error.}
7 | \usage{
8 | check_is_complete(data)
9 | }
10 | \description{
11 | Check if input data contains no NAs.
12 | Otherwise, return error.
13 | }
14 |
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/man/check_is_new_data_complete.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/checks.R
3 | \name{check_is_new_data_complete}
4 | \alias{check_is_new_data_complete}
5 | \title{Check if new data has NAs in columns others than predicted outcome}
6 | \usage{
7 | check_is_new_data_complete(object, new_data)
8 | }
9 | \description{
10 | Check if new data has NAs in columns others than predicted outcome
11 | }
12 |
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/man/check_recursion.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/checks.R
3 | \name{check_recursion}
4 | \alias{check_recursion}
5 | \title{Check, if recursion should be used in forecasting}
6 | \usage{
7 | check_recursion(object, new_data)
8 | }
9 | \description{
10 | Check, if recursion should be used in forecasting
11 | }
12 |
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/man/clear_outcome.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils.R
3 | \name{clear_outcome}
4 | \alias{clear_outcome}
5 | \title{Partially clear outcome variable
6 | in new data by overriding with NA values}
7 | \usage{
8 | clear_outcome(data, index, outcome, timesteps, key = NULL)
9 | }
10 | \arguments{
11 | \item{data}{(\code{data.frame}) New data}
12 |
13 | \item{index}{Date variable}
14 |
15 | \item{outcome}{Outcome (target) variable}
16 |
17 | \item{timesteps}{(\code{integer}) Number of timesteps used by RNN model}
18 |
19 | \item{key}{A key (id) to group the data.frame (for panel data)}
20 | }
21 | \description{
22 | Partially clear outcome variable
23 | in new data by overriding with NA values
24 | }
25 | \examples{
26 | tarnow_temp <-
27 | weather_pl \%>\%
28 | filter(station == "TRN") \%>\%
29 | select(date, tmax_daily, tmin_daily, press_mean_daily)
30 |
31 | TIMESTEPS <- 20
32 | HORIZON <- 1
33 |
34 | data_split <-
35 | time_series_split(
36 | tarnow_temp, date,
37 | initial = "18 years",
38 | assess = "2 years",
39 | lag = TIMESTEPS
40 | )
41 |
42 | cleared_new_data <-
43 | testing(data_split) \%>\%
44 | clear_outcome(date, tmax_daily, TIMESTEPS)
45 |
46 | head(cleared_new_data, TIMESTEPS + 10)
47 |
48 | }
49 |
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/man/col_map_out.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils.R
3 | \name{col_map_out}
4 | \alias{col_map_out}
5 | \title{Colmap for outcome variable}
6 | \usage{
7 | col_map_out(dataloader)
8 | }
9 | \description{
10 | Colmap for outcome variable
11 | }
12 |
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/man/dict_size.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/categorical.R
3 | \name{dict_size}
4 | \alias{dict_size}
5 | \title{Return size of categorical variables in the data.frame}
6 | \usage{
7 | dict_size(data)
8 | }
9 | \arguments{
10 | \item{data}{(\code{data.frame}) A data.frame containing categorical variables.
11 | The function automatically finds categorical variables,
12 | calling internally \link{is_categorical} function.}
13 | }
14 | \value{
15 | Named logical vector
16 | }
17 | \description{
18 | Return size of categorical variables in the data.frame
19 | }
20 | \examples{
21 | glimpse(tiny_m5)
22 | dict_size(tiny_m5)
23 |
24 | # We can choose only the features we want - otherwise it automatically
25 | # selects logical, factor, character or integer vectors
26 |
27 | tiny_m5 \%>\%
28 | select(store_id, event_name_1) \%>\%
29 | dict_size()
30 |
31 | }
32 |
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/man/embedding_size.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/categorical.R
3 | \name{embedding_size}
4 | \alias{embedding_size}
5 | \alias{embedding_size_google}
6 | \alias{embedding_size_fastai}
7 | \title{Propose the length of embedding vector for each embedded feature.}
8 | \usage{
9 | embedding_size_google(x, max_size = 100)
10 |
11 | embedding_size_fastai(x, max_size = 100)
12 | }
13 | \arguments{
14 | \item{x}{(\code{integer}) A vector with dictionary size for each feature}
15 | }
16 | \value{
17 | Proposed embedding sizes.
18 | }
19 | \description{
20 | These functions returns proposed embedding sizes for each categorical feature.
21 | They are "rule of thumbs", so the are based on empirical rather than theoretical conclusions,
22 | and their parameters can look like "magic numbers". Nevertheless, when you don't know what embedding size
23 | will be "optimal", it's good to start with such kind of general rules.
24 | \itemize{
25 | \item \strong{google}
26 | Proposed on the \href{https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html}{Google Developer} site
27 | \deqn{x^0.25}
28 | \item \strong{fastai}
29 | \deqn{1.6 * x^0.56}
30 | }
31 | }
32 | \examples{
33 | dict_sizes <- dict_size(tiny_m5)
34 | embedding_size_google(dict_sizes)
35 | embedding_size_fastai(dict_sizes)
36 |
37 | }
38 | \references{
39 | \itemize{
40 | \item \href{https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html}{Introducing TensorFlow Feature Columns}
41 | \item \href{https://github.com/fastai/fastai/blob/master/fastai/tabular/model.py}{fastai - embedding size rule of thumb}
42 | }
43 | }
44 |
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1 |
2 |
23 |
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/man/fit_network.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/training-helpers.R
3 | \name{fit_network}
4 | \alias{fit_network}
5 | \title{Fit a neural network}
6 | \usage{
7 | fit_network(net, train_dl, valid_dl = NULL, epochs, optimizer, loss_fn)
8 | }
9 | \description{
10 | Fit a neural network
11 | }
12 |
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/man/geometric_pyramid.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/heuristics.R
3 | \name{geometric_pyramid}
4 | \alias{geometric_pyramid}
5 | \title{Geometric pyramid rule}
6 | \usage{
7 | geometric_pyramid(input_size, next_layer_size)
8 | }
9 | \arguments{
10 | \item{input_size}{(\code{integer}) Input size}
11 |
12 | \item{next_layer_size}{(\code{integer}) Next layer size}
13 | }
14 | \description{
15 | A simple heuristics to choose hidden layer size
16 | }
17 | \references{
18 | \href{https://books.google.de/books/about/Practical_Neural_Network_Recipes_in_C++.html?id=7Ez_Pq0sp2EC&redir_esc=y}{Practical Neural Network Recipes in C++}
19 | }
20 |
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/man/get_x.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/training-helpers.R
3 | \name{get_x}
4 | \alias{get_x}
5 | \title{batch <- list(x_num = "aaa", x_cat = "bbb", y = "c")
6 | get_x(batch)}
7 | \usage{
8 | get_x(batch)
9 | }
10 | \description{
11 | batch <- list(x_num = "aaa", x_cat = "bbb", y = "c")
12 | get_x(batch)
13 | }
14 |
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/man/init_gate_bias.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/initialization.R
3 | \name{init_gate_bias}
4 | \alias{init_gate_bias}
5 | \title{Initialize gates to pass full information}
6 | \usage{
7 | init_gate_bias(rnn_layer)
8 | }
9 | \description{
10 | x <- list(rnn_layer = nn_lstm(2, 20))
11 | init_gate_bias(x$rnn_layer)
12 | x$rnn_layer$parameters$bias_ih_l1
13 | }
14 | \details{
15 | ~LSTM.bias_ih_l\link{k} – (b_ii|b_if|b_ig|b_io), of shape (4\emph{hidden_size)
16 | ~LSTM.bias_hh_l\link{k} – (b_hi|b_hf|b_hg|b_ho), of shape (4}hidden_size)
17 | }
18 |
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/man/is_categorical.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/categorical.R
3 | \name{is_categorical}
4 | \alias{is_categorical}
5 | \title{Check, if vector is categorical, i.e.
6 | if is logical, factor, character or integer}
7 | \usage{
8 | is_categorical(x)
9 | }
10 | \arguments{
11 | \item{x}{A vector of arbitrary type}
12 | }
13 | \value{
14 | Logical value
15 | }
16 | \description{
17 | Check, if vector is categorical, i.e.
18 | if is logical, factor, character or integer
19 | }
20 | \examples{
21 | is_categorical(c(TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE))
22 | is_categorical(1:10)
23 | is_categorical((1:10) + 0.1)
24 | is_categorical(as.factor(c("Ferrari", "Lamborghini", "Porsche", "McLaren", "Koenigsegg")))
25 | is_categorical(c("Ferrari", "Lamborghini", "Porsche", "McLaren", "Koenigsegg"))
26 |
27 | }
28 |
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/man/lagged_mlp.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/mlp-parsnip.R
3 | \name{lagged_mlp}
4 | \alias{lagged_mlp}
5 | \title{General interface to MLP networks with lagged variables}
6 | \usage{
7 | lagged_mlp(
8 | mode = "regression",
9 | timesteps = NULL,
10 | horizon = 1,
11 | learn_rate = 0.01,
12 | epochs = 50,
13 | hidden_units = NULL,
14 | dropout = NULL,
15 | batch_size = 32,
16 | scale = TRUE,
17 | shuffle = FALSE,
18 | jump = 1,
19 | sample_frac = 1
20 | )
21 | }
22 | \arguments{
23 | \item{mode}{(\code{character}) Model mode, default: 'regression'.}
24 |
25 | \item{timesteps}{(\code{integer}) Number of timesteps to look back.}
26 |
27 | \item{horizon}{(\code{integer}) Forecast horizon.}
28 |
29 | \item{learn_rate}{(\code{numeric} or \code{\link[dials:learn_rate]{dials::learn_rate}}) Learning rate.}
30 |
31 | \item{epochs}{(\code{integer} or \code{\link[dials:dropout]{dials::epochs}}) Number of epochs.}
32 |
33 | \item{hidden_units}{(\code{integer}) Number of hidden units.}
34 |
35 | \item{dropout}{(\code{logical} or \code{\link[dials:dropout]{dials::dropout}}) Flag to use dropout.}
36 |
37 | \item{batch_size}{(\code{integer}) Batch size.}
38 |
39 | \item{scale}{(\code{logical}) Scale input features.}
40 |
41 | \item{shuffle}{(\code{logical}) Shuffle examples during the training (default: FALSE).}
42 |
43 | \item{jump}{(\code{integer}) Input window shift.}
44 |
45 | \item{sample_frac}{(\code{numeric}) A percent of subsamples used for training.}
46 | }
47 | \description{
48 | General interface to MLP networks with lagged variables
49 | }
50 | \details{
51 | This is a \code{parsnip} API to the lagged feed-forward networks (aka MLP). For now the only
52 | available engine is \code{torchts_mlp}.
53 | }
54 | \section{Categorical features}{
55 |
56 | Categorical features are detected automatically - if a column of your input data (defined in the formula)
57 | is \code{logical}, \code{character}, \code{factor} or \code{integer}.
58 | }
59 |
60 | \section{Empty model}{
61 |
62 | Neural networks, unlike many other models (e.g. linear models) can return values
63 | before any training epoch ended. It's because every neural networks model starts with
64 | "random" parameters, which are gradually tuned in the following iterations according to the
65 | Gradient Descent algorithm.
66 |
67 | If you'd like to get a non-trained model, simply set \code{epochs = 0}.
68 | You still have to "fit" the model to stick the standard \code{parsnip}'s API procedure.
69 | }
70 |
71 | \examples{
72 | library(torchts)
73 | library(parsnip)
74 | library(dplyr, warn.conflicts = FALSE)
75 | library(rsample)
76 |
77 | # Univariate time series
78 | tarnow_temp <-
79 | weather_pl \%>\%
80 | filter(station == "TARNÓW") \%>\%
81 | select(date, temp = tmax_daily)
82 |
83 | data_split <- initial_time_split(tarnow_temp)
84 |
85 | mlp_model <-
86 | lagged_mlp(
87 | timesteps = 20,
88 | horizon = 1,
89 | epochs = 10,
90 | hidden_units = 32
91 | )
92 |
93 | mlp_model <-
94 | mlp_model \%>\%
95 | fit(temp ~ date, data = training(data_split))
96 |
97 |
98 | }
99 |
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/man/make_lagged_mlp.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/mlp-parsnip.R
3 | \name{make_lagged_mlp}
4 | \alias{make_lagged_mlp}
5 | \title{torchts engine}
6 | \usage{
7 | make_lagged_mlp()
8 | }
9 | \description{
10 | torchts engine
11 | }
12 |
--------------------------------------------------------------------------------
/man/make_rnn.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/rnn-parsnip.R
3 | \name{make_rnn}
4 | \alias{make_rnn}
5 | \title{torchts engine}
6 | \usage{
7 | make_rnn()
8 | }
9 | \description{
10 | torchts engine
11 | }
12 |
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/man/model_mlp.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/mlp-module.R
3 | \name{model_mlp}
4 | \alias{model_mlp}
5 | \title{A configurable feed forward network (Multi-Layer Perceptron)
6 | with embedding}
7 | \usage{
8 | model_mlp(..., horizon, output_size, embedding = NULL, activation = nnf_relu)
9 | }
10 | \description{
11 | A configurable feed forward network (Multi-Layer Perceptron)
12 | with embedding
13 | }
14 | \examples{
15 | net <- model_mlp(4, 2, 1)
16 | x <- as_tensor(iris[, 1:4])
17 | net(x)
18 |
19 | # With categorical features
20 | library(recipes)
21 | iris_prep <-
22 | recipe(iris) \%>\%
23 | step_integer(Species) \%>\%
24 | prep() \%>\%
25 | juice()
26 |
27 | iris_prep <- mutate(iris_prep, Species = as.integer(Species))
28 |
29 | x_num <- as_tensor(iris_prep[, 1:4])
30 | x_cat <- as_tensor(dplyr::select(iris_prep, 5))
31 |
32 | n_unique_values <- dict_size(iris_prep)
33 |
34 | .init_layer_spec <-
35 | init_layer_spec(
36 | num_embeddings = n_unique_values,
37 | embedding_dim = embedding_size_google(n_unique_values),
38 | numeric_in = 4,
39 | numeric_out = 2
40 | )
41 |
42 | net <- model_mlp(.init_layer_spec, 2, 1)
43 |
44 | net(x_num, x_cat)
45 |
46 | }
47 |
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/man/model_rnn.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/rnn-module.R
3 | \name{model_rnn}
4 | \alias{model_rnn}
5 | \title{A configurable recurrent neural network model}
6 | \usage{
7 | model_rnn(
8 | rnn_layer = nn_gru,
9 | input_size,
10 | output_size,
11 | hidden_size,
12 | horizon = 1,
13 | embedding = NULL,
14 | initial_layer = nn_nonlinear,
15 | last_timesteps = 1,
16 | final_layer = nn_linear,
17 | dropout = 0,
18 | batch_first = TRUE
19 | )
20 | }
21 | \arguments{
22 | \item{rnn_layer}{(\code{nn_rnn_base}) A recurrent \code{torch} layer.}
23 |
24 | \item{input_size}{(\code{integer}) Input size.}
25 |
26 | \item{output_size}{(\code{integer}) Output size (number of target variables).}
27 |
28 | \item{hidden_size}{(\code{integer}) A size of recurrent hidden layer.}
29 |
30 | \item{horizon}{(\code{integer}) Horizon size. How many steps ahead produce from the last n steps?}
31 |
32 | \item{embedding}{(\code{embedding_spec}) List with two values: num_embeddings and embedding_dim.}
33 |
34 | \item{initial_layer}{(\code{nn_module}) A \code{torch} module to preprocess numeric features before the recurrent layer.}
35 |
36 | \item{final_layer}{(\code{nn_module}) If not null, applied instead of default linear layer.}
37 |
38 | \item{dropout}{(\code{logical}) Use dropout.}
39 |
40 | \item{batch_first}{(\code{logical}) Channel order.}
41 | }
42 | \description{
43 | New features will be added in near future, e.g. categorical feature handling and so on.
