tests/testthat/_snaps/forecast-setup.md

In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values

error with custom model without providing predict_model

Code
  model_custom_arima_temp <- model_arima_temp
  class(model_custom_arima_temp) <- "whatever"
  explain_forecast(model = model_custom_arima_temp, y = data[1:150, "Temp"],
  xreg = data[, "Wind"], train_idx = 2:148, explain_idx = 149:150,
  explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Message
  Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
  Consistency checks between model and data is therefore disabled.

Condition
  Error in `get_predict_model()`:
  ! You passed a model to explain() which is not natively supported, and did not supply the 'predict_model' function to explain().
  See ?shapr::explain or the vignette for more information on how to run shapr with custom models.

erroneous input: x_train/x_explain

Code
  y_wrong_format <- data[, c("Temp", "Wind")]
  explain_forecast(model = model_arima_temp, y = y_wrong_format, xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
  n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! `y` has 2 columns (Temp,Wind).
  `explain_y_lags` has length 1.
  These two should match.

Code
  xreg_wrong_format <- data[, c("Temp", "Wind")]
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = xreg_wrong_format,
  train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
  n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! `xreg` has 2 columns (Temp,Wind).
  `explain_xreg_lags` has length 1.
  These two should match.

Code
  xreg_no_column_names <- data[, "Wind"]
  names(xreg_no_column_names) <- NULL
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = xreg_no_column_names,
  train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
  n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! `xreg` misses column names.

erroneous input: model

Code
  explain_forecast(y = data[1:150, "Temp"], xreg = data[, "Wind"], train_idx = 2:
    148, explain_idx = 149:150, explain_y_lags = 2, explain_xreg_lags = 2,
  horizon = 3, approach = "independence", prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `explain_forecast()`:
  ! argument "model" is missing, with no default

erroneous input: prediction_zero

Code
  p0_wrong_length <- p0_ar[1:2]
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_wrong_length,
  n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `prediction_zero` (77.8823529411765, 77.8823529411765) must be numeric and match the output size of the model (3).

erroneous input: n_combinations

Code
  horizon <- 3
  explain_y_lags <- 2
  explain_xreg_lags <- 2
  n_combinations <- horizon + explain_y_lags + explain_xreg_lags - 1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags,
  explain_xreg_lags = explain_xreg_lags, horizon = horizon, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1, n_combinations = n_combinations,
  group_lags = FALSE)
Message
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.

Condition
  Error in `check_n_combinations()`:
  ! `n_combinations` (6) has to be greater than the number of components to decompose  the forecast onto:
  `horizon` (3) + `explain_y_lags` (2) + sum(`explain_xreg_lags`) (2).

Code
  horizon <- 3
  explain_y_lags <- 2
  explain_xreg_lags <- 2
  n_combinations <- 1 + 1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags,
  explain_xreg_lags = explain_xreg_lags, horizon = horizon, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1, n_combinations = n_combinations,
  group_lags = TRUE)
Message
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.

Condition
  Error in `check_n_combinations()`:
  ! `n_combinations` (2) has to be greater than the number of components to decompose the forecast onto:
  ncol(`xreg`) (1) + 1

erroneous input: train_idx

Code
  train_idx_too_short <- 2
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = train_idx_too_short, explain_idx = 149:150,
  explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `train_idx` must be a vector of positive finite integers and length > 1.

Code
  train_idx_not_integer <- c(3:5) + 0.1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = train_idx_not_integer, explain_idx = 149:150,
  explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `train_idx` must be a vector of positive finite integers and length > 1.

Code
  train_idx_out_of_range <- 1:5
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = train_idx_out_of_range, explain_idx = 149:150,
  explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! The train (`train_idx`) and explain (`explain_idx`) indices must fit in the lagged data.
  The lagged data begins at index 2 and ends at index 150.

erroneous input: explain_idx

Code
  explain_idx_not_integer <- c(3:5) + 0.1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = explain_idx_not_integer,
  explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `explain_idx` must be a vector of positive finite integers.

Code
  explain_idx_out_of_range <- 1:5
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = explain_idx_out_of_range,
  explain_y_lags = 2, explain_xreg_lags = 2, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! The train (`train_idx`) and explain (`explain_idx`) indices must fit in the lagged data.
  The lagged data begins at index 2 and ends at index 150.

erroneous input: explain_y_lags

Code
  explain_y_lags_negative <- -1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags_negative,
  explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
  n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `explain_y_lags` must be a vector of positive finite integers.

Code
  explain_y_lags_not_integer <- 2.1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags_not_integer,
  explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
  n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `explain_y_lags` must be a vector of positive finite integers.

Code
  explain_y_lags_more_than_one <- c(1, 2)
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = explain_y_lags_more_than_one,
  explain_xreg_lags = 2, horizon = 3, approach = "independence", prediction_zero = p0_ar,
  n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! `y` has 1 columns (Temp).
  `explain_y_lags` has length 2.
  These two should match.

Code
  explain_y_lags_zero <- 0
  explain_forecast(model = model_arima_temp_noxreg, y = data[1:150, "Temp"],
  train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 0, horizon = 3,
  approach = "independence", prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! `explain_y_lags=0` is not allowed for models without exogeneous variables

erroneous input: explain_x_lags

Code
  explain_xreg_lags_negative <- -2
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = explain_xreg_lags_negative, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `explain_xreg_lags` must be a vector of positive finite integers.

Code
  explain_xreg_lags_not_integer <- 2.1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = explain_xreg_lags_not_integer, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `explain_xreg_lags` must be a vector of positive finite integers.

Code
  explain_x_lags_wrong_length <- c(1, 2)
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = explain_x_lags_wrong_length, horizon = 3, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_data_forecast()`:
  ! `xreg` has 1 columns (Wind).
  `explain_xreg_lags` has length 2.
  These two should match.

erroneous input: horizon

Code
  horizon_negative <- -2
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = 2, horizon = horizon_negative, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `horizon` must be a vector (or scalar) of positive integers.

Code
  horizon_not_integer <- 2.1
  explain_forecast(model = model_arima_temp, y = data[1:150, "Temp"], xreg = data[,
    "Wind"], train_idx = 2:148, explain_idx = 149:150, explain_y_lags = 2,
  explain_xreg_lags = 2, horizon = horizon_not_integer, approach = "independence",
  prediction_zero = p0_ar, n_batches = 1)
Condition
  Error in `get_parameters()`:
  ! `horizon` must be a vector (or scalar) of positive integers.

NorskRegnesentral/shapr documentation built on April 19, 2024, 1:19 p.m.