diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index ee945f88a..58d68ba35 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -13,11 +13,11 @@ about: Create a report to help us improve cd neural_prophet pip install . ``` - * Checked the Answered Questions on the Github Disscussion board: https://github.com/ourownstory/neural_prophet/discussions + * Checked the Answered Questions on the Github Discussion board: https://github.com/ourownstory/neural_prophet/discussions If you have the same question but the Answer does not solve your issue, please continue the conversation there. * Checked that your issue isn't already filed: https://github.com/ourownstory/neural_prophet/issues If you have the same issue but there is a twist to your situation, please add an explanation there. - * Considered whether your bug might actually be solveable by getting a question answered: + * Considered whether your bug might actually be solvable by getting a question answered: * Please [post a package use question](https://github.com/ourownstory/neural_prophet/discussions/categories/q-a-get-help-using-neuralprophet) * Please [post a forecasting best practice question](https://github.com/ourownstory/neural_prophet/discussions/categories/q-a-forecasting-best-practices) * Please [post an idea or feedback](https://github.com/ourownstory/neural_prophet/discussions/categories/ideas-feedback) @@ -48,7 +48,7 @@ Describe what happens, and how often it happens. If applicable, add screenshots and console printouts to help explain your problem. -**Environement (please complete the following information):** +**Environment (please complete the following information):** - Python environment [e.g. Python 3.8, in standalone venv with no other packages] - NeuralProphet version and install method [e.g. 2.7, installed from PYPI with `pip install neuralprophet`] diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index f544fdd73..a2f9632ad 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -7,7 +7,7 @@ about: Suggest an idea for this project **Prerequisites** * [ ] Put an X between the brackets on this line if you have done all of the following: - * Checked the Answered Questions on the Github Disscussion board: https://github.com/ourownstory/neural_prophet/discussions + * Checked the Answered Questions on the Github Discussion board: https://github.com/ourownstory/neural_prophet/discussions If you have the same question but the Answer does not solve your issue, please continue the conversation there. * Checked that your issue isn't already filed: https://github.com/ourownstory/neural_prophet/issues If you have the same issue but there is a twist to your situation, please add an explanation there. diff --git a/.github/workflows/metrics.yml b/.github/workflows/metrics.yml index 885b35e38..7a07e9938 100644 --- a/.github/workflows/metrics.yml +++ b/.github/workflows/metrics.yml @@ -51,16 +51,12 @@ jobs: echo "## Model Training" >> report.md echo "### PeytonManning" >> report.md cml asset publish tests/metrics/PeytonManning.svg --md >> report.md - echo "### PeytonManning_test30" >> report.md - cml asset publish tests/metrics/PeytonManning_test30.svg --md >> report.md echo "### YosemiteTemps" >> report.md cml asset publish tests/metrics/YosemiteTemps.svg --md >> report.md - echo "### YosemiteTemps_test20" >> report.md - cml asset publish tests/metrics/YosemiteTemps_test20.svg --md >> report.md echo "### AirPassengers" >> report.md cml asset publish tests/metrics/AirPassengers.svg --md >> report.md - echo "### AirPassengers_test30" >> report.md - cml asset publish tests/metrics/AirPassengers_test30.svg --md >> report.md + echo "### EnergyPriceDaily" >> report.md + cml asset publish tests/metrics/EnergyPriceDaily.svg --md >> report.md echo "\n" >> report.md # Post reports as comments in GitHub PRs cml comment update --target=pr report.md # post to PR diff --git a/README.md b/README.md index 063de5ea8..30c4a05d1 100644 --- a/README.md +++ b/README.md @@ -15,8 +15,8 @@ Please note that the project is still in beta phase. Please report any issues yo # NeuralProphet: human-centered forecasting NeuralProphet is an easy to learn framework for interpretable time series forecasting. -NeuralProphet is built on PyTorch and combines Neural Network and traditional time-series algorithms, inspired by [Facebook Prophet](https://github.com/facebook/prophet) and [AR-Net](https://github.com/ourownstory/AR-Net). -- With few lines of code, you can define, customize, visualize, and evaluate your own forecasting models. +NeuralProphet is built on PyTorch and combines Neural Networks and traditional time-series algorithms, inspired by [Facebook Prophet](https://github.com/facebook/prophet) and [AR-Net](https://github.com/ourownstory/AR-Net). +- With a few lines of code, you can define, customize, visualize, and evaluate your own forecasting models. - It is designed for iterative human-in-the-loop model building. That means that you can build a first model quickly, interpret the results, improve, repeat. Due to the focus on interpretability and customization-ability, NeuralProphet may not be the most accurate model out-of-the-box; so, don't hesitate to adjust and iterate until you like your results. - NeuralProphet is best suited for time series data that is of higher-frequency (sub-daily) and longer duration (at least two full periods/years). @@ -31,7 +31,7 @@ We compiled a [Contributing to NeuralProphet](CONTRIBUTING.md) page with practic ## Community #### Discussion and Help -If you have any question or suggestion, you can participate with [our community right here on Github](https://github.com/ourownstory/neural_prophet/discussions) +If you have any questions or suggestion, you can participate in [our community right here on Github](https://github.com/ourownstory/neural_prophet/discussions) #### Slack Chat We also have an active [Slack community](https://join.slack.com/t/neuralprophet/shared_invite/zt-sgme2rw3-3dCH3YJ_wgg01IXHoYaeCg). Come and join the conversation! @@ -100,9 +100,9 @@ pip install . ### Framework features -* Multiple time series: Fit a global/glocal model with (partially) shared model parameters +* Multiple time series: Fit a global/local model with (partially) shared model parameters * Uncertainty: Estimate values of specific quantiles - Quantile Regression -* Regularize modelling components +* Regularize modeling components * Plotting of forecast components, model coefficients and more * Time series crossvalidation utility * Model checkpointing and validation @@ -111,7 +111,7 @@ pip install . ### Coming soon:tm: * Cross-relation of lagged regressors -* Cross-relation and non-linear modelling of future regressors +* Cross-relation and non-linear modeling of future regressors * Static features / Time series featurization * Logistic growth for trend component. * Model bias modelling / correction with secondary model @@ -135,5 +135,5 @@ Please cite [NeuralProphet](https://arxiv.org/abs/2111.15397) in your publicatio ``` ## About -NeuralProphet is and open-source community project, supported by awesome people like you. +NeuralProphet is an open-source community project, supported by awesome people like you. If you are interested in joining the project, please feel free to reach out to me (Oskar) - you can find my email on the [NeuralProphet Paper](https://arxiv.org/abs/2111.15397). diff --git a/docs/source/how-to-guides/feature-guides/hyperparameter-selection.md