44 | }
45 | \examples{
46 | library(dplyr, warn.conflicts = FALSE)
47 | library(torch)
48 | library(torchts)
49 |
50 | # Preparing data
51 | weather_data <-
52 | weather_pl \%>\%
53 | filter(station == "TRN") \%>\%
54 | select(date, tmax_daily, rr_type) \%>\%
55 | mutate(rr_type = ifelse(is.na(rr_type), "NA", rr_type))
56 |
57 | weather_dl <-
58 | weather_data \%>\%
59 | as_ts_dataloader(
60 | tmax_daily ~ date + tmax_daily + rr_type,
61 | timesteps = 30,
62 | categorical = "rr_type",
63 | batch_size = 32
64 | )
65 |
66 | unique(weather_data$rr_type)
67 | n_unique_values <- n_distinct(weather_data$rr_type)
68 |
69 | .embedding_spec <-
70 | embedding_spec(
71 | num_embeddings = n_unique_values,
72 | embedding_dim = embedding_size_google(n_unique_values)
73 | )
74 |
75 | input_size <- 1 + embedding_size_google(n_unique_values) # tmax_daily + rr_type embedding
76 |
77 | # Creating a model
78 | rnn_net <-
79 | model_rnn(
80 | input_size = input_size,
81 | output_size = 2,
82 | hidden_size = 10,
83 | horizon = 10,
84 | embedding = .embedding_spec
85 | )
86 |
87 | print(rnn_net)
88 |
89 | # Prediction example on non-trained neural network
90 | batch <-
91 | dataloader_next(dataloader_make_iter(weather_dl))
92 |
93 | # debugonce(rnn_net$forward)
94 | rnn_net(batch$x_num, batch$x_cat)
95 |
96 | }
97 |
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/man/nn_mlp.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/nn-mlp.R
3 | \name{nn_mlp}
4 | \alias{nn_mlp}
5 | \title{A shortcut to create a feed-forward block (MLP block)}
6 | \usage{
7 | nn_mlp(..., activation = nnf_relu)
8 | }
9 | \arguments{
10 | \item{...}{(\code{nn_module}, \code{function} \code{integer}, \code{character})
11 | An arbitrary number of arguments, than can be:
12 | * \code{nn_module} - e.g. \code{\link[torch:nn_relu]{torch::nn_relu()}}
13 | * \code{function} - e.g. \code{\link[torch:nnf_relu]{torch::nnf_relu}}
14 | * \code{character} - e.g. \code{selu}, which is converted to \code{nnf_selu}
15 | * \code{integer} -}
16 |
17 | \item{activation}{Used if only integers are specified. By default: \code{nnf_relu}}
18 | }
19 | \description{
20 | A shortcut to create a feed-forward block (MLP block)
21 | }
22 | \examples{
23 | nn_mlp(10, 1)
24 | nn_mlp(30, 10, 1)
25 |
26 | # Simple forward pass
27 | net <- nn_mlp(4, 2, 1)
28 | x <- as_torch_tensor(iris[, 1:4])
29 | net(x)
30 |
31 | # Simple forward pass with identity function
32 | net <- nn_mlp(4, 2, 1, activation = function (x) x)
33 | x <- as_torch_tensor(iris[, 1:4])
34 | net(x)
35 |
36 | }
37 |
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/man/nn_multi_embedding.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/nn-multi-embedding.R
3 | \name{nn_multi_embedding}
4 | \alias{nn_multi_embedding}
5 | \title{Create multiple embeddings at once}
6 | \usage{
7 | nn_multi_embedding(
8 | num_embeddings,
9 | embedding_dim,
10 | padding_idx = NULL,
11 | max_norm = NULL,
12 | norm_type = 2,
13 | scale_grad_by_freq = FALSE,
14 | sparse = FALSE,
15 | .weight = NULL
16 | )
17 | }
18 | \arguments{
19 | \item{num_embeddings}{(\code{integer}) Size of the dictionary of embeddings.}
20 |
21 | \item{embedding_dim}{(\code{integer}) The size of each embedding vector.}
22 |
23 | \item{padding_idx}{(\code{integer}, optional) If given, pads the output with
24 | the embedding vector at \code{padding_idx} (initialized to zeros) whenever it encounters the index.}
25 |
26 | \item{max_norm}{(\code{numeric}, optional) If given, each embedding vector with norm larger
27 | than max_norm is renormalized to have norm max_norm.}
28 |
29 | \item{norm_type}{(\code{numeric}, optional) The p of the p-norm to compute for the max_norm option. Default 2.}
30 |
31 | \item{scale_grad_by_freq}{(\code{logical}, optional) If given, this will scale gradients by
32 | the inverse of frequency of the words in the mini-batch. Default FALSE.}
33 |
34 | \item{sparse}{(\code{logical}, optional) If TRUE, gradient w.r.t. weight matrix will be a sparse tensor.}
35 |
36 | \item{.weight}{(\code{torch_tensor} or \code{list} of \code{torch_tensor}) Embeddings weights (in case you want to set it manually).}
37 | }
38 | \description{
39 | It is especially useful, for dealing with multiple categorical features.
40 | }
41 | \examples{
42 | library(recipes)
43 |
44 | data("gss_cat", package = "forcats")
45 |
46 | gss_cat_transformed <-
47 | recipe(gss_cat) \%>\%
48 | step_integer(everything()) \%>\%
49 | prep() \%>\%
50 | juice()
51 |
52 | gss_cat_transformed <- na.omit(gss_cat_transformed)
53 |
54 | gss_cat_transformed <-
55 | gss_cat_transformed \%>\%
56 | mutate(across(where(is.numeric), as.integer))
57 |
58 | glimpse(gss_cat_transformed)
59 |
60 | gss_cat_tensor <- as_tensor(gss_cat_transformed)
61 | .dict_size <- dict_size(gss_cat_transformed)
62 | .dict_size
63 |
64 | .embedding_size <- embedding_size_google(.dict_size)
65 |
66 | embedding_module <-
67 | nn_multi_embedding(.dict_size, .embedding_size)
68 |
69 | # Expected output size
70 | sum(.embedding_size)
71 |
72 | embedding_module(gss_cat_tensor)
73 |
74 | }
75 |
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/man/nn_nonlinear.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/nn-nonlinear.R
3 | \name{nn_nonlinear}
4 | \alias{nn_nonlinear}
5 | \title{Shortcut to create linear layer with nonlinear activation function}
6 | \usage{
7 | nn_nonlinear(in_features, out_features, bias = TRUE, activation = nn_relu())
8 | }
9 | \arguments{
10 | \item{in_features}{(\code{integer}) size of each input sample}
11 |
12 | \item{out_features}{(\code{integer}) size of each output sample}
13 |
14 | \item{bias}{(\code{logical}) If set to \code{FALSE}, the layer will not learn an additive bias.
15 | Default: \code{TRUE}}
16 |
17 | \item{activation}{(\code{nn_module}) A nonlinear activation function (default: \code{\link[torch:nn_relu]{torch::nn_relu()}})}
18 | }
19 | \description{
20 | Shortcut to create linear layer with nonlinear activation function
21 | }
22 | \examples{
23 | net <- nn_nonlinear(10, 1)
24 | x <- torch_tensor(matrix(1, nrow = 2, ncol = 10))
25 | net(x)
26 |
27 | }
28 |
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/man/nnf_mae.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/metrics.R
3 | \name{nnf_mae}
4 | \alias{nnf_mae}
5 | \title{Mean absolute error}
6 | \usage{
7 | nnf_mae(input, target)
8 | }
9 | \arguments{
10 | \item{input}{(\code{torch_tensor}) A tensor of actual values}
11 |
12 | \item{target}{(\code{torch_tensor}) A tensor with the same shape as the input}
13 | }
14 | \description{
15 | Mean absolute error
16 | }
17 | \details{
18 | Computed according to the formula:
19 | \deqn{MAE = \frac{1}{n}\displaystyle\sum_{t=1}^{n}\left\|target - input\right\|}
20 | }
21 | \examples{
22 | input <- c(92, 6.5, 57.69, 15.9, 88.47, 75.01, 5.06, 45.95, 27.8, 70.96)
23 | input <- as_tensor(input)
24 |
25 | target <- c(91.54, 5.87, 58.85, 10.73, 81.47, 75.39, 2.05, 40.95, 27.34, 66.61)
26 | target <- as_tensor(target)
27 |
28 | nnf_mae(input, target)
29 |
30 | }
31 | \seealso{
32 | \link[yardstick:mae]{yardstick::mae}
33 | }
34 |
--------------------------------------------------------------------------------
/man/nnf_mape.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/metrics.R
3 | \name{nnf_mape}
4 | \alias{nnf_mape}
5 | \title{Mean absolute percentage error}
6 | \usage{
7 | nnf_mape(input, target)
8 | }
9 | \arguments{
10 | \item{input}{(\code{torch_tensor}) A tensor of actual values}
11 |
12 | \item{target}{(\code{torch_tensor}) A tensor with the same shape as the input}
13 | }
14 | \description{
15 | Mean absolute percentage error
16 | }
17 | \details{
18 | Computed according to the formula:
19 | \deqn{MAPE = \frac{1}{n}\displaystyle\sum_{t=1}^{n} \left\|\frac{target - input}{target}\right\|}
20 | }
21 | \examples{
22 | input <- c(92, 6.5, 57.69, 15.9, 88.47, 75.01, 5.06, 45.95, 27.8, 70.96)
23 | input <- as_tensor(input)
24 |
25 | target <- c(91.54, 5.87, 58.85, 10.73, 81.47, 75.39, 2.05, 40.95, 27.34, 66.61)
26 | target <- as_tensor(target)
27 |
28 | nnf_mape(input, target)
29 |
30 | }
31 | \seealso{
32 | \link[yardstick:mape]{yardstick::mape}
33 | }
34 |
--------------------------------------------------------------------------------
/man/nnf_smape.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/metrics.R
3 | \name{nnf_smape}
4 | \alias{nnf_smape}
5 | \title{Symmetric mean absolute percentage error}
6 | \usage{
7 | nnf_smape(input, target)
8 | }
9 | \arguments{
10 | \item{input}{(\code{torch_tensor}) A tensor of actual values}
11 |
12 | \item{target}{(\code{torch_tensor}) A tensor with the same shape as the input}
13 | }
14 | \description{
15 | Symmetric mean absolute percentage error
16 | }
17 | \details{
18 | Computed according to the formula:
19 | \deqn{SMAPE = \frac{1}{n}\displaystyle\sum_{t=1}^{n} \frac{\left\|input - target\right\|}{(\left\|target\right\| + \left\|input\right\|) *0.5}}
20 | }
21 | \examples{
22 | input <- c(92, 6.5, 57.69, 15.9, 88.47, 75.01, 5.06, 45.95, 27.8, 70.96)
23 | input <- as_tensor(input)
24 |
25 | target <- c(91.54, 5.87, 58.85, 10.73, 81.47, 75.39, 2.05, 40.95, 27.34, 66.61)
26 | target <- as_tensor(target)
27 |
28 | nnf_smape(input, target)
29 |
30 | }
31 | \seealso{
32 | \link[yardstick:smape]{yardstick::smape}
33 | }
34 |
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/man/plot_forecast.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/plot.R
3 | \name{plot_forecast}
4 | \alias{plot_forecast}
5 | \title{Plot forecast vs ground truth}
6 | \usage{
7 | plot_forecast(
8 | data,
9 | forecast,
10 | outcome,
11 | index = NULL,
12 | interactive = FALSE,
13 | title = "Forecast vs actual values",
14 | ...
15 | )
16 | }
17 | \arguments{
18 | \item{interactive}{(\code{logical})}
19 | }
20 | \description{
21 | Plot forecast vs ground truth
22 | }
23 |
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/man/prepare_dl.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/prepare-data.R
3 | \name{prepare_dl}
4 | \alias{prepare_dl}
5 | \title{Prepare dataloders}
6 | \usage{
7 | prepare_dl(
8 | data,
9 | formula,
10 | index,
11 | timesteps,
12 | horizon,
13 | categorical = NULL,
14 | validation = NULL,
15 | sample_frac = 1,
16 | batch_size,
17 | shuffle,
18 | jump,
19 | parsed_formula = NULL,
20 | flatten = FALSE,
21 | ...
22 | )
23 | }
24 | \arguments{
25 | \item{data}{(\code{data.frame}) An input data.frame object with.
26 | For now only \strong{single} data frames are handled with no categorical features.}
27 |
28 | \item{formula}{(\code{formula}) A formula describing, how to use the data}
29 |
30 | \item{index}{(\code{character}) The index column name.}
31 |
32 | \item{timesteps}{(\code{integer}) The time series chunk length.}
33 |
34 | \item{horizon}{(\code{integer}) Forecast horizon.}
35 |
36 | \item{categorical}{(\code{character}) Categorical features.}
37 |
38 | \item{validation}{(\code{data.frame} or \code{numeric}) Validation dataset or percent of TODO.}
39 |
40 | \item{sample_frac}{(\code{numeric}) Sample a fraction of rows (default: 1, i.e.: all the rows).}
41 |
42 | \item{batch_size}{(\code{integer}) Batch size.}
43 |
44 | \item{shuffle}{(\code{logical}) A dataloader argument - shuffle rows or not?}
45 |
46 | \item{jump}{(\code{integer}) Input window shift.}
47 | }
48 | \description{
49 | Prepare dataloders
50 | }
51 |
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/man/print_and_capture.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils-internal.R
3 | \name{print_and_capture}
4 | \alias{print_and_capture}
5 | \title{https://stackoverflow.com/questions/26083625/how-do-you-include-data-frame-output-inside-warnings-and-errors}
6 | \usage{
7 | print_and_capture(x)
8 | }
9 | \description{
10 | https://stackoverflow.com/questions/26083625/how-do-you-include-data-frame-output-inside-warnings-and-errors
11 | }
12 |
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/man/remove_model.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils-internal.R
3 | \name{remove_model}
4 | \alias{remove_model}
5 | \title{Remove parsnip model
6 | For development purposes only}
7 | \usage{
8 | remove_model(model = "rnn")
9 | }
10 | \description{
11 | Remove parsnip model
12 | For development purposes only
13 | }
14 |
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/man/rep_if_one_element.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils-internal.R
3 | \name{rep_if_one_element}
4 | \alias{rep_if_one_element}
5 | \title{Repeat element if it length == 1}
6 | \usage{
7 | rep_if_one_element(x, output_length)
8 | }
9 | \description{
10 | Repeat element if it length == 1
11 | }
12 |
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/man/rnn.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/rnn-parsnip.R
3 | \name{rnn}
4 | \alias{rnn}
5 | \title{General interface to recurrent neural network models}
6 | \usage{
7 | rnn(
8 | mode = "regression",
9 | timesteps = NULL,
10 | horizon = 1,
11 | learn_rate = 0.01,
12 | epochs = 50,
13 | hidden_units = NULL,
14 | dropout = NULL,
15 | batch_size = 32,
16 | shuffle = FALSE,
17 | jump = 1,
18 | sample_frac = 1
19 | )
20 | }
21 | \arguments{
22 | \item{mode}{(\code{character}) Model mode, default: 'regression'.}
23 |
24 | \item{timesteps}{(\code{integer}) Number of timesteps to look back.}
25 |
26 | \item{horizon}{(\code{integer}) Forecast horizon.}
27 |
28 | \item{learn_rate}{(\code{numeric} or \code{\link[dials:learn_rate]{dials::learn_rate}}) Learning rate.}
29 |
30 | \item{epochs}{(\code{integer} or \code{\link[dials:dropout]{dials::epochs}}) Number of epochs.}
31 |
32 | \item{hidden_units}{(\code{integer}) Number of hidden units.}
33 |
34 | \item{dropout}{(\code{logical} or \code{\link[dials:dropout]{dials::dropout}}) Flag to use dropout.}
35 |
36 | \item{batch_size}{(\code{integer}) Batch size.}
37 |
38 | \item{shuffle}{(\code{logical}) Shuffle examples during the training (default: FALSE).}
39 |
40 | \item{jump}{(\code{integer}) Input window shift.}
41 |
42 | \item{sample_frac}{(\code{numeric}) A percent of subsamples used for training.}
43 |
44 | \item{scale}{(\code{logical}) Scale input features.}
45 | }
46 | \description{
47 | General interface to recurrent neural network models
48 | }
49 | \details{
50 | This is a \code{parsnip} API to the recurent network models. For now the only
51 | available engine is \code{torchts_rnn}.
