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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#| default_exp common._model_checks" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "The autoreload extension is already loaded. To reload it, use:\n", | ||
| " %reload_ext autoreload\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "#| hide\n", | ||
| "%load_ext autoreload\n", | ||
| "%autoreload 2" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# 1. Checks for models" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "This file provides a set of unit tests for all models" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#| export\n", | ||
| "import pandas as pd\n", | ||
| "import neuralforecast.losses.pytorch as losses\n", | ||
| "\n", | ||
| "from neuralforecast import NeuralForecast\n", | ||
| "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic, generate_series" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#| export\n", | ||
| "seed = 0\n", | ||
| "test_size = 14\n", | ||
| "FREQ = \"D\"\n", | ||
| "\n", | ||
| "# 1 series, no exogenous\n", | ||
| "N_SERIES_1 = 1\n", | ||
| "df = generate_series(n_series=N_SERIES_1, seed=seed, freq=FREQ, equal_ends=True)\n", | ||
| "max_ds = df.ds.max() - pd.Timedelta(test_size, FREQ)\n", | ||
| "Y_TRAIN_DF_1 = df[df.ds < max_ds]\n", | ||
| "Y_TEST_DF_1 = df[df.ds >= max_ds]\n", | ||
| "\n", | ||
| "# 5 series, no exogenous\n", | ||
| "N_SERIES_2 = 5\n", | ||
| "df = generate_series(n_series=N_SERIES_2, seed=seed, freq=FREQ, equal_ends=True)\n", | ||
| "max_ds = df.ds.max() - pd.Timedelta(test_size, FREQ)\n", | ||
| "Y_TRAIN_DF_2 = df[df.ds < max_ds]\n", | ||
| "Y_TEST_DF_2 = df[df.ds >= max_ds]\n", | ||
| "\n", | ||
| "# 1 series, with static and temporal exogenous\n", | ||
| "N_SERIES_3 = 1\n", | ||
| "df, STATIC_3 = generate_series(n_series=N_SERIES_3, n_static_features=2, \n", | ||
| " n_temporal_features=2, seed=seed, freq=FREQ, equal_ends=True)\n", | ||
| "max_ds = df.ds.max() - pd.Timedelta(test_size, FREQ)\n", | ||
| "Y_TRAIN_DF_3 = df[df.ds < max_ds]\n", | ||
| "Y_TEST_DF_3 = df[df.ds >= max_ds]\n", | ||
| "\n", | ||
| "# 5 series, with static and temporal exogenous\n", | ||
| "N_SERIES_4 = 5\n", | ||
| "df, STATIC_4 = generate_series(n_series=N_SERIES_4, n_static_features=2, \n", | ||
| " n_temporal_features=2, seed=seed, freq=FREQ, equal_ends=True)\n", | ||
| "max_ds = df.ds.max() - pd.Timedelta(test_size, FREQ)\n", | ||
| "Y_TRAIN_DF_4 = df[df.ds < max_ds]\n", | ||
| "Y_TEST_DF_4 = df[df.ds >= max_ds]\n", | ||
| "\n", | ||
| "# Generic test for a given config for a model\n", | ||
| "def _run_model_tests(model_class, config):\n", | ||
| " if model_class.RECURRENT:\n", | ||
| " config[\"inference_input_size\"] = config[\"input_size\"]\n", | ||
| "\n", | ||
| " # DF_1\n", | ||
| " if model_class.MULTIVARIATE:\n", | ||
| " config[\"n_series\"] = N_SERIES_1\n", | ||
| " if isinstance(config[\"loss\"], losses.relMSE):\n", | ||
| " config[\"loss\"].y_train = Y_TRAIN_DF_1[\"y\"].values \n", | ||
| " if isinstance(config[\"valid_loss\"], losses.relMSE):\n", | ||