b/docs/source/how-to-guides/feature-guides/hyperparameter-selection.md index 0e532d5f5..9754112e3 100644 --- a/docs/source/how-to-guides/feature-guides/hyperparameter-selection.md +++ b/docs/source/how-to-guides/feature-guides/hyperparameter-selection.md @@ -21,7 +21,7 @@ NeuralProphet is fit with stochastic gradient descent - more precisely, with an If the parameter `learning_rate` is not specified, a learning rate range test is conducted to determine the optimal learning rate. The `epochs`, `loss_func` and `optimizer` are other parameters that directly affect the model training process. If not defined, `epochs`and `loss_func` are automatically set based on the dataset size. They are set in a manner that controls the total number training steps to be around 1000 to 4000. -NeuralProphet offers to set two different values for `optimizer`, namely `AdamW` and `SDG` (stochastic gradient decsent). +NeuralProphet offers to set two different values for `optimizer`, namely `AdamW` and `SDG` (stochastic gradient descent). If it looks like the model is overfitting to the training data (the live loss plot can be useful hereby), you can reduce `epochs` and `learning_rate`, and potentially increase the `batch_size`. diff --git a/docs/source/tutorials/tutorial05.ipynb b/docs/source/tutorials/tutorial05.ipynb index a83d5890c..4636be765 100644 --- a/docs/source/tutorials/tutorial05.ipynb +++ b/docs/source/tutorials/tutorial05.ipynb @@ -12,9 +12,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Lagged regressors are used to correlate other observed variables to our target time series. For example the temperature of the previous day might be a good predictor of the temperature of the next day.\n", + "Lagged regressors are used to correlate other observed variables to our target time series. For example the temperature of the previous days might be a good predictor of the temperature of the next day.\n", "\n", - "They are often referred to as covariates. Unlike future regressors, the future of lagged regressors is unknown to us.\n", + "They are often referred to as exogenous variables or as covariates. Unlike future regressors, the future of a lagged regressor is unknown to us.\n", "\n", "At the time $t$ of forecasting, we only have access to their observed, past values up to and including $t − 1$.\n", "\n", @@ -26,12 +26,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we load a new dataset which also contains the temperature of the previous day." + "First we load a new dataset which also contains the temperature." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ "4 2015-01-05 64.89 279.05" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -119,12 +119,22 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Optional:To align the scale of temperature with the energy price, we convert it to Farenheit:\n", + "df[\"temperature\"] = (df[\"temperature\"] - 273.15) * 1.8 + 32" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -142,66 +152,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After viewing the additional data we will add it as lagged regressor to our model. We start with our model from the previous tutorial. And then add the lagged regressor for the temperature to get a better energy price prediction." + "From the data we can see that there is a weak inverse relationship of electricity price to temperature. \n", + "We start with our model from the previous tutorial and then add temperature as a lagged regressor to our model." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "85ae9d185a1f4d219b358c6dde902716", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Finding best initial lr: 0%| | 0/229 [00:002015201620172018201920406080100yhat1Actual1w1m6m1yallyds" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from neuralprophet import NeuralProphet, set_log_level\n", "\n", @@ -213,30 +172,49 @@ " n_changepoints=10,\n", " yearly_seasonality=True,\n", " weekly_seasonality=True,\n", - " daily_seasonality=True,\n", - " # Add the autogression\n", - " n_lags=10,\n", + " daily_seasonality=False,\n", + " n_lags=10, # Autogression\n", ")\n", "m.set_plotting_backend(\"plotly-static\")\n", "\n", - "# Add the new lagged regressor\n", - "m.add_lagged_regressor(\"temperature\")\n", + "# Add temperature of last three days as lagged regressor\n", + "m.add_lagged_regressor(\"temperature\", n_lags=3)\n", "\n", "# Continue training the model and making a prediction\n", "metrics = m.fit(df)\n", - "forecast = m.predict(df)\n", + "forecast = m.predict(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "2015201620172018201920406080100PredictedPredictedActual1w1m6m1yallyds" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# set plotting to focus on forecasting horizon 1 (the only one for us here)\n", + "m.highlight_nth_step_ahead_of_each_forecast(1)\n", "m.plot(forecast)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "Jan 2015Jul 2015Jan 2016Jul 2016Jan 2017Jul 2017Jan 2018Jul 2018Jan 2019−202dsLagged Regressor \"temperature\"" + "Jan 2015Jul 2015Jan 2016Jul 2016Jan 2017Jul 2017Jan 2018Jul 2018Jan 2019−2024dsLagged Regressor \"temperature\" (1)-ahead" ] }, "metadata": {}, @@ -244,18 +222,27 @@ } ], "source": [ + "# show the component's forecast contribution\n", "m.plot_components(forecast, components=[\"lagged_regressors\"])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the temperatur impact the forecasted price by few units. \n", + "Compared to the overall price fluctuations, the temperature impact seems minor, but not insignificant." + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "02468100%10%20%Lagged Regressor \"temperature\" lag numberLagged Regressor \"temperature\" relevance" + "00.511.522.533.54−0.200.2Lagged Regressor \"temperature\" lag numberLagged Regressor \"temperature\" weight (1)-ahead" ] }, "metadata": {}, @@ -263,6 +250,7 @@ } ], "source": [ + "# visualize model parameters of lagged regression\n", "m.plot_parameters(components=[\"lagged_regressors\"])" ] }, @@ -271,12 +259,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "The model learns a different weights for each of the lags, which may also capture changes in the direction of temperature.\n", + "\n", "Let us explore how our model improved after adding the lagged regressor." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -309,122 +299,29 @@ " \n", " \n", " \n", - " 0\n", - " 57.422047\n", - " 68.648613\n", - " 0.454331\n", - " 0.0\n", - " 0\n", - " \n", - " \n", - " 1\n", - " 53.999779\n", - " 64.757294\n", - " 0.413841\n", - " 0.0\n", - " 1\n", - " \n", - " \n", - " 2\n", - " 50.681854\n", - " 61.224556\n", - " 0.374771\n", - " 0.0\n", - " 2\n", - " \n", - " \n", - " 3\n", - " 45.628796\n", - " 55.634834\n", - " 0.319898\n", - " 0.0\n", - " 3\n", - " \n", - " \n", - " 4\n", - " 40.890278\n", - " 49.883217\n", - " 0.266118\n", - " 0.0\n", - " 4\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 168\n", - " 4.899452\n", - " 6.577740\n", - " 0.005222\n", - " 0.0\n", - " 168\n", - " \n", - " \n", - " 169\n", - " 4.902421\n", - " 6.521432\n", - " 0.005134\n", - " 0.0\n", - " 169\n", - " \n", - " \n", - " 170\n", - " 4.891082\n", - " 6.474842\n", - " 0.005115\n", - " 0.0\n", - " 170\n", - " \n", - " \n", - " 171\n", - " 4.916732\n", - " 6.520473\n", - " 0.005170\n", - " 0.0\n", - " 171\n", - " \n", - " \n", " 172\n", - " 4.910669\n", - " 6.507926\n", - " 0.005161\n", + " 4.936666\n", + " 6.578746\n", + " 0.00533\n", " 0.0\n", " 172\n", " \n", " \n", "\n", - "