52 | }
53 | \section{Categorical features}{
54 |
55 | Categorical features are detected automatically - if a column of your input data (defined in the formula)
56 | is \code{logical}, \code{character}, \code{factor} or \code{integer}.
57 | }
58 |
59 | \section{Empty model}{
60 |
61 | Neural networks, unlike many other models (e.g. linear models) can return values
62 | before any training epoch ended. It's because every neural networks model starts with
63 | "random" parameters, which are gradually tuned in the following iterations according to the
64 | Gradient Descent algorithm.
65 |
66 | If you'd like to get a non-trained model, simply set \code{epochs = 0}.
67 | You still have to "fit" the model to stick the standard \code{parsnip}'s API procedure.
68 | }
69 |
70 | \examples{
71 | library(torchts)
72 | library(parsnip)
73 | library(dplyr, warn.conflicts = FALSE)
74 | library(rsample)
75 |
76 | # Univariate time series
77 | tarnow_temp <-
78 | weather_pl \%>\%
79 | filter(station == "TARNÓW") \%>\%
80 | select(date, temp = tmax_daily)
81 |
82 | data_split <- initial_time_split(tarnow_temp)
83 |
84 | rnn_model <-
85 | rnn(
86 | timesteps = 20,
87 | horizon = 1,
88 | epochs = 10,
89 | hidden_units = 32
90 | )
91 |
92 | rnn_model <-
93 | rnn_model \%>\%
94 | fit(temp ~ date, data = training(data_split))
95 |
96 |
97 | }
98 |
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/man/rnn_output_size.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/utils.R
3 | \name{rnn_output_size}
4 | \alias{rnn_output_size}
5 | \title{RNN output size}
6 | \usage{
7 | rnn_output_size(module)
8 | }
9 | \arguments{
10 | \item{module}{(nn_module) A torch \code{nn_module}}
11 | }
12 | \description{
13 | RNN output size
14 | }
15 | \examples{
16 | gru_layer <- nn_gru(15, 3)
17 | rnn_output_size(gru_layer)
18 | }
19 |
--------------------------------------------------------------------------------
/man/set_device.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/device.R
3 | \name{set_device}
4 | \alias{set_device}
5 | \title{Set model device.}
6 | \usage{
7 | set_device(object, device, ...)
8 | }
9 | \arguments{
10 | \item{object}{An neural network object.}
11 |
12 | \item{device}{(\code{character}) Selected device.}
13 | }
14 | \value{
15 | Object of the same class with device set.
16 | }
17 | \description{
18 | Set model device.
19 | }
20 | \examples{
21 | rnn_net <-
22 | model_rnn(
23 | input_size = 1,
24 | output_size = 1,
25 | hidden_size = 10
26 | ) \%>\%
27 | set_device("cpu")
28 |
29 | rnn_net
30 |
31 | }
32 |
--------------------------------------------------------------------------------
/man/tiny_m5.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/data-tiny-m5.R
3 | \docType{data}
4 | \name{tiny_m5}
5 | \alias{tiny_m5}
6 | \title{A subset from M5 Walmart Challenge Dataset in one data frame}
7 | \format{
8 | \describe{
9 | \item{item_id}{The id of the product}
10 | \item{dept_id}{The id of the department the product belongs to}
11 | \item{cat_id}{The id of the category the product belongs to}
12 | \item{store_id}{The id of the store where the product is sold}
13 | \item{state_id}{The State where the store is located}
14 | \item{value}{The number of sold units}
15 | \item{date}{The date in a “y-m-d” format}
16 | \item{wm_yr_wk}{The id of the week the date belongs to}
17 | \item{weekday}{The type of the day (Saturday, Sunday, …, Friday)}
18 | \item{wday}{The id of the weekday, starting from Saturday}
19 | \item{month}{ The month of the date}
20 | \item{year}{The year of the date}
21 | \item{event_name_1}{If the date includes an event, the name of this event}
22 | \item{event_type_1}{If the date includes an event, the type of this event}
23 | \item{event_name_2}{If the date includes a second event, the name of this event}
24 | \item{event_type_2}{If the date includes a second event, the type of this event}
25 | \item{snap}{A binary variable (0 or 1) indicating whether the stores of CA, TX or WI allow SNAP1 purchases on the examined date. 1 indicates that SNAP purchases are allowed}
26 | \item{sell_price}{The price of the product for the given week/store.
27 | The price is provided per week (average across seven days). If not available, this means that the product was not sold during the examined week.
28 | Note that although prices are constant at weekly basis, they may change through time (both training and test set)}
29 | }
30 | }
31 | \usage{
32 | tiny_m5
33 | }
34 | \description{
35 | A piece of data cut from the training dataset used in the M5 challenges on Kaggle.
36 | M5 is a challenge from a series organized by Spyros Makridakis.
37 | }
38 | \examples{
39 | # Head of tiny_m5
40 | head(tiny_m5)
41 | }
42 | \seealso{
43 | \href{https://www.kaggle.com/c/m5-forecasting-accuracy}{M5 Forecasting - Accuracy}
44 |
45 | \href{https://www.kaggle.com/c/m5-forecasting-uncertainty}{M5 Forecasting - Uncertainty}
46 |
47 | \href{https://www.sciencedirect.com/science/article/pii/S0169207021001187}{The M5 competition: Background, organization, and implementation}
48 |
49 | \href{https://business-science.github.io/timetk/reference/index.html#section-time-series-datasets}{Other Walmart datasets in timetk}
50 | }
51 | \keyword{datasets}
52 |
--------------------------------------------------------------------------------
/man/torchts-package.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/torchts-package.R
3 | \docType{package}
4 | \name{torchts-package}
5 | \alias{torchts-package}
6 | \alias{torchts}
7 | \title{torchts: Time Series Models in torch}
8 | \author{
9 | Krzysztof Joachimiak
10 | }
11 | \keyword{package}
12 |
--------------------------------------------------------------------------------
/man/torchts_mlp.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/mlp-impl.R
3 | \name{torchts_mlp}
4 | \alias{torchts_mlp}
5 | \title{MLP model for time series forecasting}
6 | \usage{
7 | torchts_mlp(
8 | formula,
9 | data,
10 | learn_rate = 0.001,
11 | hidden_units,
12 | dropout = FALSE,
13 | timesteps = 20,
14 | horizon = 1,
15 | jump = horizon,
16 | optim = optim_adam(),
17 | validation = NULL,
18 | stateful = FALSE,
19 | batch_size = 1,
20 | epochs = 10,
21 | shuffle = TRUE,
22 | scale = TRUE,
23 | sample_frac = 1,
24 | loss_fn = nn_mse_loss(),
25 | device = NULL
26 | )
27 | }
28 | \arguments{
29 | \item{formula}{(\code{formula}) A formula describing, how to use the data}
30 |
31 | \item{data}{(\code{data.frame}) A input data.frame.}
32 |
33 | \item{learn_rate}{(\code{numeric}) Learning rate.}
34 |
35 | \item{hidden_units}{(\code{integer}) Number of hidden units.}
36 |
37 | \item{timesteps}{(\code{integer}) Number of timesteps used to produce a forecast.}
38 |
39 | \item{horizon}{(\code{integer}) Forecast horizon.}
40 |
41 | \item{jump}{(\code{integer}) Input window shift.}
42 |
43 | \item{optim}{(\code{function}) A function returning a \code{torch} optimizer (like \code{optim_adam})
44 | or R expression like \code{optim_adam(amsgrad = TRUE)}. Such expression will be handled and feed with
45 | \code{params} and \code{lr} arguments.}
46 |
47 | \item{validation}{(\code{data.frame} or \code{numeric}) Validation dataset or percent of TODO.}
48 |
49 | \item{batch_size}{(\code{integer}) Batch size.}
50 |
51 | \item{epochs}{(\code{integer}) Number of epochs to train the network.}
52 |
53 | \item{shuffle}{(\code{logical}) A dataloader argument - shuffle rows or not?}
54 |
55 | \item{scale}{(\code{logical} or \code{list})}
56 |
57 | \item{sample_frac}{(\code{numeric}) A fraction of time series to be sampled.}
58 |
59 | \item{loss_fn}{(\code{function}) A \code{torch} loss function.}
60 |
61 | \item{device}{(\code{character}) A \code{torch} device.}
62 | }
63 | \description{
64 | MLP model for time series forecasting
65 | }
66 | \examples{
67 | library(dplyr, warn.conflicts = FALSE)
68 | library(torch)
69 | library(torchts)
70 | library(timetk)
71 |
72 | # Preparing a dataset
73 | tiny_m5_sample <-
74 | tiny_m5 \%>\%
75 | filter(item_id == "FOODS_3_586", store_id == "CA_1") \%>\%
76 | mutate(value = as.numeric(value))
77 |
78 | tk_summary_diagnostics(tiny_m5_sample)
79 | glimpse(tiny_m5_sample)
80 |
81 | TIMESTEPS <- 20
82 |
83 | data_split <-
84 | time_series_split(
85 | tiny_m5_sample, date,
86 | initial = "4 years",
87 | assess = "1 year",
88 | lag = TIMESTEPS
89 | )
90 |
91 | # Training
92 | mlp_model <-
93 | torchts_mlp(
94 | value ~ date + value + sell_price + wday,
95 | data = training(data_split),
96 | hidden_units = 10,
97 | timesteps = TIMESTEPS,
98 | horizon = 1,
99 | epochs = 10,
100 | batch_size = 32
101 | )
102 |
103 | # Prediction
104 | cleared_new_data <-
105 | testing(data_split) \%>\%
106 | clear_outcome(date, value, TIMESTEPS)
107 |
108 | forecast <-
109 | mlp_model \%>\%
110 | predict(cleared_new_data)
111 |
112 | }
113 |
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/man/torchts_model.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/torchts-model.R
3 | \name{torchts_model}
4 | \alias{torchts_model}
5 | \title{Torchts abstract model}
6 | \usage{
7 | torchts_model(
8 | class,
9 | net,
10 | index,
11 | key,
12 | outcomes,
13 | predictors,
14 | optim,
15 | timesteps,
16 | parsed_formula,
17 | horizon,
18 | device,
19 | col_map_out,
20 | extras
21 | )
22 | }
23 | \description{
24 | Torchts abstract model
25 | }
26 |
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/man/torchts_palette.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/palette.R
3 | \docType{data}
4 | \name{torchts_palette}
5 | \alias{torchts_palette}
6 | \title{Picked from torchts logo}
7 | \format{
8 | An object of class \code{character} of length 5.
9 | }
10 | \usage{
11 | torchts_palette
12 | }
13 | \description{
14 | Picked from torchts logo
15 | }
16 | \keyword{datasets}
17 |
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/man/torchts_parse_formula.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/parse-formula.R
3 | \name{torchts_parse_formula}
4 | \alias{torchts_parse_formula}
5 | \title{Parse time series formula}
6 | \usage{
7 | torchts_parse_formula(formula, data)
8 | }
9 | \description{
10 | First version returns 3 types of variables:
11 | \itemize{
12 | \item outcome
13 | \item predictor
14 | \item index
15 | }
16 | }
17 | \details{
18 | If no predictor is defined, every outcome variable is treated as a predictor
19 | }
20 | \examples{
21 | library(dplyr)
22 | library(torchts)
23 |
24 | tarnow_temp <-
25 | weather_pl \%>\%
26 | filter(station == "TARNÓW") \%>\%
27 | select(date, max_temp = tmax_daily, min_temp = tmin_daily)
28 |
29 | View(torchts_parse_formula(max_temp ~ max_temp +index(date), tarnow_temp))
30 | View(torchts_parse_formula(max_temp ~ date, tarnow_temp))
31 |
32 | debugonce(torchts_parse_formula)
33 |
34 | # This example is not working
35 | View(torchts_parse_formula(max_temp + min_temp ~ max_temp + min_temp + index(date), tarnow_temp))
36 | View(torchts_parse_formula(max_temp + min_temp ~ max_temp + index(date), tarnow_temp))
37 | View(torchts_parse_formula(min_temp ~ max_temp + date, tarnow_temp))
38 |
39 | View(torchts_parse_formula(min_temp ~ max_temp + date + lead(min_temp, 1:5), tarnow_temp))
40 |
41 | }
42 |
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/man/torchts_rnn.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/rnn-impl.R
3 | \name{torchts_rnn}
4 | \alias{torchts_rnn}
5 | \title{RNN model for time series forecasting}
6 | \usage{
7 | torchts_rnn(
8 | formula,
9 | data,
10 | learn_rate = 0.001,
11 | hidden_units,
12 | dropout = FALSE,
13 | timesteps = 20,
14 | horizon = 1,
15 | jump = horizon,
16 | rnn_layer = nn_gru,
17 | initial_layer_size = NULL,
18 | optim = optim_adam(),
19 | validation = NULL,
20 | stateful = FALSE,
21 | batch_size = 1,
22 | epochs = 10,
23 | shuffle = TRUE,
24 | scale = TRUE,
25 | sample_frac = 0.5,
26 | loss_fn = nnf_mae,
27 | device = NULL
28 | )
29 | }
30 | \arguments{
31 | \item{formula}{(\code{formula}) A formula describing, how to use the data}
32 |
33 | \item{data}{(\code{data.frame}) A input data.frame.}
34 |
35 | \item{learn_rate}{(\code{numeric}) Learning rate.}
36 |
37 | \item{hidden_units}{(\code{integer}) Number of hidden units.}
38 |
39 | \item{dropout}{(\code{logical}) Use dropout (default = FALSE).}
40 |
41 | \item{timesteps}{(\code{integer}) Number of timesteps used to produce a forecast.}
42 |
43 | \item{horizon}{(\code{integer}) Forecast horizon.}
44 |
45 | \item{jump}{(\code{integer}) Input window shift.}
46 |
47 | \item{rnn_layer}{(\code{nn_rnn_base}) A \code{torch} recurrent layer.}
48 |
49 | \item{optim}{(\code{function}) A function returning a \code{torch} optimizer (like \code{optim_adam})
50 | or R expression like \code{optim_adam(amsgrad = TRUE)}. Such expression will be handled and feed with
51 | \code{params} and \code{lr} arguments.}
52 |
53 | \item{validation}{(\code{data.frame} or \code{numeric}) Validation dataset or percent of TODO.}
54 |
55 | \item{stateful}{(\code{logical} or \code{list}) Determine network behaviour: is stateful or not.}
56 |
57 | \item{batch_size}{(\code{integer}) Batch size.}
58 |
59 | \item{epochs}{(\code{integer}) Number of epochs to train the network.}
60 |
61 | \item{shuffle}{(\code{logical}) A dataloader argument - shuffle rows or not?}
62 |
63 | \item{scale}{(\code{logical} or \code{list})}
64 |
65 | \item{sample_frac}{(\code{numeric}) A fraction of time series to be sampled.}
66 |
67 | \item{loss_fn}{(\code{function}) A \code{torch} loss function.}
68 |
69 | \item{device}{(\code{character}) A \code{torch} device.}
70 | }
71 | \description{
72 | RNN model for time series forecasting
73 | }
74 | \examples{
75 | library(dplyr, warn.conflicts = FALSE)
76 | library(torch)
77 | library(torchts)
78 | library(timetk)
79 |
80 | # Preparing a dataset
81 | tiny_m5_sample <-
82 | tiny_m5 \%>\%
83 | filter(item_id == "FOODS_3_586", store_id == "CA_1") \%>\%
84 | mutate(value = as.numeric(value))
85 |
86 | tk_summary_diagnostics(tiny_m5_sample)
87 | glimpse(tiny_m5_sample)
88 |
89 | TIMESTEPS <- 20
90 |
91 | data_split <-
92 | time_series_split(
93 | tiny_m5_sample, date,
94 | initial = "4 years",
95 | assess = "1 year",
96 | lag = TIMESTEPS
97 | )
98 |
99 | # Training
100 | rnn_model <-
101 | torchts_rnn(
102 | value ~ date + value + sell_price + wday,
103 | data = training(data_split),
104 | hidden_units = 10,
105 | timesteps = TIMESTEPS,
106 | horizon = 1,
107 | epochs = 10,
108 | batch_size = 32
109 | )
110 |
111 | # Prediction
112 | cleared_new_data <-
113 | testing(data_split) \%>\%
114 | clear_outcome(date, value, TIMESTEPS)
115 |
116 | forecast <-
117 | predict(rnn_model, cleared_new_data)
118 |
119 | }
120 |
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/man/train_batch.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/training-helpers.R
3 | \name{train_batch}
4 | \alias{train_batch}
5 | \title{Training helper}
6 | \usage{
7 | train_batch(input, target, net, optimizer, loss_fun = nnf_mse_loss)
8 | }
9 | \description{
10 | Training helper
11 | }
12 |
--------------------------------------------------------------------------------
/man/ts_dataset.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/ts-dataset.R
3 | \name{ts_dataset}
4 | \alias{ts_dataset}
5 | \title{Create a time series dataset from a \code{torch_tensor} matrix}
6 | \usage{
7 | ts_dataset(
8 | data,
9 | timesteps,
10 | horizon,
11 | index,
12 | jump = horizon,
13 | past_spec = list(x = NULL),
14 | future_spec = list(y = NULL),
15 | categorical = NULL,
16 | sample_frac = 1,
17 | device = "cpu",
18 | extras = NULL,
19 | ...