| " config[\"valid_loss\"].y_train = Y_TRAIN_DF_1[\"y\"].values \n", | ||
| "\n", | ||
| " model = model_class(**config)\n", | ||
| " fcst = NeuralForecast(models=[model], freq=FREQ)\n", | ||
| " fcst.fit(df=Y_TRAIN_DF_1, val_size=24)\n", | ||
| " forecasts = fcst.predict(futr_df=Y_TEST_DF_1)\n", | ||
| " assert forecasts.shape == (7, 2), f\"Forecast does not have the right shape: {forecasts.shape}\"\n", | ||
| " # DF_2\n", | ||
| " if model_class.MULTIVARIATE:\n", | ||
| " config[\"n_series\"] = N_SERIES_2\n", | ||
| " if isinstance(config[\"loss\"], losses.relMSE):\n", | ||
| " config[\"loss\"].y_train = Y_TRAIN_DF_2[\"y\"].values \n", | ||
| " if isinstance(config[\"valid_loss\"], losses.relMSE):\n", | ||
| " config[\"valid_loss\"].y_train = Y_TRAIN_DF_2[\"y\"].values\n", | ||
| " model = model_class(**config)\n", | ||
| " fcst = NeuralForecast(models=[model], freq=FREQ)\n", | ||
| " fcst.fit(df=Y_TRAIN_DF_2, val_size=24)\n", | ||
| " forecasts = fcst.predict(futr_df=Y_TEST_DF_2)\n", | ||
| " assert forecasts.shape == (7, 2), f\"Forecast does not have the right shape: {forecasts.shape}\"\n", | ||
| "\n", | ||
| " if model.EXOGENOUS_STAT and model.EXOGENOUS_FUTR:\n", | ||
| " # DF_3\n", | ||
| " if model_class.MULTIVARIATE:\n", | ||
| " config[\"n_series\"] = N_SERIES_3\n", | ||
| " if isinstance(config[\"loss\"], losses.relMSE):\n", | ||
| " config[\"loss\"].y_train = Y_TRAIN_DF_3[\"y\"].values \n", | ||
| " if isinstance(config[\"valid_loss\"], losses.relMSE):\n", | ||
| " config[\"valid_loss\"].y_train = Y_TRAIN_DF_3[\"y\"].values\n", | ||
| " model = model_class(**config)\n", | ||
| " fcst = NeuralForecast(models=[model], freq=FREQ)\n", | ||
| " fcst.fit(df=Y_TRAIN_DF_3, static_df=STATIC_3, val_size=24)\n", | ||
| " forecasts = fcst.predict(futr_df=Y_TEST_DF_3)\n", | ||
| " assert forecasts.shape == (7, 2), f\"Forecast does not have the right shape: {forecasts.shape}\"\n", | ||
| "\n", | ||
| " # DF_4\n", | ||
| " if model_class.MULTIVARIATE:\n", | ||
| " config[\"n_series\"] = N_SERIES_4\n", | ||
| " if isinstance(config[\"loss\"], losses.relMSE):\n", | ||
| " config[\"loss\"].y_train = Y_TRAIN_DF_4[\"y\"].values \n", | ||
| " if isinstance(config[\"valid_loss\"], losses.relMSE):\n", | ||
| " config[\"valid_loss\"].y_train = Y_TRAIN_DF_4[\"y\"].values \n", | ||
| " model = model_class(**config)\n", | ||
| " fcst = NeuralForecast(models=[model], freq=FREQ)\n", | ||
| " fcst.fit(df=Y_TRAIN_DF_4, static_df=STATIC_4, val_size=24)\n", | ||
| " forecasts = fcst.predict(futr_df=Y_TEST_DF_4) \n", | ||
| " assert forecasts.shape == (7, 2), f\"Forecast does not have the right shape: {forecasts.shape}\"\n", | ||
| "\n", | ||
| "# Tests a model against every loss function\n", | ||
| "def check_loss_functions(model_class):\n", | ||
| " loss_list = [losses.MAE(), losses.MSE(), losses.RMSE(), losses.MAPE(), losses.SMAPE(), losses.MASE(seasonality=7), \n", | ||
| " losses.QuantileLoss(q=0.5), losses.MQLoss(), losses.IQLoss(), losses.DistributionLoss(\"Normal\"), \n", | ||