173 rows × 5 columns

\n", "" ], "text/plain": [ - " MAE RMSE Loss RegLoss epoch\n", - "0 57.422047 68.648613 0.454331 0.0 0\n", - "1 53.999779 64.757294 0.413841 0.0 1\n", - "2 50.681854 61.224556 0.374771 0.0 2\n", - "3 45.628796 55.634834 0.319898 0.0 3\n", - "4 40.890278 49.883217 0.266118 0.0 4\n", - ".. ... ... ... ... ...\n", - "168 4.899452 6.577740 0.005222 0.0 168\n", - "169 4.902421 6.521432 0.005134 0.0 169\n", - "170 4.891082 6.474842 0.005115 0.0 170\n", - "171 4.916732 6.520473 0.005170 0.0 171\n", - "172 4.910669 6.507926 0.005161 0.0 172\n", - "\n", - "[173 rows x 5 columns]" + " MAE RMSE Loss RegLoss epoch\n", + "172 4.936666 6.578746 0.00533 0.0 172" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "metrics" + "metrics.tail(1)" ] }, { @@ -465,7 +362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.12" }, "orig_nbformat": 4, "vscode": { diff --git a/tests/metrics/debug-energy-price-daily.ipynb b/tests/metrics/debug-energy-price-daily.ipynb new file mode 100644 index 000000000..e94b8d756 --- /dev/null +++ b/tests/metrics/debug-energy-price-daily.ipynb @@ -0,0 +1,5759 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "from plotly_resampler import unregister_plotly_resampler\n", + "\n", + "from neuralprophet import NeuralProphet, set_random_seed" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def create_metrics_plot(metrics):\n", + " # Deactivate the resampler since it is not compatible with kaleido (image export)\n", + " unregister_plotly_resampler()\n", + "\n", + " # Plotly params\n", + " prediction_color = \"#2d92ff\"\n", + " actual_color = \"black\"\n", + " line_width = 2\n", + " xaxis_args = {\"showline\": True, \"mirror\": True, \"linewidth\": 1.5, \"showgrid\": False}\n", + " yaxis_args = {\n", + " \"showline\": True,\n", + " \"mirror\": True,\n", + " \"linewidth\": 1.5,\n", + " \"showgrid\": False,\n", + " \"rangemode\": \"tozero\",\n", + " \"type\": \"log\",\n", + " }\n", + " layout_args = {\n", + " \"autosize\": True,\n", + " \"template\": \"plotly_white\",\n", + " \"margin\": go.layout.Margin(l=0, r=10, b=0, t=30, pad=0),\n", + " \"font\": dict(size=10),\n", + " \"title\": dict(font=dict(size=10)),\n", + " \"width\": 1000,\n", + " \"height\": 200,\n", + " }\n", + "\n", + " metric_cols = [col for col in metrics.columns if not (\"_val\" in col or col == \"RegLoss\" or col == \"epoch\")]\n", + " fig = make_subplots(rows=1, cols=len(metric_cols), subplot_titles=metric_cols)\n", + " for i, metric in enumerate(metric_cols):\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " y=metrics[metric],\n", + " name=metric,\n", + " mode=\"lines\",\n", + " line=dict(color=prediction_color, width=line_width),\n", + " legendgroup=metric,\n", + " ),\n", + " row=1,\n", + " col=i + 1,\n", + " )\n", + " if f\"{metric}_val\" in metrics.columns:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " y=metrics[f\"{metric}_val\"],\n", + " name=f\"{metric}_val\",\n", + " mode=\"lines\",\n", + " line=dict(color=actual_color, width=line_width),\n", + " legendgroup=metric,\n", + " ),\n", + " row=1,\n", + " col=i + 1,\n", + " )\n", + " if metric == \"Loss\":\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " y=metrics[\"RegLoss\"],\n", + " name=\"RegLoss\",\n", + " mode=\"lines\",\n", + " line=dict(color=actual_color, width=line_width),\n", + " legendgroup=metric,\n", + " ),\n", + " row=1,\n", + " col=i + 1,\n", + " )\n", + " fig.update_xaxes(xaxis_args)\n", + " fig.update_yaxes(yaxis_args)\n", + " fig.update_layout(layout_args)\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "DIR = \"~/github/neural_prophet\"\n", + "DATA_DIR = os.path.join(DIR, \"tests\", \"test-data\")\n", + "PEYTON_FILE = os.path.join(DATA_DIR, \"wp_log_peyton_manning.csv\")\n", + "AIR_FILE = os.path.join(DATA_DIR, \"air_passengers.csv\")\n", + "YOS_FILE = os.path.join(DATA_DIR, \"yosemite_temps.csv\")\n", + "ENERGY_PRICE_DAILY_FILE = os.path.join(DATA_DIR, \"tutorial04_kaggle_energy_daily_temperature.