20 | )
21 | }
22 | \arguments{
23 | \item{data}{(\code{data.frame}) An data.frame-like input object.}
24 |
25 | \item{timesteps}{(\code{integer}) Number of timesteps for input tensor.}
26 |
27 | \item{horizon}{(\code{integer}) Forecast horizon: number of timesteps for output tensor.}
28 |
29 | \item{jump}{(\code{integer}) Jump length. By default: horizon length.}
30 |
31 | \item{past_spec}{(\code{list}) Specification of the variables which values from the past will be available.
32 | It should be a list with names representing names of tensors served by dataset, and values being feature indices.}
33 |
34 | \item{future_spec}{(\code{list}) Specification of the variableswith known values from the future.
35 | It should be a list with names representing names of tensors served b}
36 |
37 | \item{categorical}{(\code{character}) Names of specified column subsets considered as categorical.
38 | They will be provided as integer tensors.}
39 |
40 | \item{extras}{(\code{list}) List of extra object to be stored inside the ts_dataset object.}
41 |
42 | \item{sample_fram}{(\code{numeric}) A numeric value > 0. and <= 1 to sample a subset of data.}
43 | }
44 | \description{
45 | Create a time series dataset from a \code{torch_tensor} matrix
46 | }
47 | \note{
48 | If \code{scale} is TRUE, only the input variables are scale and not the outcome ones.
49 |
50 | See: \href{https://stats.stackexchange.com/questions/111467/is-it-necessary-to-scale-the-target-value-in-addition-to-scaling-features-for-re}{Is it necessary to scale the target value in addition to scaling features for regression analysis? (Cross Validated)}
51 | }
52 | \examples{
53 | library(dplyr, warn.conflicts = FALSE)
54 | library(torchts)
55 |
56 | tarnow_temp <-
57 | weather_pl \%>\%
58 | filter(station == 'TRN') \%>\%
59 | arrange(date)
60 |
61 | weather_pl_dataset <-
62 | ts_dataset(
63 | data = tarnow_temp,
64 | timesteps = 28,
65 | horizon = 7,
66 | past_spec = list(x_num = c('tmax_daily', 'tmin_daily')),
67 | future_spec = list(y = 'tmax_daily')
68 | )
69 |
70 | debugonce(weather_pl_dataset$.getitem)
71 | weather_pl_dataset$.getitem(1)
72 |
73 | }
74 |
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/man/valid_batch.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/training-helpers.R
3 | \name{valid_batch}
4 | \alias{valid_batch}
5 | \title{Validation helper function}
6 | \usage{
7 | valid_batch(net, input, target, loss_fun = nnf_mse_loss)
8 | }
9 | \description{
10 | Validation helper function
11 | }
12 |
--------------------------------------------------------------------------------
/man/weather_pl.Rd:
--------------------------------------------------------------------------------
1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/data-weather-pl.R
3 | \docType{data}
4 | \name{weather_pl}
5 | \alias{weather_pl}
6 | \title{Weather data from Polish "poles of extreme temperatures" in 2001-2020}
7 | \format{
8 | \describe{
9 | \item{station}{A place where weather data were measured}
10 | \item{date}{Date}
11 | \item{tmax_daily}{Maximum daily air temperatury \link{C}}
12 | \item{tmin_daily}{Minimum daily air temperature \link{C}}
13 | \item{tmin_soil}{Minimum near surface air temperature \link{C}}
14 | \item{rr_daily}{Total daily preciptation \link{mm}}
15 | \item{rr_type}{Precipitation type \link{S/W}}
16 | \item{rr_daytime}{Total precipitation during day \link{mm}}
17 | \item{rr_nightime}{Total precipitation during night \link{mm}}
18 | \item{press_mean_daily}{Daily mean pressure at station level \link{hPa}}
19 | }
20 | }
21 | \usage{
22 | weather_pl
23 | }
24 | \description{
25 | The data comes from IMGW (Institute of Meteorology and Water Management) and
26 | was downloaded using the \link{climate} package. Two places have been chosen:
27 | \itemize{
28 | \item{TRN - Tarnów ("pole of warmth")}
29 | \item{SWK - Suwałki ("pole of cold")}
30 | }
31 | A subset of columns has been selected and \code{date} column was added.
32 | }
33 | \examples{
34 | # Head of weather_pl
35 | head(weather_pl)
36 | }
37 | \seealso{
38 | \href{https://github.com/bczernecki/climate}{climate}
39 | \href{https://danepubliczne.imgw.pl/}{IMGW public data}
40 | \href{https://danepubliczne.imgw.pl/data/dane_pomiarowo_obserwacyjne/}{IMGW public data (direct access to folders)}
41 | }
42 | \keyword{datasets}
43 |
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/man/which_static.Rd:
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1 | % Generated by roxygen2: do not edit by hand
2 | % Please edit documentation in R/static.R
3 | \name{which_static}
4 | \alias{which_static}
5 | \title{Check, which variables are static}
6 | \usage{
7 | which_static(data, key, cols = NULL)
8 | }
9 | \description{
10 | Check, which variables are static
11 | }
12 | \examples{
13 | data <- tiny_m5 \%>\%
14 | dplyr::select(store_id, item_id, state_id,
15 | weekday, wday, month, year)
16 |
17 | }
18 |
--------------------------------------------------------------------------------
/notes.md:
--------------------------------------------------------------------------------
1 | ## TODO
2 |
3 | * handling missing data as a separate vignette
4 | * add a vignette with name explanation (index, key, outcome = target etc.)
5 | * monitor hidden state
6 | https://fairyonice.github.io/Understand-Keras%27s-RNN-behind-the-scenes-with-a-sin-wave-example.html
7 | * sine
8 | https://towardsdatascience.com/can-machine-learn-the-concept-of-sine-4047dced3f11
9 | https://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html
10 | https://goelhardik.github.io/2016/05/25/lstm-sine-wave/
11 | https://codesachin.wordpress.com/2016/01/23/predicting-trigonometric-waves-few-steps-ahead-with-lstms-in-tensorflow/
12 |
13 | * check model "confusion", i.e. variance for nearly the same examples
14 | https://stats.stackexchange.com/questions/220307/rnn-learning-sine-waves-of-different-frequencies
15 |
16 | * verbosity level as percentage
17 | *
18 |
19 |
20 | ## Which name?
21 |
22 | * h, horizon, prediction_length (modeltime.gluonts)
23 | * timesteps, steps, lookback_length (modeltime.gluonts)
24 | * targets, outcomes <= https://recipes.tidymodels.org/articles/Roles.html
25 | * predictor, inputs
26 | * key (fable), id (modeltime)
27 | * index (fable), date_var (timetk) <= https://tsibble.tidyverts.org/reference/tsibble.html
28 |
29 | ## Conditional RNN
30 | * [cond_rnn](https://github.com/philipperemy/cond_rnn)
31 | * [Adding Features To Time Series Model LSTM](https://datascience.stackexchange.com/questions/17099/adding-features-to-time-series-model-lstm/17139#17139)
32 |
33 | ## Missing values
34 | * See: https://www.tensorflow.org/guide/keras/masking_and_padding
35 | * https://www.nature.com/articles/s41598-018-24271-9
36 |
37 | ## Demand
38 | * https://www.researchgate.net/publication/298725275_A_new_metric_of_absolute_percentage_error_for_intermittent_demand_forecasts
39 | * https://frepple.com/blog/demand-classification/
40 |
41 | ## Metrics
42 | * https://datascience.stackexchange.com/questions/55388/tweedie-loss-for-keras
43 |
44 | ## Libraries
45 | * https://github.com/zalandoresearch/pytorch-ts
46 | * https://pytorch-widedeep.readthedocs.io/en/latest/index.html
47 |
48 | ## Other
49 | * [AR-Net](https://arxiv.org/pdf/1911.12436.pdf)
50 |
51 | ## Heuristics
52 | https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
53 |
54 |
55 | ## R + Keras
56 | https://www.datatechnotes.com/2019/01/regression-example-with-lstm-networks.html
57 |
58 |
59 | ## RNN
60 | * A Formal Hierarchy of RNN Architectures
61 | https://arxiv.org/abs/2004.08500
62 |
63 |
64 | ## Formula proposals
65 |
66 | h(value, 7) ~ b(value, 28) + price + lead(price, 5)
67 |
68 | ## Visualization
69 | https://github.com/tensorspace-team/tensorspace
70 |
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/pkgdown/extra.css:
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1 | .navbar-default {
2 | background-color: #7e1f77;
3 | border-color: #7e1f77;
4 | }
5 |
6 | .navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus {
7 | background-color: #a953a2;
8 | }
9 |
10 | .navbar-default .navbar-collapse, .navbar-default .navbar-form {
11 | border-color: #ffffff;
12 | }
13 |
14 | .navbar-default .navbar-nav .open .dropdown-menu>.dropdown-header {
15 | border-color: #ffffff !important;
16 | }
17 |
18 | .navbar-default .navbar-nav>li>a:hover, .navbar-default .navbar-nav>li>a:focus {
19 | color: #ffffff;
20 | background-color: #a953a2;
21 | }
22 |
23 | .navbar-default .navbar-nav>.active>a, .navbar-default .navbar-nav>.active>a:hover, .navbar-default .navbar-nav>.active>a:focus {
24 | background-color: #a953a2;
25 | }
26 |
27 | .navbar-default .navbar-nav>.open>a, .navbar-default .navbar-nav>.open>a:hover, .navbar-default .navbar-nav>.open>a:focus {
28 | background-color: #a953a2;
29 | }
30 |
31 | .nav-pills>li.active>a, .nav-pills>li.active>a:hover, .nav-pills>li.active>a:focus {
32 | background-color: #a953a2;
33 | }
34 |
35 | .dropdown-menu>.active>a,
36 | .dropdown-menu>.active>a:hover,
37 | .dropdown-menu>.active>a:focus {
38 | background-color: #7e1f77;
39 | }
40 |
41 | .dropdown-menu>a:hover,
42 | .dropdown-menu>a:focus {
43 | background-color: #7e1f77;
44 | }
45 |
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/tests/testthat.R:
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1 | library(testthat)
2 | # library(torchts)
3 | library(torch)
4 | library(dplyr)
5 | library(timetk)
6 | library(rsample)
7 |
8 | test_check("torchts")
9 |
--------------------------------------------------------------------------------
/tests/testthat/test-as-tensor.R:
--------------------------------------------------------------------------------
1 | library(torch)
2 | library(dplyr)
3 |
4 | test_that("torch_tensor passed to as_torch_tensor with no arguments", {
5 | x <- torch_tensor(rep(3, 10))
6 | y <- as_torch_tensor(x)
7 | expect_identical(x, y)
8 | })
9 |
10 | test_that("Simple data.frame transformation", {
11 | x <- as_torch_tensor(mtcars)
12 | expect_equal(dim(x), dim(mtcars))
13 | })
14 |
15 | test_that("data.frame reshaped with non-numeric columns", {
16 |
17 | weather_tensor <-
18 | weather_pl %>%
19 | select(-rr_type) %>%
20 | as_torch_tensor(station, date)
21 |
22 | expected_shape <-
23 | c(
24 | n_distinct(weather_pl$station),
25 | n_distinct(weather_pl$date),
26 | length(colnames(weather_pl)) - 3 # station, date and removed rr_type
27 | )
28 |
29 | expect_equal(
30 | dim(weather_tensor), expected_shape
31 | )
32 |
33 | })
34 |
35 | test_that("data.frame with a non-numeric colum: raises error", {
36 | expect_error(as_torch_tensor(weather_pl))
37 | })
38 |
39 |
40 | test_that("Check data order after reshaping", {
41 |
42 | # TODO: add tests for more than 3 shapes
43 |
44 | temperature_pl <-
45 | weather_pl %>%
46 | select(station, date, tmax_daily)
47 |
48 | temperature_tensor <-
49 | temperature_pl %>%
50 | as_torch_tensor(station, date)
51 |
52 | result <-
53 | temperature_tensor[1, 1:10] %>%
54 | as.vector()
55 |
56 | expected <-
57 | temperature_pl %>%
58 | filter(station == "SWK") %>%
59 | arrange(date) %>%
60 | head(10) %>%
61 | pull()
62 |
63 | expect_equal(result, expected, tolerance=1e-7)
64 | })
65 |
66 |
67 |
--------------------------------------------------------------------------------
/tests/testthat/test-as-ts-dataloader.R:
--------------------------------------------------------------------------------
1 | library(dplyr)
2 |
3 | test_that("Dataloader basic test", {
4 | library(rsample)
5 |
6 | suwalki_temp <-
7 | weather_pl %>%
8 | filter(station == "SWK") %>%
9 | select(date, temp = tmax_daily)
10 |
11 | # Splitting on training and test
12 | data_split <-
13 | initial_time_split(suwalki_temp)
14 |
15 | train_ds <-
16 | training(data_split) %>%
17 | as_ts_dataloader(temp ~ date, timesteps = 20, horizon = 1, batch_size = 32)
18 |
19 | batch <-
20 | dataloader_next(dataloader_make_iter(train_ds))
21 |
22 | expect_equal(dim(batch$x_num), c(32, 20, 1))
23 | expect_equal(dim(batch$y), c(32, 1, 1))
24 |
25 | })
26 |
27 |
28 | test_that("Dataloader - categorical variables", {
29 |
30 | library(rsample)
31 |
32 | suwalki_temp <-
33 | weather_pl %>%
34 | filter(station == "SWK") %>%
35 | select(date, temp = tmax_daily, rr_type)
36 |
37 | # Splitting on training and test
38 | data_split <-
39 | initial_time_split(suwalki_temp)
40 |
41 | train_ds <-
42 | training(data_split) %>%
43 | as_ts_dataloader(temp ~ date + temp + rr_type,
44 | timesteps = 20, horizon = 1, batch_size = 32)
45 |
46 | batch <-
47 | dataloader_next(dataloader_make_iter(train_ds))
48 |
49 | # Sizes