| " losses.DistributionLoss(\"StudentT\"), losses.DistributionLoss(\"Poisson\"), losses.DistributionLoss(\"NegativeBinomial\"), \n", | ||
| " losses.DistributionLoss(\"Tweedie\", rho=1.5), losses.DistributionLoss(\"ISQF\"), losses.PMM(), losses.PMM(weighted=True), \n", | ||
| " losses.GMM(), losses.GMM(weighted=True), losses.NBMM(), losses.NBMM(weighted=True), losses.HuberLoss(), \n", | ||
| " losses.TukeyLoss(), losses.HuberQLoss(q=0.5), losses.HuberMQLoss()]\n", | ||
| " for loss in loss_list:\n", | ||
| " test_name = f\"{model_class.__name__}: checking {loss._get_name()}\"\n", | ||
| " print(f\"{test_name}\")\n", | ||
| " config = {'max_steps': 2,\n", | ||
| " 'h': 7,\n", | ||
| " 'input_size': 28,\n", | ||
| " 'loss': loss,\n", | ||
| " 'valid_loss': None,\n", | ||
| " 'enable_progress_bar': False,\n", | ||
| " 'enable_model_summary': False,\n", | ||
| " 'val_check_steps': 2} \n", | ||
| " try:\n", | ||
| " _run_model_tests(model_class, config) \n", | ||
| " except RuntimeError:\n", | ||
| " raise Exception(f\"{test_name} failed.\")\n", | ||
| " except Exception:\n", | ||
| " print(f\"{test_name} skipped on raised Exception.\")\n", | ||
| " pass\n", | ||
| "\n", | ||
| "# Tests a model against the AirPassengers dataset\n", | ||
| "def check_airpassengers(model_class):\n", | ||
| " print(f\"{model_class.__name__}: checking forecast AirPassengers dataset\")\n", | ||
| " Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n", | ||
| " Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n", | ||
| "\n", | ||
| " config = {'max_steps': 2,\n", | ||
| " 'h': 12,\n", | ||
| " 'input_size': 24,\n", | ||
| " 'enable_progress_bar': False,\n", | ||
| " 'enable_model_summary': False,\n", | ||
| " 'val_check_steps': 2,\n", | ||
| " }\n", | ||
| "\n", | ||
| " if model_class.MULTIVARIATE:\n", | ||
| " config[\"n_series\"] = Y_train_df[\"unique_id\"].nunique()\n", | ||
| " # Normal forecast\n", | ||
| " fcst = NeuralForecast(models=[model_class(**config)], freq='M')\n", | ||
| " fcst.fit(df=Y_train_df, static_df=AirPassengersStatic)\n", | ||
| " forecasts = fcst.predict(futr_df=Y_test_df) \n", | ||
| " assert forecasts.shape == (24, 2), f\"Forecast does not have the right shape: {forecasts.shape}\"\n", | ||
| "\n", | ||
| " # Cross-validation\n", | ||
| " fcst = NeuralForecast(models=[model_class(**config)], freq='M')\n", | ||
| " forecasts = fcst.cross_validation(df=AirPassengersPanel, static_df=AirPassengersStatic, n_windows=2, step_size=12)\n", | ||
| " assert forecasts.shape == (48, 4), f\"Forecast does not have the right shape: {forecasts.shape}\"\n", | ||
| "\n", | ||
| "# Add unit test functions to this function\n", | ||
| "def check_model(model_class): \n", | ||
| " check_loss_functions(model_class) \n", | ||
| " try:\n", | ||
| " check_airpassengers(model_class) \n", | ||
| " except RuntimeError:\n", | ||
| " raise Exception(f\"{model_class.__name__}: AirPassengers forecast test failed.\")\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "python3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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