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(ENERGY_PRICE_DAILY_FILE)\n", + "df[\"temp\"] = df[\"temperature\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = NeuralProphet(\n", + " n_forecasts=7,\n", + " n_changepoints=0,\n", + " yearly_seasonality=True,\n", + " weekly_seasonality=True,\n", + " daily_seasonality=False,\n", + " n_lags=14,\n", + ")\n", + "m.add_lagged_regressor(\"temp\", n_lags=3)\n", + "m.add_future_regressor(\"temperature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.932% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n", + "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n", + "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n", + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.924% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n", + "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n", + "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n", + "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 179\n", + "WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (41) is too small than the required number for the learning rate finder (228). The results might not be optimal.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e4fec661564844fa9e06d8185793742c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Finding best initial lr: 0%| | 0/228 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MAE_valRMSE_valLoss_valRegLoss_valepochMAERMSELossRegLoss
1785.4276926.7127460.0075820.01785.9145327.906580.0078940.0
\n", + "" + ], + "text/plain": [ + " MAE_val RMSE_val Loss_val RegLoss_val epoch MAE RMSE \\\n", + "178 5.427692 6.712746 0.007582 0.0 178 5.914532 7.90658 \n", + "\n", + " Loss RegLoss \n", + "178 0.007894 0.0 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics.tail(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.932% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n", + "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.932% of the data.\n", + "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1629e66c030f4d32b8b1e1a9180c3c62", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: 41it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n" + ] + } + ], + "source": [ + "forecast = m.predict(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fb93a042f07e4a71b627b5dec9217daf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FigureWidgetResampler({\n", + " 'data': [{'line': {'color': '#2d92ff', 'width': 2},\n", + " 'mode': 'lines',\n", + " 'name': '[R] Predicted ~1D',\n", + " 'type': 'scatter',\n", + " 'uid': '396ea1b0-5f35-4332-8e56-e4779a7b8143',\n", + " 'x': array([datetime.datetime(2015, 1, 21, 0, 0),\n", + " datetime.datetime(2015, 1, 22, 0, 0),\n", + " datetime.datetime(2015, 1, 23, 0, 0), ...,\n", + " datetime.datetime(2018, 12, 28, 0, 0),\n", + " datetime.datetime(2018, 12, 29, 0, 0),\n", + " datetime.datetime(2018, 12, 31, 0, 0)], dtype=object),\n", + " 'y': array([54.09432 , 53.393017, 50.852264, ..., 56.8785 , 58.787167, 58.58358 ],\n", + " dtype=float32)},\n", + " {'marker': {'color': 'blue', 'size': 4, 'symbol': 'x'},\n", + " 'mode': 'markers',\n", + " 'name': '[R] Predicted ~1D',\n", + " 'type': 'scatter',\n", + " 'uid': '459a4e9f-cc5b-4141-a5ca-3e649b0337e8',\n", + " 'x': array([datetime.datetime(2015, 1, 21, 0, 0),\n", + " datetime.datetime(2015, 1, 22, 0, 0),\n", + " datetime.datetime(2015, 1, 23, 0, 0), ...,\n", + " datetime.datetime(2018, 12, 28, 0, 0),\n", + " datetime.datetime(2018, 12, 29, 0, 0),\n", + " datetime.datetime(2018, 