50 | expect_equal(dim(batch$x_num), c(32, 20, 1))
51 | expect_equal(dim(batch$x_cat), c(32, 20, 1))
52 | expect_equal(dim(batch$y), c(32, 1))
53 |
54 | # Types
55 | expect_equal(batch$x_num$dtype, torch_float())
56 | expect_equal(batch$x_cat$dtype, torch_int())
57 | expect_equal(batch$y$dtype, torch_float())
58 |
59 | })
60 |
61 | # TODO: add further unit tests
62 |
--------------------------------------------------------------------------------
/tests/testthat/test-as-ts-dataset.R:
--------------------------------------------------------------------------------
1 | tarnow_temp <-
2 | weather_pl %>%
3 | filter(station == "TRN") %>%
4 | select(date, max_temp = tmax_daily, min_temp = tmin_daily)
5 |
6 | tarnow_temp_full <-
7 | weather_pl %>%
8 | filter(station == "TRN") %>%
9 | rename(max_temp = tmax_daily, min_temp = tmin_daily)
10 |
11 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | # SELECTED VARIABLES
13 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
14 |
15 | test_that("Test as_ts_dataset", {
16 |
17 | TIMESTEPS <- 20
18 | HORIZON <- 1
19 |
20 | tarnow_ds <-
21 | tarnow_temp %>%
22 | as_ts_dataset(
23 | max_temp ~ date,
24 | timesteps = TIMESTEPS,
25 | horizon = HORIZON,
26 | scale = FALSE
27 | )
28 |
29 | # First slice
30 | ds_slice <- tarnow_ds[1]
31 |
32 | # Input
33 | expect_equal(
34 | as.vector(ds_slice$x_num),
35 | tarnow_temp$max_temp[1:TIMESTEPS],
36 | tolerance = 1e-7
37 | )
38 |
39 | # Target
40 | expect_equal(
41 | as.vector(ds_slice$y),
42 | tarnow_temp$max_temp[TIMESTEPS + HORIZON],
43 | tolerance = 1e-7
44 | )
45 |
46 | })
47 |
48 |
49 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
50 | # ALL THE VARIABLES
51 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
52 |
53 | test_that("Test as_ts_dataset (all vars)", {
54 |
55 | TIMESTEPS <- 20
56 | HORIZON <- 1
57 |
58 | tarnow_ds <-
59 | tarnow_temp_full %>%
60 | as_ts_dataset(
61 | max_temp ~ date,
62 | timesteps = TIMESTEPS,
63 | horizon = HORIZON,
64 | scale = FALSE
65 | )
66 |
67 | # First slice
68 | ds_slice <- tarnow_ds[1]
69 |
70 | # Input is scaled
71 | expect_equal(
72 | as.vector(ds_slice$x_num),
73 | tarnow_temp_full$max_temp[1:TIMESTEPS],
74 | tolerance = 1e-7
75 | )
76 |
77 | # Target is not scaled
78 | expect_equal(
79 | as.vector(ds_slice$y),
80 | tarnow_temp_full$max_temp[TIMESTEPS + HORIZON],
81 | tolerance = 1e-7
82 | )
83 |
84 | })
85 |
86 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
87 | # ERRORS
88 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
89 |
90 | test_that("Error when index not defined", {
91 | expect_error(
92 | as_ts_dataset(
93 | tarnow_temp,
94 | max_temp ~ min_temp,
95 | timesteps = TIMESTEPS,
96 | horizon = HORIZON
97 | )
98 | )
99 | })
100 |
101 | test_that("Error when passed empty data.frame", {
102 | expect_error(
103 | as_ts_dataset(
104 | tarnow_temp %>% filter(date < as.Date("2001-01-01")),
105 | max_temp ~ min_temp,
106 | timesteps = TIMESTEPS,
107 | horizon = HORIZON
108 | )
109 | )
110 | })
111 |
112 | test_that("Error when passed a data.frame with non-numeric values (for now)", {
113 |
114 | tarnow_temp_non_numeric <-
115 | weather_pl %>%
116 | filter(station == "TRN")
117 |
118 | expect_error(
119 | as_ts_dataset(
120 | tarnow_temp_non_numeric,
121 | max_temp ~ min_temp,
122 | timesteps = TIMESTEPS,
123 | horizon = HORIZON
124 | )
125 | )
126 |
127 | })
128 |
129 |
--------------------------------------------------------------------------------
/tests/testthat/test-as-vector.R:
--------------------------------------------------------------------------------
1 | test_that("Test as.vector.torch_tensor", {
2 | x <- torch_tensor(array(10, dim = c(3, 3, 3)))
3 | expect_equal(as.vector(x), rep(10, 27))
4 | })
5 |
--------------------------------------------------------------------------------
/tests/testthat/test-categorical.R:
--------------------------------------------------------------------------------
1 |
2 | test_that("Test is_categorical", {
3 |
4 | char_vector <- c("Ferrari", "Lamborghini", "Porsche", "McLaren", "Koenigsegg")
5 |
6 | # TRUE
7 | expect_true(is_categorical(c(TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE)))
8 | expect_true(is_categorical(1:10))
9 | expect_true(is_categorical(char_vector))
10 | expect_true(is_categorical(as.factor(char_vector)))
11 |
12 | # FALSE
13 | expect_false(is_categorical((1:10) + 0.1))
14 | expect_false(
15 | withr::with_options(
16 | list(torchts_categoricals = "factor"), {
17 | is_categorical(char_vector)
18 | })
19 | )
20 |
21 |
22 | })
23 |
--------------------------------------------------------------------------------
/tests/testthat/test-checks.R:
--------------------------------------------------------------------------------
1 | test_that("Test check_is_complete", {
2 | expect_error(check_is_complete(weather_pl))
3 | expect_equal(check_is_complete(mtcars), NULL)
4 | })
5 |
6 | test_that("Test check_is_new_data_complete", {
7 |
8 | weather_trn_1 <-
9 | weather_pl %>%
10 | filter(station == 'TRN') %>%
11 | select(date, tmin_daily, tmin_soil)
12 |
13 | weather_trn_1 <-
14 | clear_outcome(weather_trn, date,
15 | c(tmin_daily, tmin_soil), 20)
16 |
17 | object_1 <- list(predictors = "tmin_soil", outcomes = "tmin_daily")
18 |
19 | expect_error(check_is_new_data_complete(object_1, weather_trn_1))
20 |
21 | weather_trn_2 <-
22 | weather_pl %>%
23 | filter(station == 'TRN') %>%
24 | select(date, tmin_daily, tmin_soil)
25 |
26 | weather_trn_2 <-
27 | clear_outcome(weather_trn_2, date, tmin_daily, 20)
28 |
29 | object_2 <- list(predictors = "tmin_soil", outcomes = "tmin_daily")
30 |
31 | expect_equal(check_is_new_data_complete(object_2, weather_trn_2), NULL)
32 | })
33 |
--------------------------------------------------------------------------------
/tests/testthat/test-metrics.R:
--------------------------------------------------------------------------------
1 |
2 | test_that("MAPE", {
3 |
4 | target <- c(91.54, 5.87, 58.85, 10.73, 81.47, 75.39, 2.05, 40.95, 27.34, 66.61)
5 | target <- as_torch_tensor(target)
6 |
7 | input <- c(92, 6.5, 57.69, 15.9, 88.47, 75.01, 5.06, 45.95, 27., 70.96)
8 | input <- as_torch_tensor(input)
9 |
10 | expect_equal(
11 | as.vector(nnf_mape(input, target)),
12 | 0.2372984,
13 | tolerance = 1e-7
14 | )
15 |
16 | })
17 |
18 | test_that("MAE", {
19 |
20 | target <- c(91.54, 5.87, 58.85, 10.73, 81.47, 75.39, 2.05, 40.95, 27.34, 66.61)
21 | target <- as_torch_tensor(target)
22 |
23 | input <- c(92, 6.5, 57.69, 15.9, 88.47, 75.01, 5.06, 45.95, 27.8, 70.96)
24 | input <- as_torch_tensor(input)
25 |
26 | expect_equal(
27 | nnf_mae(input, target),
28 | as_torch_tensor(2.762),
29 | tolerance = 1e-10
30 | )
31 |
32 | })
33 |
--------------------------------------------------------------------------------
/tests/testthat/test-module-nn-nonlinear.R:
--------------------------------------------------------------------------------
1 | test_that("Nonlinear module simple test", {
2 |
3 | net <- nn_nonlinear(10, 1)
4 | x <- torch_tensor(matrix(1, nrow = 2, ncol = 10))
5 | out <- net(x)
6 |
7 | expect_equal(dim(out), c(2, 1))
8 | })
9 |
--------------------------------------------------------------------------------
/tests/testthat/test-prepare-dl.R:
--------------------------------------------------------------------------------
1 | library(timetk)
2 |
3 | test_that("Simple prepare_dl test - no validataion dataloader", {
4 |
5 | tarnow_temp <-
6 | weather_pl %>%
7 | filter(station == "TRN") %>%
8 | select(date, tmax_daily, tmin_daily, press_mean_daily)
9 |
10 | data_split <-
11 | time_series_split(
12 | tarnow_temp, date,
13 | initial = "18 years",
14 | assess = "2 years",
15 | lag = 20
16 | )
17 |
18 | dls <-
19 | prepare_dl(
20 | data = training(data_split),
21 | formula = tmax_daily ~ date,
22 | index = "date",
23 | timesteps = 28,
24 | horizon = 7,
25 | validation = NULL,
26 | scale = TRUE,
27 | batch_size = 32
28 | )
29 |
30 | expect_equal(class(dls[[1]]), c("dataloader", "R6"))
31 | expect_equal(dls[[2]], NULL)
32 |
33 | })
34 |
35 |
36 | test_that("Simple prepare_dl test - with numeric validataion argument", {
37 |
38 | tarnow_temp <-
39 | weather_pl %>%
40 | filter(station == "TRN") %>%
41 | select(date, tmax_daily, tmin_daily, press_mean_daily)
42 |
43 | data_split <-
44 | time_series_split(
45 | tarnow_temp, date,
46 | initial = "18 years",
47 | assess = "2 years",
48 | lag = 20
49 | )
50 |
51 | dls <-
52 | prepare_dl(
53 | data = training(data_split),
54 | formula = tmax_daily ~ date,
55 | index = "date",
56 | timesteps = 28,
57 | horizon = 7,
58 | validation = 0.25,
59 | scale = TRUE,
60 | batch_size = 32
61 | )
62 |
63 | expect_equal(class(dls[[1]]), c("dataloader", "R6"))
64 | expect_equal(class(dls[[2]]), c("dataloader", "R6"))
65 |
66 | })
67 |
--------------------------------------------------------------------------------
/tests/testthat/test-rnn-impl.R:
--------------------------------------------------------------------------------
1 | library(timetk)
2 |
3 | tarnow_temp <-
4 | weather_pl %>%
5 | filter(station == "TRN") %>%
6 | select(date, tmax_daily, tmin_daily, press_mean_daily)
7 |
8 | test_that("RNN autoregression output", {
9 |
10 | TIMESTEPS <- 20
11 | HORIZON <- 1
12 |
13 | data_split <-
14 | time_series_split(
15 | tarnow_temp, date,
16 | initial = "18 years",
17 | assess = "2 years",
18 | lag = TIMESTEPS
19 | )
20 |
21 | non_trained_model <-
22 | torchts_rnn(
23 | tmax_daily ~ date,
24 | data = training(data_split),
25 | dropout = FALSE,
26 | learn_rate = 0.9,
27 | hidden_units = 10,
28 | timesteps = TIMESTEPS,
29 | horizon = HORIZON,
30 | epochs = 0
31 | )
32 |
33 | cleared_new_data <-
34 | testing(data_split) %>%
35 | clear_outcome(date, tmax_daily, TIMESTEPS)
36 |
37 | output <-
38 | non_trained_model %>%
39 | predict(cleared_new_data)
40 |
41 | expect_equal(
42 | length(output), nrow(cleared_new_data)
43 | )
44 |
45 | })
46 |
47 |
48 | test_that("RNN autoregression multioutput", {
49 |
50 | TIMESTEPS <- 20
51 | HORIZON <- 1
52 |
53 | data_split <-
54 | time_series_split(
55 | tarnow_temp, date,
56 | initial = "18 years",
57 | assess = "2 years",
58 | lag = TIMESTEPS
59 | )
60 |
61 | non_trained_model <-
62 | torchts_rnn(
63 | tmax_daily + tmin_daily ~ date,
64 | data = training(data_split),
65 | dropout = FALSE,
66 | learn_rate = 0.9,
67 | hidden_units = 10,
68 | timesteps = TIMESTEPS,
69 | horizon = HORIZON,
70 | epochs = 0
71 | )
72 |
73 | cleared_new_data <-
74 | testing(data_split) %>%
75 | clear_outcome(date, c(tmax_daily, tmin_daily), TIMESTEPS)
76 |
77 | output <-
78 | non_trained_model %>%
79 | predict(cleared_new_data)
80 |
81 | # Dimensions
82 | expect_equal(
83 | nrow(output),
84 | nrow(cleared_new_data)
85 | )
86 |
87 | expect_equal(dim(output)[2], 2)
88 |
89 | # Colnames if mor than two columns
90 | expect_equal(
91 | colnames(output),
92 | c("tmax_daily", "tmin_daily")
93 | )
94 |
95 | })
96 |
97 |
98 |
99 |
100 |
--------------------------------------------------------------------------------
/tests/testthat/test-rnn-module.R:
--------------------------------------------------------------------------------
1 | test_that("Basic RNN test", {
2 |
3 | # Preparing data
4 | weather_dl <-
5 | weather_pl %>%
6 | filter(station == "TRN") %>%
7 | select(date, tmax_daily) %>%
8 | as_ts_dataloader(
9 | tmax_daily ~ date,
10 | timesteps = 30,
11 | batch_size = 32
12 | )
13 |
14 | # Creating a model
15 | rnn_net <-
16 | model_rnn(
17 | input_size = 1,
18 | output_size = 1,
19 | hidden_size = 10
20 | )
21 |
22 | expect_equal(
23 | class(rnn_net), c("model_rnn", "nn_module")
24 | )
25 |
26 | # Prediction example on non-trained neural network
27 | batch <-
28 | dataloader_next(dataloader_make_iter(weather_dl))
29 |
30 | output <- rnn_net(batch$x)
31 |
32 | expect_equal(dim(output), c(32, 1, 1))
33 |
34 | })
35 |
--------------------------------------------------------------------------------
/tests/testthat/test-torchts-parse-formula.R:
--------------------------------------------------------------------------------
1 | library(dplyr)
2 |
3 | tarnow_temp <-
4 | weather_pl %>%
5 | filter(station == "TRN") %>%
6 | select(date, max_temp = tmax_daily, min_temp = tmin_daily)
7 |
8 | test_that("Test simple formula", {
9 |
10 | output <-
11 | torchts_parse_formula(max_temp ~ date, tarnow_temp)
12 |
13 | expected <- tribble(
14 | ~ .var, ~ .role, ~ .class, ~ .type,
15 | "max_temp", "outcome", "numeric", "numeric",
16 | "date", "index", "Date", "date",
17 | "max_temp", "predictor", "numeric", "numeric"
18 | )
19 |
20 | class(expected$.role) <- "list"
21 |
22 | expect_equal(expected, output)
23 | })
24 |
25 |
26 | test_that("Test formula with two outcome variables", {
27 |
28 | output <-
29 | torchts_parse_formula(max_temp + min_temp ~ max_temp + date, tarnow_temp)
30 |
31 | expected <- tribble(
32 | ~ .var, ~ .role, ~ .class, ~ .type,
33 | "max_temp", "outcome", "numeric", "numeric",
34 | "min_temp", "outcome", "numeric", "numeric",
35 | "max_temp", "predictor", "numeric", "numeric",
36 | "date", "index", "Date", "date"
37 | )
38 |
39 | class(expected$.role) <- "list"
40 |
41 | expect_equal(expected, output)
42 | })
43 |
44 |
45 | test_that("Test formula, where outcome is not an input variable as well", {
46 |
47 | output <-
48 | torchts_parse_formula(min_temp ~ max_temp + date, tarnow_temp)
49 |
50 | expected <- tribble(
51 | ~ .var, ~ .role, ~ .class, ~ .type,
52 | "min_temp", "outcome", "numeric", "numeric",
53 | "max_temp", "predictor", "numeric", "numeric",
54 | "date", "index", "Date", "date"
55 | )
56 |
57 | class(expected$.role) <- "list"
58 |
59 | expect_equal(expected, output)
60 | })
61 |
62 |
63 | test_that("Test formula without explicit predictors", {
64 |
65 | output <-
66 | torchts_parse_formula(max_temp + min_temp ~ date, tarnow_temp)
67 |
68 | expected <- tribble(
69 | ~ .var, ~ .role, ~ .class, ~ .type,
70 | "max_temp", "outcome", "numeric", "numeric",
71 | "min_temp", "outcome", "numeric", "numeric",
72 | "date", "index", "Date", "date",
73 | "max_temp", "predictor", "numeric", "numeric",
74 | "min_temp", "predictor", "numeric", "numeric"
75 | )
76 |
77 | class(expected$.role) <- "list"
78 |
79 | expect_equal(expected, output)
80 | })
81 |
82 | test_that("Formula outcome variables order", {
83 |
84 | parsed_formula_1 <-
85 | torchts_parse_formula(max_temp + min_temp ~ date, tarnow_temp)
86 |
87 | outcome_1 <- filter(parsed_formula_1, .role == "outcome")$.var
88 |
89 | expect_equal(outcome_1, c("max_temp", "min_temp"))
90 |
91 |
92 | parsed_formula_2 <-
93 | torchts_parse_formula(min_temp + max_temp ~ date, tarnow_temp)
94 |
95 | outcome_2 <- filter(parsed_formula_2, .role == "outcome")$.var
96 |
97 | expect_equal(outcome_2, c("min_temp", "max_temp"))
98 |
99 | })
100 |
101 | test_that("Variables that not appear in the data.frame", {
102 |
103 | expect_error(
104 | torchts_parse_formula(max_temp + min_temperature ~ date, tarnow_temp)
105 | )
106 |
107 | })
108 |
109 |
110 | test_that("Test formula with multiple predictors and outcomes where index is first", {
111 |
112 | output <-
113 | torchts_parse_formula(
114 | min_temp + max_temp ~ date + min_temp + max_temp,
115 | tarnow_temp
116 | )
117 |
118 | expected <- tribble(
119 | ~ .var, ~ .role, ~ .class, ~ .type,
120 | "min_temp", "outcome", "numeric", "numeric",
121 | "max_temp", "outcome", "numeric", "numeric",
122 | "date", "index", "Date", "date",
123 | "min_temp", "predictor", "numeric", "numeric",
124 | "max_temp", "predictor", "numeric", "numeric"
125 | )
126 |
127 | class(expected$.role) <- "list"
128 |
129 | expect_equal(expected, output)
130 | })
131 |
132 |
133 | test_that("Test formula with a modifier", {
134 |
135 | output <-
136 | torchts_parse_formula(
137 | min_temp ~ date + min_temp + max_temp + lead(max_temp, 5),
138 | tarnow_temp
139 | )
140 |
141 | expected <- tribble(
142 | ~ .var, ~ .role, ~ .modifier, ~ .class, ~ .type,
143 | "min_temp", "outcome", NA, "numeric", "numeric",
144 | "date", "index", NA, "Date", "date",
145 | "min_temp", "predictor", NA, "numeric", "numeric",
146 | "max_temp", "predictor", NA, "numeric", "numeric",
147 | "max_temp", "predictor", "lead(5)", "numeric", "numeric"
148 | )
149 |
150 | class(expected$.role) <- "list"
151 |
152 | expect_equal(expected, output)
153 | })
154 |
155 |
156 |
157 |
158 |
--------------------------------------------------------------------------------
/tests/testthat/test-ts-dataset.R:
--------------------------------------------------------------------------------
1 | test_that("Test ts_dataset", {
2 |
3 | weather_pl_tensor <-
4 | weather_pl %>%
5 | filter(station == "TRN") %>%
6 | select(-station, -rr_type) %>%
7 | as_tensor(date)
8 |
9 | weather_pl_dataset <-
10 | ts_dataset(
11 | data = weather_pl_tensor,
12 | timesteps = 28,
13 | horizon = 7,
14 | predictors_spec = list(x = 2:7),
15 | outcomes_spec = list(y = 1),
16 | scale = TRUE
17 | )
18 |
19 | batch <-
20 | weather_pl_dataset$.getitem(1)
21 |
22 | expect_equal(dim(batch$x), c(28, 6))
23 | expect_equal(dim(batch$y), 7)
24 |
25 | })
26 |
27 |
28 | test_that("Passing non-tabular data as first argument", {
29 |
30 | weather_pl_tensor <-
31 | weather_pl %>%
32 | select(-rr_type) %>%
33 | as_tensor(station, date)
34 |
35 | expect_error(
36 | ts_dataset(
37 | data = weather_pl_tensor,
38 | timesteps = 28,
39 | horizon = 7,
40 | predictors_spec = list(x = 2:7),
41 | outcomes_spec = list(y = 1),
42 | scale = TRUE
43 | )
44 | )
45 |
46 | })
47 |
48 |
49 | test_that("Test ts_dataset jump option", {
50 |
51 | weather_pl_tensor <-
52 | weather_pl %>%
53 | filter(station == "TRN") %>%
54 | select(-station, -rr_type) %>%
55 | as_tensor(date)
56 |
57 | DS_2_JUMP <- 7
58 |
59 | ds_1 <-
60 | ts_dataset(
61 | data = weather_pl_tensor,
62 | timesteps = 28,
63 | horizon = 7,
64 | jump = 1,
65 | predictors_spec = list(x = 2:7),
66 | outcomes_spec = list(y = 1),
67 | scale = TRUE
68 | )
69 |
70 | ds_2 <-
71 | ts_dataset(
72 | data = weather_pl_tensor,
73 | timesteps = 28,
74 | horizon = 7,
75 | jump = DS_2_JUMP,
76 | predictors_spec = list(x = 2:7),
77 | outcomes_spec = list(y = 1),
78 | scale = TRUE
79 | )
80 |
81 | expected_ds_2_length <- floor(length(ds_1) / DS_2_JUMP)
82 |
83 | expect_equal(length(ds_2), expected_ds_2_length)
84 | })
85 |
86 | # TODO: add additional test to check scaling
87 |
88 |
--------------------------------------------------------------------------------
/tests/testthat/test-utils.R:
--------------------------------------------------------------------------------
1 | tarnow_temp <-
2 | weather_pl %>%
3 | filter(station == "TRN") %>%
4 | select(date, tmax_daily, tmin_daily)
5 |
6 | test_that("Test vars_with_role", {
7 |
8 | parsed_formula_1 <-
9 | torchts_parse_formula(tmax_daily + tmin_daily ~ date, tarnow_temp)
10 |
11 | expect_equal(
12 | vars_with_role(parsed_formula_1, "predictor"),
13 | c("tmax_daily", "tmin_daily")
14 | )
15 |
16 | expect_equal(
17 | vars_with_role(parsed_formula_1, "outcome"),
18 | c("tmax_daily", "tmin_daily")
19 | )
20 |
21 | expect_equal(
22 | vars_with_role(parsed_formula_1, "index"),
23 | c("date")
24 | )
25 |
26 | })
27 |
28 |
29 | test_that("Test empty_rows", {
30 |
31 | EMPTY <- 5
32 |
33 | iris_with_empty_rows <- preprend_empty(iris, EMPTY)
34 |
35 | expect_equal(
36 | nrow(iris_with_empty_rows),
37 | nrow(iris) + EMPTY
38 | )
39 |
40 | expect_true(all(is.na(
41 | iris_with_empty_rows[1:EMPTY, ]
42 | )))
43 |
44 | expect_false(all(is.na(
45 | iris_with_empty_rows[1:(2 * EMPTY), ]
46 | )))
47 |
48 | })
49 |
50 |
--------------------------------------------------------------------------------
/torchts.Rproj:
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1 | Version: 1.0
2 |
3 | RestoreWorkspace: No
4 | SaveWorkspace: No
5 | AlwaysSaveHistory: Default
6 |
7 | EnableCodeIndexing: Yes
8 | UseSpacesForTab: Yes
9 | NumSpacesForTab: 2
10 | Encoding: UTF-8
11 |
12 | RnwWeave: Sweave
13 | LaTeX: pdfLaTeX
14 |
15 | AutoAppendNewline: Yes
16 | StripTrailingWhitespace: Yes
17 | LineEndingConversion: Posix
18 |
19 | BuildType: Package
20 | PackageUseDevtools: Yes
21 | PackageInstallArgs: --no-multiarch --with-keep.source
22 | PackageRoxygenize: rd,collate,namespace
23 |
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/vignettes/.gitignore:
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1 | *.html
2 | *.R
3 |
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/vignettes/naming-convention.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | title: "Naming convention"
3 | output: rmarkdown::html_vignette
4 | vignette: >
5 | %\VignetteIndexEntry{Naming convention}
6 | %\VignetteEngine{knitr::rmarkdown}
7 | %\VignetteEncoding{UTF-8}
8 | ---
9 |
10 | ```{r, include = FALSE}
11 | knitr::opts_chunk$set(
12 | collapse = TRUE,
13 | comment = "#>"
14 | )
15 | ```
16 |
17 | It should not be surprising that in any field of human activity, when many persons work (more or less) independently across the world, the same thing can be known by different terms. For example, in the classical statistics, the same variables can be described as "explanatory variables" or "predictors" while in the related ML world they are called "features" or "input variables". For more elaborate text on this topic, read [Statistical Modeling: The Two Cultures](https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.full) by Leo Breiman or concise subchapter from [EMA book](https://ema.drwhy.ai/introduction.html#teminology) by P. Biecek and T. Burzykowski (they refer to Breiman's paper).
18 |
19 | In this vignette we consider not only the data science terminology, but also the names existing in different packages' APIs (what is, obviously, partially based on the scientific naming).
20 |
21 | In the table below you can see, what names is decided to use, where they come from and what are their synonyms in the scientific world or among the other R/Python packages API etc.
22 |
23 | |**Name** | **Explanation**| **Consistent with** | **Other names**|
24 | |:-------:|----------------|---------------------|----------------|
25 | |`index` | A time variable like `Date`| Inspired by `index` variable in [`tsibble`](https://tsibble.tidyverts.org) package | • `.date_var` in [`timetk`](https://business-science.github.io/timetk/) • `time_idx` in `Python` library [`pytorch-forecasting`](https://pytorch-forecasting.readthedocs.io/en/latest/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html)|
26 | |`key` |A variable (or variables) to distinguish different time series in the dataset| • `tsibble` package • SQL databases • `data.table` | • `id` in [`modeltime.gluonts`](https://business-science.github.io/modeltime.gluonts) (for example: [`deep_ar`](https://business-science.github.io/modeltime.gluonts/reference/deep_ar.html)) • `group_ids` in `Python` library [`pytorch-forecasting`](https://pytorch-forecasting.readthedocs.io/en/latest/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html)|
27 | |`timesteps`|A number of timesteps used to train the model| *Timestep* is a commonly used word in Deep Learning terminology to describe a "moment" in a time series (sequence). |Meaning partially reflected by: • `lookback_length` in `modeltime.gluonts` (e.g. [`nbeats`](https://business-science.github.io/modeltime.gluonts/reference/nbeats.html)) • `lookback` in [`forecastML`](https://github.com/nredell/forecastML#vignettes)|
28 | |`horizon`| Length of a output sequence, i.e. how many steps ahead we'd like to forecast. If we consider that each future timestep refer to a separate horizon, `horizon` is the maximal *horizon* of the forecast | • `horizons` in [`forecastML`](https://github.com/nredell/forecastML#vignettes) • [FPP book](https://otexts.com/fpp3/arima-forecasting.html) by Hyndman and Athanasopoulos • term used in scientific papers, e.g. [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://arxiv.org/pdf/1912.09363.pdf) by Lim et al., by meaning in the context of this particular paper slightly differ (refers to a single timestep in the forecast, not to the maximal length of forecast)|• `prediction_length` in `modeltime.gluonts` (e.g. [`nbeats`](https://business-science.github.io/modeltime.gluonts/reference/nbeats.html)) • `h` in [`forecast`](https://pkg.robjhyndman.com/forecast/reference/forecast.html) package|
29 | |`predictors`| Input variables | [`recipes`](https://recipes.tidymodels.org/reference/index.html) package API from `tidymodels` | • ML: *features* or *input variables* • Statistics: *explanatory variables*, *independent variables* etc. |
30 | |`outcomes`| Target variables | • [`recipes`](https://recipes.tidymodels.org/reference/index.html) package API from `tidymodels` • `outcome_col` variable and *outcome* term in vignettes in [`forecastML`](https://github.com/nredell/forecastML#vignettes) | • ML: *outputs*, *targets* • Statistics: *response*, *dependent variables* etc. • `target` in `Python` library [`pytorch-forecasting`](https://pytorch-forecasting.readthedocs.io/en/latest/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html)|
31 |
32 |
33 | Bear in mind that API may evolve. Especially, if I would like to implement new engines to `parsnip` models provided in `modeltime.gluonts`, we have to stick to the same name arguments in both cases.