12, 31, 0, 0)], dtype=object),\n", + " 'y': array([54.09432 , 53.393017, 50.852264, ..., 56.8785 , 58.787167, 58.58358 ],\n", + " dtype=float32)},\n", + " {'marker': {'color': 'black', 'size': 4},\n", + " 'mode': 'markers',\n", + " 'name': '[R] Actual ~1D',\n", + " 'type': 'scatter',\n", + " 'uid': '3a7dd528-0c4a-46b2-837a-f715eac24027',\n", + " 'x': array([datetime.datetime(2015, 1, 1, 0, 0),\n", + " datetime.datetime(2015, 1, 2, 0, 0),\n", + " datetime.datetime(2015, 1, 3, 0, 0), ...,\n", + " datetime.datetime(2018, 12, 28, 0, 0),\n", + " datetime.datetime(2018, 12, 29, 0, 0),\n", + " datetime.datetime(2018, 12, 31, 0, 0)], dtype=object),\n", + " 'y': array([64.92, 58.46, 63.35, ..., 68.61, 60.22, 60.32])}],\n", + " 'layout': {'autosize': True,\n", + " 'font': {'size': 10},\n", + " 'height': 420,\n", + " 'hovermode': 'x unified',\n", + " 'margin': {'b': 0, 'l': 0, 'pad': 0, 'r': 10, 't': 10},\n", + " 'showlegend': True,\n", + " 'template': '...',\n", + " 'title': {'font': {'size': 12}},\n", + " 'width': 700,\n", + " 'xaxis': {'linewidth': 1.5,\n", + " 'mirror': True,\n", + " 'rangeselector': {'buttons': [{'count': 7, 'label': '1w', 'step': 'day', 'stepmode': 'backward'},\n", + " {'count': 1,\n", + " 'label': '1m',\n", + " 'step': 'month',\n", + " 'stepmode': 'backward'},\n", + " {'count': 6,\n", + " 'label': '6m',\n", + " 'step': 'month',\n", + " 'stepmode': 'backward'},\n", + " {'count': 1, 'label': '1y', 'step': 'year', 'stepmode': 'backward'},\n", + " {'step': 'all'}]},\n", + " 'rangeslider': {'visible': True},\n", + " 'showline': True,\n", + " 'title': {'text': 'ds'},\n", + " 'type': 'date'},\n", + " 'yaxis': {'linewidth': 1.5, 'mirror': True, 'showline': True, 'title': {'text': 'y'}}}\n", + "})" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.highlight_nth_step_ahead_of_each_forecast(7)\n", + "m.plot(forecast)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f91f136109cc42c88223af3dca5bf62e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FigureWidgetResampler({\n", + " 'data': [{'line': {'color': '#2d92ff', 'width': 2},\n", + " 'mode': 'lines',\n", + " 'name': '[R] Trend ~1D',\n", + " 'showlegend': False,\n", + " 'type': 'scatter',\n", + " 'uid': 'b9e952d8-a39c-48e7-82d5-1e76eeec2f04',\n", + " 'x': array([datetime.datetime(2015, 1, 15, 0, 0),\n", + " datetime.datetime(2015, 1, 16, 0, 0),\n", + " datetime.datetime(2015, 1, 17, 0, 0), ...,\n", + " datetime.datetime(2018, 12, 28, 0, 0),\n", + " datetime.datetime(2018, 12, 29, 0, 0),\n", + " datetime.datetime(2018, 12, 31, 0, 0)], dtype=object),\n", + " 'xaxis': 'x',\n", + " 'y': array([44.47614 , 44.48715 , 44.498158, ..., 60.361263, 60.372272, 60.39429 ],\n", + " dtype=float32),\n", + " 'yaxis': 'y'},\n", + " {'line': {'color': '#2d92ff', 'width': 2},\n", + " 'mode': 'lines',\n", + " 'name': ('[R' ... 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"ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/test_model_performance.py b/tests/test_model_performance.py index f3ba7d1bc..6a519fe03 100644 --- a/tests/test_model_performance.py +++ b/tests/test_model_performance.py @@ -23,6 +23,7 @@ PEYTON_FILE = os.path.join(DATA_DIR, "wp_log_peyton_manning.csv") AIR_FILE = os.path.join(DATA_DIR, "air_passengers.csv") YOS_FILE = os.path.join(DATA_DIR, "yosemite_temps.csv") +ENERGY_PRICE_DAILY_FILE = os.path.join(DATA_DIR, "tutorial04_kaggle_energy_daily_temperature.csv") # Important to set seed for reproducibility set_random_seed(42) @@ -142,36 +143,16 @@ def test_PeytonManning(): create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "PeytonManning.svg")) -def test_PeytonManning_test30(): - df = pd.read_csv(PEYTON_FILE) - m = NeuralProphet() - df_train, df_test = m.split_df(df=df, freq="D", valid_p=0.3) - - system_speed, std = get_system_speed() - start = time.time() - metrics = m.fit(df_train, validation_df=df_test, freq="D") # , early_stopping=True) - end = time.time() - - accuracy_metrics = metrics.to_dict("records")[-1] - accuracy_metrics["time"] = round(end - start, 2) - accuracy_metrics["system_performance"] = round(system_speed, 5) - accuracy_metrics["system_std"] = round(std, 5) - with open(os.path.join(DIR, "tests", "metrics", "PeytonManning_test30.json"), "w") as outfile: - json.dump(accuracy_metrics, outfile) - - create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "PeytonManning_test30.svg")) - - def test_YosemiteTemps(): df = pd.read_csv(YOS_FILE) m = NeuralProphet( n_lags=36, n_forecasts=12, - changepoints_range=0.95, + changepoints_range=0.9, n_changepoints=30, weekly_seasonality=False, ) - df_train, df_test = m.split_df(df=df, freq="5min", valid_p=0.05) + df_train, df_test = m.split_df(df=df, freq="5min", valid_p=0.1) system_speed, std = get_system_speed() start = time.time() @@ -188,32 +169,6 @@ def test_YosemiteTemps(): create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "YosemiteTemps.svg")) -def test_YosemiteTemps_test20(): - df = pd.read_csv(YOS_FILE) - m = NeuralProphet( - n_lags=36, - n_forecasts=12, - changepoints_range=0.8, - n_changepoints=20, - weekly_seasonality=False, - ) - df_train, df_test = m.split_df(df=df, freq="5min", valid_p=0.2) - - system_speed, std = get_system_speed() - start = time.time() - metrics = m.fit(df_train, validation_df=df_test, freq="5min") # , early_stopping=True) - end = time.time() - - accuracy_metrics = metrics.to_dict("records")[-1] - accuracy_metrics["time"] = round(end - start, 2) - accuracy_metrics["system_performance"] = round(system_speed, 5) - accuracy_metrics["system_std"] = round(std, 5) - with open(os.path.join(DIR, "tests", "metrics", "YosemiteTemps_test20.json"), "w") as outfile: - json.dump(accuracy_metrics, outfile) - - create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "YosemiteTemps_test20.svg")) - - def test_AirPassengers(): df = pd.read_csv(AIR_FILE) m = NeuralProphet(seasonality_mode="multiplicative") @@ -234,21 +189,33 @@ def test_AirPassengers(): create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "AirPassengers.svg")) -def test_AirPassengers_test30(): - df = pd.read_csv(AIR_FILE) - m = NeuralProphet(seasonality_mode="multiplicative") - df_train, df_test = m.split_df(df=df, freq="MS", valid_p=0.3) +def test_EnergyPriceDaily(): + df = pd.read_csv(ENERGY_PRICE_DAILY_FILE) + df["temp"] = df["temperature"] + + m = NeuralProphet( + n_forecasts=7, + n_changepoints=0, + yearly_seasonality=True, + weekly_seasonality=True, + daily_seasonality=False, + n_lags=14, + ) + m.add_lagged_regressor("temp", n_lags=3) + m.add_future_regressor("temperature") + + df_train, df_test = m.split_df(df=df, freq="D", valid_p=0.1) system_speed, std = get_system_speed() start = time.time() - metrics = m.fit(df_train, validation_df=df_test, freq="MS") # , early_stopping=True) + metrics = m.fit(df_train, validation_df=df_test, freq="D") # , early_stopping=True) end = time.time() accuracy_metrics = metrics.to_dict("records")[-1] accuracy_metrics["time"] = round(end - start, 2) accuracy_metrics["system_performance"] = round(system_speed, 5) accuracy_metrics["system_std"] = round(std, 5) - with open(os.path.join(DIR, "tests", "metrics", "AirPassengers_test30.json"), "w") as outfile: + with open(os.path.join(DIR, "tests", "metrics", "EnergyPriceDaily.json"), "w") as outfile: json.dump(accuracy_metrics, outfile) - create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "AirPassengers_test30.svg")) + create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "EnergyPriceDaily.svg"))