34 |
--------------------------------------------------------------------------------
/vignettes/torchts-api.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | title: "torchts ready-to-use models API"
3 | output: rmarkdown::html_vignette
4 | vignette: >
5 | %\VignetteIndexEntry{univariate-time-series}
6 | %\VignetteEngine{knitr::rmarkdown}
7 | %\VignetteEncoding{UTF-8}
8 | ---
9 |
10 | ```{r, include = FALSE}
11 | knitr::opts_chunk$set(
12 | collapse = TRUE,
13 | comment = "#>"
14 | )
15 | ```
16 |
17 | ```{r setup}
18 | library(torch)
19 | #library(torchts)
20 | library(rsample)
21 | library(dplyr, warn.conflicts = FALSE)
22 | library(ggplot2)
23 | library(parsnip)
24 | library(timetk)
25 |
26 | # write.csv(weather_pl, file = "../weather_pl.csv")
27 |
28 | devtools::load_all()
29 | ```
30 |
31 | ## Preparing dataset
32 |
33 | `parsnip` API provides quick and convenient way to train time series models based on `torch` library.
34 |
35 | First, let's prepare input data set using excelent `timetk` library. `weather_pl` dataset from `torchts` package contains 19-year history of weather data from two Polish "poles of extreme temperature", i.e. Suwałki and Tarnów. In this excercise, we'll use a slice of data registered in the "pole of warmth", i.e in the city of [Tarnów](https://en.wikipedia.org/wiki/Tarn%C3%B3w).
36 |
37 | ```{r preparing.data.1}
38 | tarnow_temp <-
39 | weather_pl %>%
40 | filter(station == "TRN") %>%
41 | select(date, tmax_daily, tmin_daily, press_mean_daily)
42 |
43 | tk_summary_diagnostics(tarnow_temp, date)
44 |
45 | tarnow_temp <-
46 | tarnow_temp %>%
47 | mutate(year_day = as.numeric(lubridate::yday(date)))
48 | ```
49 |
50 | As we can see, this is a time series between 1th of Janury 2001 till the end of 2020 - twenty years.
51 | In the next step, we'll split our data into two training and testing datasets using a handy `time_series_split` function.
52 |
53 | ## Univariate time series
54 |
55 | ```{r univariate.ts}
56 | EPOCHS <- 10
57 | HORIZON <- 7
58 | TIMESTEPS <- 28
59 |
60 | data_split <-
61 | time_series_split(
62 | tarnow_temp, date,
63 | initial = "18 years",
64 | assess = "2 years",
65 | lag = TIMESTEPS
66 | )
67 |
68 | mlp_model <-
69 | torchts_mlp(
70 | tmax_daily ~ date + year_day,
71 | data = training(data_split),
72 | timesteps = TIMESTEPS,
73 | horizon = HORIZON,
74 | jump = 1,
75 | epochs = EPOCHS,
76 | learn_rate = 0.001,
77 | hidden_units = c(50, 30),
78 | batch_size = 32,
79 | scale = TRUE,
80 | device = 'cuda'
81 | )
82 |
83 | ```
84 |
85 | We want to generate forecast for a full year. To do so, we'll remove the outcome column values
86 | we'd like to forecast. Bear in mind that input data differs from the inputs for classical tabular-data algorithms.
87 | In this specific case, we have to keep some "history" (in this case: `r TIMESTEPS` steps, i.e: `r TIMESTEPS` days).
88 |
89 | ```{r model.forecast.1}
90 | # Clear outcome variable
91 | # Ostrzeżenie, jeśli wyczyszczono za dużo danych
92 | cleared_new_data <-
93 | testing(data_split) %>%
94 | clear_outcome(date, c(tmax_daily), TIMESTEPS)
95 |
96 | fcast <-
97 | mlp_model %>%
98 | predict(new_data = testing(data_split))
99 |
100 | fcast <-
101 | mlp_model %>%
102 | predict(new_data = cleared_new_data)
103 |
104 | fcast <- tibble(.pred = fcast)
105 |
106 | # plot(ts(fcast))
107 |
108 | plot_forecast(
109 | testing(data_split),
110 | fcast,
111 | tmax_daily
112 | )
113 |
114 | # fcast_vs_true <-
115 | # bind_cols(
116 | # n = 1:nrow(testing(data_split)),
117 | # actual = testing(data_split)$tmax_daily,
118 | # fcast
119 | # ) %>%
120 | # tidyr::pivot_longer(c(actual, .pred))
121 | #
122 | # ggplot(fcast_vs_true) +
123 | # geom_line(aes(n, value, col = name)) +
124 | # theme_minimal() +
125 | # ggtitle("Forecast vs actual values")
126 |
127 | # test_input <- c(
128 | # -0.9743, -1.0750, -1.3369, -1.2966, -1.2866, -0.9440, -1.2866, -1.3168,
129 | # -0.8634, -0.9138, -1.0548, -0.9440, -0.8231, -1.2160, -1.1757, -0.8634,
130 | # -0.6519, -0.5612, -0.8131, -1.2765, -1.1153, -0.6922, -0.3799, -0.5914,
131 | # -0.9843
132 | # )
133 | #
134 | # inp <- torch_tensor(array(test_input, dim = c(1, 28)))$cuda()
135 | #
136 | #
137 | # mlp_model$net(x_num = inp)
138 | ```
139 |
140 |
141 | ## Multivariate time series
142 |
143 | In the next next, we're adding new predictor: `press_mean_daily`. It means that we extend the autoregressive model we obtained in the previous exercise by providing a variable that could be recognised as **external regressor** (what is a term commonly used in the context of methods like ARIMA etc.).
144 |
145 | ```{r multivariate.ts}
146 |
147 | EPOCHS <- 10
148 | HORIZON <- 7
149 | TIMESTEPS <- 28
150 |
151 | data_split <-
152 | time_series_split(
153 | tarnow_temp, date,
154 | initial = "18 years",
155 | assess = "2 years",
156 | lag = TIMESTEPS
157 | )
158 |
159 | rnn_model <-
160 | torchts_rnn(
161 | tmax_daily ~ date + tmax_daily + year_day,
162 | data = training(data_split),
163 | timesteps = TIMESTEPS,
164 | horizon = HORIZON,
165 | jump = 1,
166 | epochs = EPOCHS,
167 | learn_rate = 0.001,
168 | hidden_units = 30,
169 | batch_size = 32,
170 | scale = TRUE,
171 | device = 'cuda'
172 | )
173 | ```
174 |
175 | ```{r model.forecast.2}
176 | # Clear outcome variable
177 | cleared_new_data <-
178 | testing(data_split) %>%
179 | clear_outcome(date, tmax_daily, 20)
180 |
181 | # Forecast
182 | fcast <-
183 | rnn_model %>%
184 | predict(new_data = testing(data_split))
185 |
186 | plot_forecast(
187 | head(testing(data_split), nrow(fcast)),
188 | fcast,
189 | tmax_daily
190 | )
191 |
192 | ```
193 |
194 |
--------------------------------------------------------------------------------
/vignettes/torchts-formula.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | title: "Explaining formulae in torchts"
3 | output: rmarkdown::html_vignette
4 | vignette: >
5 | %\VignetteIndexEntry{Explaining formulae in torchts}
6 | %\VignetteEngine{knitr::rmarkdown}
7 | %\VignetteEncoding{UTF-8}
8 | ---
9 |
10 | ```{r, include = FALSE}
11 | knitr::opts_chunk$set(
12 | collapse = TRUE,
13 | comment = "#>"
14 | )
15 | ```
16 |
17 | Meaning of **formulae** in `torchts` package slightly differs from the typical
18 | way we can understand this object in the most of regression models in R.
19 | It should not be a surprise - some formulae syntax innovations can be found, for instance,
20 | in other time series related packages such as `fable` and `modeltime`.
21 |
22 | This is how we read a formula in the `torchts`:
23 |
24 | ### 1. Simple autoregressive model
25 |
26 |
27 | $value \sim date$
28 |
29 | Future values of **value** column are base on the previous values of this variable.
30 | For now, lags are typically specified in the model object, not in the formula itself
31 | (it may change in the future).
32 |
33 | You should always specify a time-related variable as date, integer index etc.
34 | You are not obliged to specify **value** explicitly on the right side of the formula.
35 |
36 | ### 2. Explanatory and explained variables
37 |
38 | $tmax\_daily \sim tmin\_daily + date$
39 |
40 | Future **tmax_daily** values are based on the previous **tmin_daily** values.
41 | **tmax_daily** values are not taken into account as explanatory variable in this case.
42 |
43 |
44 | ### 3. Explanatory and explained variables(with autoregression)
45 |
48 | Future **tmax_daily** values are based on both
49 | the previous **tmax_daily** and **tmin_daily** values.
50 | It's an example of **autoregression with external regressors**.
51 |
52 |
53 | ### 4. Model with values from the future
54 |
55 | $value \sim date + value + price + lead(price)$
56 |
57 | Future values of the **value** variable are based on the previous values from this column as well
58 | as previous values from the price column and the **future** value from the price column.
59 |
60 | In some cases, especially in the **demand forecasting**, there exist some variable
61 | that are known in advance (as future price or holidays). Sometimes we can treat some other
62 | forecasts (e.g. weather forecast) as a known values to feed the model with.
63 |
--------------------------------------------------------------------------------
18 |
19 |
20 | [](https://CRAN.R-project.org/package=torchts)
21 | [](https://github.com/krzjoa/torchts/actions)
22 | [](https://codecov.io/gh/krzjoa/torchts?branch=master)
23 | [](https://www.redbubble.com/i/sticker/torchts-R-package-hex-sticker-by-krzjoa/93537989.EJUG5)
25 |
26 |
27 |
28 |
29 | > Time series models with torch
30 |
31 | [](https://www.buymeacoffee.com/kjoachimiak)
32 |
33 | ## Installation
34 |
35 | You can install the released version of torchts from [CRAN](https://CRAN.R-project.org) with:
36 |
37 | The development version from [GitHub](https://github.com/) with:
38 |
39 | ``` r
40 | # install.packages("devtools")
41 | devtools::install_github("krzjoa/torchts")
42 | ```
43 |
44 | ## parsnip models
45 |
46 | ```{r parsnip.api}
47 | library(torchts)
48 | library(torch)
49 | library(rsample)
50 | library(dplyr, warn.conflicts = FALSE)
51 | library(parsnip)
52 | library(timetk)
53 | library(ggplot2)
54 |
55 | tarnow_temp <-
56 | weather_pl %>%
57 | filter(station == "TRN") %>%
58 | select(date, tmax_daily)
59 |
60 | # Params
61 | EPOCHS <- 3
62 | HORIZON <- 1
63 | TIMESTEPS <- 28
64 |
65 | # Splitting on training and test
66 | data_split <-
67 | time_series_split(
68 | tarnow_temp, date,
69 | initial = "18 years",
70 | assess = "2 years",
71 | lag = TIMESTEPS
72 | )
73 |
74 | # Training
75 | rnn_model <-
76 | rnn(
77 | timesteps = TIMESTEPS,
78 | horizon = HORIZON,
79 | epochs = EPOCHS,
80 | learn_rate = 0.01,
81 | hidden_units = 20,
82 | batch_size = 32,
83 | scale = TRUE
84 | ) %>%
85 | set_device('cpu') %>%
86 | fit(tmax_daily ~ date,
87 | data = training(data_split))
88 |
89 | prediction <-
90 | rnn_model %>%
91 | predict(new_data = testing(data_split))
92 |
93 | plot_forecast(
94 | data = testing(data_split),
95 | forecast = prediction,
96 | outcome = tmax_daily
97 | )
98 | ```
99 |
100 | ## Transforming data.frames to tensors
101 |
102 | In `as_tensor` function we can specify columns, that are used to
103 | create a tensor out of the input `data.frame`. Listed column names
104 | are only used to determine dimension sizes - they are removed after that
105 | and are not present in the final tensor.
106 |
107 | ```{r example}
108 | temperature_pl <-
109 | weather_pl %>%
110 | select(station, date, tmax_daily)
111 |
112 | # Expected shape
113 | c(
114 | n_distinct(temperature_pl$station),
115 | n_distinct(temperature_pl$date),
116 | 1
117 | )
118 |
119 | temperature_tensor <-
120 | temperature_pl %>%
121 | as_tensor(station, date)
122 |
123 | dim(temperature_tensor)
124 | temperature_tensor[1, 1:10]
125 |
126 | temperature_pl %>%
127 | filter(station == "SWK") %>%
128 | arrange(date) %>%
129 | head(10)
130 | ```
131 |
132 | ## Similar projects in Python
133 |
134 | * [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/stable/)
135 | * [PyTorchTS](https://github.com/zalandoresearch/pytorch-ts)
136 | * [TorchTS](https://rose-stl-lab.github.io/torchTS/)
137 | * [GluonTS ](https://ts.gluon.ai/)
138 | * [sktime-dl](https://github.com/sktime/sktime-dl)
139 |
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/README.md:
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1 |
2 |
3 |
4 | # torchts 
100 |
101 | ## Transforming data.frames to tensors
102 |
103 | In `as_tensor` function we can specify columns, that are used to create
104 | a tensor out of the input `data.frame`. Listed column names are only
105 | used to determine dimension sizes - they are removed after that and are
106 | not present in the final tensor.
107 |
108 | ``` r
109 | temperature_pl <-
110 | weather_pl %>%
111 | select(station, date, tmax_daily)
112 |
113 | # Expected shape
114 | c(
115 | n_distinct(temperature_pl$station),
116 | n_distinct(temperature_pl$date),
117 | 1
118 | )
119 | #> [1] 2 7305 1
120 |
121 | temperature_tensor <-
122 | temperature_pl %>%
123 | as_tensor(station, date)
124 |
125 | dim(temperature_tensor)
126 | #> [1] 2 7305 1
127 | temperature_tensor[1, 1:10]
128 | #> torch_tensor
129 | #> -0.2000
130 | #> -1.4000
131 | #> 0.4000
132 | #> 1.0000
133 | #> 0.6000
134 | #> 3.0000
135 | #> 4.0000
136 | #> 1.0000
137 | #> 1.2000
138 | #> 1.4000
139 | #> [ CPUFloatType{10,1} ]
140 |
141 | temperature_pl %>%
142 | filter(station == "SWK") %>%
143 | arrange(date) %>%
144 | head(10)
145 | #> station date tmax_daily
146 | #> 1140 SWK 2001-01-01 -0.2
147 | #> 1230 SWK 2001-01-02 -1.4
148 | #> 2330 SWK 2001-01-03 0.4
149 | #> 2630 SWK 2001-01-04 1.0
150 | #> 2730 SWK 2001-01-05 0.6
151 | #> 2830 SWK 2001-01-06 3.0
152 | #> 2930 SWK 2001-01-07 4.0
153 | #> 3030 SWK 2001-01-08 1.0
154 | #> 3130 SWK 2001-01-09 1.2
155 | #> 2140 SWK 2001-01-10 1.4
156 | ```
157 |
158 | ## Similar projects in Python
159 |
160 | - [PyTorch
161 | Forecasting](https://pytorch-forecasting.readthedocs.io/en/stable/)
162 | - [PyTorchTS](https://github.com/zalandoresearch/pytorch-ts)
163 | - [TorchTS](https://rose-stl-lab.github.io/torchTS/)
164 | - [GluonTS](https://ts.gluon.ai/)
165 |
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/_pkgdown.yml:
--------------------------------------------------------------------------------
1 | destination: docs
2 | template:
3 | params:
4 | bootswatch: united
5 | authors:
6 | Krzysztof Joachimiak:
7 | href: https://krzjoa.github.io
8 | reference:
9 | - title: torchts quick API
10 | contents:
11 | - torchts_rnn
12 | - torchts_mlp
13 | - title: parsnip API
14 | contents:
15 | - rnn
16 | - lagged_mlp
17 | - title: Modules
18 | contents:
19 | - model_rnn
20 | - model_mlp
21 | - nn_multi_embedding
22 | - nn_nonlinear
23 | - nn_mlp
24 | - title: Data transformations
25 | contents:
26 | - as_tensor
27 | - ts_dataset
28 | - as_ts_dataset
29 | - as_ts_dataloader
30 | - as.vector.torch_tensor
31 | - title: Metrics
32 | contents:
33 | - nnf_mae
34 | - nnf_mape
35 | - nnf_smape
36 | - title: Utils
37 | contents:
38 | - is_categorical
39 | - dict_size
40 | - embedding_size
41 | - clear_outcome
42 | - set_device
43 | - plot_forecast
44 | - title: Data
45 | contents:
46 | - weather_pl
47 | - tiny_m5
48 |
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/codecov.yml:
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1 | comment: false
2 |
3 | coverage:
4 | status:
5 | project:
6 | default:
7 | target: auto
8 | threshold: 1%
9 | informational: true
10 | patch:
11 | default:
12 | target: auto
13 | threshold: 1%
14 | informational: true
15 |
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/data-raw/debug-mlp.Rmd:
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1 | ---
2 | title: "debug"
3 | output: html_document
4 | ---
5 |
6 | ```{r setup, include=FALSE}
7 | knitr::opts_chunk$set(echo = TRUE)
8 | ```
9 |
10 | ## Prepare data
11 |
12 | ```{r libs}
13 | library(torch)
14 | #library(torchts)
15 | library(rsample)
16 | library(dplyr, warn.conflicts = FALSE)
17 | library(ggplot2)
18 | library(parsnip)
19 | library(timetk)
20 |
21 | # write.csv(weather_pl, file = "../weather_pl.csv")
22 |
23 | devtools::load_all()
24 | ```
25 |
26 | ## Data
27 | ```{r data}
28 | tarnow_temp <-
29 | weather_pl %>%
30 | filter(station == 'TRN') %>%
31 | arrange(date)
32 | head(tarnow_temp)
33 |
34 |
35 | train <- tarnow_temp %>%
36 | filter(date < as.Date('2018-01-01'))
37 |
38 | test <- tarnow_temp %>%
39 | filter(date >= as.Date('2018-01-01'))
40 | ```
41 |
42 | ```{r ts_dataset}
43 |
44 | TimeSeriesDataset <- torch::dataset(
45 | "TimeSeriesDataSet",
46 |
47 | initialize = function(ts, lookback, horizon, jump, trim_last = TRUE){
48 | # TS
49 | self$ts <- ts
50 | self$lookback <- lookback
51 | self$horizon <- horizon
52 | self$jump <- jump
53 |
54 | # Non overlapping chunks
55 | # Tu jest błąd
56 | self$chunk_size <- (lookback + horizon)
57 | if (trim_last)
58 | self$length <- (length(ts) - self$chunk_size ) %/% jump
59 | else
60 | self$length <- (length(ts) - self$horizon) %/% jump
61 | },
62 |
63 | .length = function(){
64 | self$length
65 | },
66 |
67 | .getitem = function(idx){
68 | # Input
69 | first <- (idx - 1) * self$jump + 1
70 | last_input <- first + self$lookback - 1
71 | X <- self$ts[first:last_input]
72 |
73 | # Output
74 | y <- self$ts[last_input:(last_input + self$horizon - 1)]
75 |
76 | X_tensor <- torch_tensor(X, dtype = torch_float32())
77 | y_tensor <- torch_tensor(y, dtype = torch_float32())
78 |
79 | return(list(
80 | X_tensor$squeeze()$cuda(),
81 | y_tensor$squeeze()$cuda()
82 | ))
83 | }
84 | )
85 | ```
86 |
87 |
88 | ```{r declare.vars}
89 | TIMESTEPS <- 28
90 | HORIZON <- 7
91 | ```
92 |
93 | ```{r scale.data}
94 | mean_val <- mean(train$tmax_daily)
95 | sd_val <- sd(train$tmax_daily)
96 |
97 | train_scaled <- (train$tmax_daily - mean_val) / sd_val
98 | test_scaled <- (test$tmax_daily - mean_val) / sd_val
99 | ```
100 |
101 | ```{r create.ds}
102 | train_ds <- TimeSeriesDataset(train_scaled, TIMESTEPS, HORIZON, 1)
103 | test_ds <- TimeSeriesDataset(test_scaled, TIMESTEPS, HORIZON, HORIZON, FALSE)
104 | ```
105 |
106 |
107 | ```{r cmp.data}
108 | test_ds[1][1]
109 | ```
110 |
111 | ```{r creat.net}
112 | MLP <-
113 | nn_module(
114 |
115 | "MLP",
116 |
117 | initialize = function(input_size, output_size, layers){
118 | self$linear_1 <- nn_linear(input_size, layers[1])
119 | self$activation_1 <- nn_relu()
120 | self$linear_2 <- nn_linear(layers[1], layers[2])
121 | self$activation_2 <- nn_relu()
122 | self$linear_3 <- nn_linear(layers[2], output_size)
123 | },
124 |
125 |
126 | forward = function(X){
127 | X <- self$activation_1(self$linear_1(X))
128 | X <- self$activation_2(self$linear_2(X))
129 | self$linear_3(X)
130 | }
131 | )
132 | ```
133 |
134 | ```{r init.net}
135 | net <- MLP(TIMESTEPS, HORIZON, c(50, 30))
136 | net <- net$cuda()
137 | epochs <- 10
138 | ```
139 |
140 | ```{r optimizer}
141 | optimizer <- optim_adam(net$parameters)
142 | loss_fun <- nn_mse_loss()
143 |
144 | epochs <- 30
145 |
146 | #X, y = next(iter(train_dl))
147 | #X.shape
148 |
149 | ```
150 |
151 | ```{r creating.dls}
152 | train_dl <- dataloader(train_ds, batch_size = 32)
153 | test_dl <- dataloader(test_ds, batch_size = 1)
154 | ```
155 |
156 | ```{r training.loop}
157 | dataloader_next(
158 | dataloader_make_iter(train_dl)
159 | )[1]
160 |
161 |
162 | net$train()
163 |
164 | for (e in seq_len(epochs)) {
165 | train_loss <- 0.0
166 |
167 | coro::loop(for (b in train_dl) {
168 |
169 | X <- b[[1]]
170 | y <- b[[2]]
171 |
172 | optimizer$zero_grad()
173 | target <- net(X)
174 | loss <- loss_fun(target, y)
175 | # Calculate gradients
176 | loss$backward()
177 | # Update Weights
178 | optimizer$step()
179 | # Calculate Loss
180 | train_loss <- train_loss + loss$item()
181 | })
182 | print(glue::glue(
183 | 'Epoch {e} \t\t Training Loss: {train_loss / length(train_dl)}'
184 | ))
185 | }
186 |
187 | ```
188 | ```{r forecast}
189 | # Forecast
190 | forecast <- function(net, test_dl, timesteps){
191 | targets <- rep(NA, timesteps)
192 | net$eval()
193 | coro::loop(for(b in test_dl){
194 | X <- b[[1]]
195 | # y <- b[[2]]
196 | # print(dim(X))
197 | if (dim(X)[2] == TIMESTEPS) {
198 | out <- net(X)$cpu()$flatten()$detach()
199 | out <- as.vector(out)
200 | targets <- c(out, targets)
201 | }
202 | })
203 | targets
204 | }
205 | ```
206 |
207 | ```{r fcast}
208 | fcast <- forecast(net, test_dl, TIMESTEPS)
209 | ```
210 |
211 | ```{r show.fcast}
212 | plot(ts(fcast))
213 | fcast
214 | ```
215 | # Diagnoza
216 | * przepisać i zdebugować ts_dataset
217 | * zdebugować i być może przepisać torchts_mlp
218 | * przyda się wizualizacja tych sieci
219 | * wejściem powinien być data.frame, a nie tensor -> dzięki temu można np. użyć disk.frame a może nawet połączenia do bazy!
220 | * skalować od razu na wejściu do datasetu -> obecny scenariusz niewiele daje(?)
221 |
222 |
223 |
224 |
225 |
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/data-raw/prepare-logo.R:
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1 | library(ggplot2)
2 | library(data.table)
3 |
4 | devtools::install_github("coolbutuseless/minisvg") # SVG creation
5 | devtools::install_github("coolbutuseless/devout") # Device interface
6 | devtools::install_github("coolbutuseless/devoutsvg") # This package
7 |
8 | shampoo <- read.csv("https://raw.githubusercontent.com/jbrownlee/Datasets/master/shampoo.csv")
9 | setDT(shampoo)
10 | shampoo[, n := 1:.N]
11 |
12 | plt <-
13 | ggplot(shampoo) +
14 | geom_line(aes(n, Sales, lwd = 0.1)) +
15 | theme_void() +
16 | theme(legend.position="none")
17 |
18 | devoutsvg::svgout(
19 | filename = here::here("shampoo.svg"),
20 | width = 8, height = 4,
21 | )
22 |
23 | plt
24 |
25 | invisible(dev.off())
26 |
27 | ggsave("torch.svg")
28 |
29 |
30 | plt
31 |
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/data-raw/prepare-weather-pl.R:
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1 | suppressMessages(library(dplyr))
2 |
3 | temp_data_set <-
4 | climate::meteo_imgw_daily(year = 2001:2020)
5 |
6 | # temp_data_set %>%
7 | # mutate(date = lubridate::make_date(yy, mm, day)) %>%
8 | # group_by(station) %>%
9 | # summarise(max_date = max(date),
10 | # min_date = min(date), n = n())
11 |
12 | weather_pl <-
13 | temp_data_set %>%
14 | filter(station %in% c("SUWAŁKI", "TARNÓW")) %>%
15 | mutate(date = lubridate::make_date(yy, mm, day)) %>%
16 | select(-rank, -id, -yy, -mm, -day) %>%
17 | select(station, date, starts_with("tm"), starts_with("rr"), starts_with("press"))
18 |
19 | weather_pl <-
20 | weather_pl %>%
21 | mutate(station = case_when(
22 | station == "TARNÓW" ~ "TRN",
23 | station == "SUWAŁKI" ~ "SWK"
24 | ))
25 |
26 | save(weather_pl, file = here::here("data/weather_pl.rda"))
27 |
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3 | // Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
4 |
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2 | // v0.0.1
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4 |
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1 | // Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
2 | // v0.0.1
3 | // Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
4 |
5 | document.addEventListener('DOMContentLoaded', function() {
6 | const codeList = document.getElementsByClassName("sourceCode");
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14 | }
15 | });
16 |
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1 | // Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
2 | // v0.0.1
3 | // Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
4 |
5 | document.addEventListener('DOMContentLoaded', function() {
6 | const codeList = document.getElementsByClassName("sourceCode");
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2 | // v0.0.1
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4 |
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1 | /*!
2 | * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
3 | * Copyright 2015 Aidan Feldman
4 | * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
5 |
6 | /* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */
7 |
8 | /* All levels of nav */
9 | nav[data-toggle='toc'] .nav > li > a {
10 | display: block;
11 | padding: 4px 20px;
12 | font-size: 13px;
13 | font-weight: 500;
14 | color: #767676;
15 | }
16 | nav[data-toggle='toc'] .nav > li > a:hover,
17 | nav[data-toggle='toc'] .nav > li > a:focus {
18 | padding-left: 19px;
19 | color: #563d7c;
20 | text-decoration: none;
21 | background-color: transparent;
22 | border-left: 1px solid #563d7c;
23 | }
24 | nav[data-toggle='toc'] .nav > .active > a,
25 | nav[data-toggle='toc'] .nav > .active:hover > a,
26 | nav[data-toggle='toc'] .nav > .active:focus > a {
27 | padding-left: 18px;
28 | font-weight: bold;
29 | color: #563d7c;
30 | background-color: transparent;
31 | border-left: 2px solid #563d7c;
32 | }
33 |
34 | /* Nav: second level (shown on .active) */
35 | nav[data-toggle='toc'] .nav .nav {
36 | display: none; /* Hide by default, but at >768px, show it */
37 | padding-bottom: 10px;
38 | }
39 | nav[data-toggle='toc'] .nav .nav > li > a {
40 | padding-top: 1px;
41 | padding-bottom: 1px;
42 | padding-left: 30px;
43 | font-size: 12px;
44 | font-weight: normal;
45 | }
46 | nav[data-toggle='toc'] .nav .nav > li > a:hover,
47 | nav[data-toggle='toc'] .nav .nav > li > a:focus {
48 | padding-left: 29px;
49 | }
50 | nav[data-toggle='toc'] .nav .nav > .active > a,
51 | nav[data-toggle='toc'] .nav .nav > .active:hover > a,
52 | nav[data-toggle='toc'] .nav .nav > .active:focus > a {
53 | padding-left: 28px;
54 | font-weight: 500;
55 | }
56 |
57 | /* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */
58 | nav[data-toggle='toc'] .nav > .active > ul {
59 | display: block;
60 | }
61 |
--------------------------------------------------------------------------------
/docs/bootstrap-toc.js:
--------------------------------------------------------------------------------
1 | /*!
2 | * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
3 | * Copyright 2015 Aidan Feldman
4 | * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
5 | (function() {
6 | 'use strict';
7 |
8 | window.Toc = {
9 | helpers: {
10 | // return all matching elements in the set, or their descendants
11 | findOrFilter: function($el, selector) {
12 | // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/
13 | // http://stackoverflow.com/a/12731439/358804
14 | var $descendants = $el.find(selector);
15 | return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])');
16 | },
17 |
18 | generateUniqueIdBase: function(el) {
19 | var text = $(el).text();
20 | var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-');
21 | return anchor || el.tagName.toLowerCase();
22 | },
23 |
24 | generateUniqueId: function(el) {
25 | var anchorBase = this.generateUniqueIdBase(el);
26 | for (var i = 0; ; i++) {
27 | var anchor = anchorBase;
28 | if (i > 0) {
29 | // add suffix
30 | anchor += '-' + i;
31 | }
32 | // check if ID already exists
33 | if (!document.getElementById(anchor)) {
34 | return anchor;
35 | }
36 | }
37 | },
38 |
39 | generateAnchor: function(el) {
40 | if (el.id) {
41 | return el.id;
42 | } else {
43 | var anchor = this.generateUniqueId(el);
44 | el.id = anchor;
45 | return anchor;
46 | }
47 | },
48 |
49 | createNavList: function() {
50 | return $('');
51 | },
52 |
53 | createChildNavList: function($parent) {
54 | var $childList = this.createNavList();
55 | $parent.append($childList);
56 | return $childList;
57 | },
58 |
59 | generateNavEl: function(anchor, text) {
60 | var $a = $('');
61 | $a.attr('href', '#' + anchor);
62 | $a.text(text);
63 | var $li = $('');
64 | $li.append($a);
65 | return $li;
66 | },
67 |
68 | generateNavItem: function(headingEl) {
69 | var anchor = this.generateAnchor(headingEl);
70 | var $heading = $(headingEl);
71 | var text = $heading.data('toc-text') || $heading.text();
72 | return this.generateNavEl(anchor, text);
73 | },
74 |
75 | // Find the first heading level (`