diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 00000000..062458d7 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,27 @@ +name: publish + +on: + push: + branches: + - main + +jobs: + publish: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Install Poetry + run: | + python -m pip install --upgrade pip + pip install poetry + poetry config virtualenvs.in-project false + poetry config virtualenvs.path ~/.virtualenvs + + - name: Build and publish to pypi + env: + PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + run: | + poetry config pypi-token.pypi $PYPI_TOKEN + poetry publish --build diff --git a/.gitignore b/.gitignore index ab6a9a69..6cfd67c2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,14 +1,16 @@ -./data -.ipynb_checkpoints/ +# python/IDE articfacts .idea/ +.ipynb_checkpoints/ +.DS_Store __pycache__/ -Deep_Learning_Prediction_Intervals/ -algorithm_implement.py -exp_dir/ -data/ -!./tests/data/Synthetic Site -nsrd_auth.json +coverage/ +dist/ + +# notebook articfacts +notebooks/examples/exp_dir/ +notebooks/examples/Synthetic Site/ +notebooks/examples/Training_history.csv +tests/data/Synthetic Site/Synthetic Site Electricity Main Total Power_external_test.h5 +tests/fixtures/alfa_exp_dir/events.out.tfevents.* +tests/fixtures/alfa_exp_dir/output.out Training_history.csv -.DS_Store -.coverage -dist diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 573301f1..00000000 --- a/Dockerfile +++ /dev/null @@ -1,18 +0,0 @@ -FROM ghcr.io/haxall/hxpy:latest - -RUN mkdir /wattile - -# Copy wattile and models -COPY ./wattile /wattile/wattile -COPY ./tests/fixtures /trained_models - -ENV PYTHONPATH=${PYTHONPATH}:${PWD} - -# Install Dependices -COPY ./poetry.lock /wattile -COPY ./pyproject.toml /wattile -RUN pip3 install poetry==1.1.13 -RUN poetry config virtualenvs.create false -RUN cd /wattile && poetry install --no-dev - -ENTRYPOINT ["python"] diff --git a/README.md b/README.md index 60903cd0..717e73db 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,16 @@ Wattile -==== += -Deep learning algorithms for probabilistic forecasting of time series building performance data. +Probabilistic Deep Learning-based Forecasting of Building Energy ConsumptionProbabilistic Deep Learning-based Forecasting of Building Energy Consumption. -Set Up ----- +# Set Up Requirements: - [Python](https://www.python.org/downloads/) >= 3.7.0 - [Poetry](https://python-poetry.org/docs/#installation) - [Microsoft Visual C++ 14.0](https://visualstudio.microsoft.com/visual-cpp-build-tools/), if you're on Windows -If using conda for python management, use `environment.yml` to make an environment and configure poetry to not create a vitural enviroment. +If using conda for python management, use `environment.yml` to make an environment and configure poetry to not create a vitural enviroment (else, skip these commands). ``` conda env create -f environment.yml poetry config virtualenvs.create false @@ -29,28 +28,13 @@ poetry run pre-commit install Quick Start ---- +Please see [our example notebooks](./notebooks/examples) to see how to run Wattile out of the box. -Wattile has two main functions: -- `create_input_dataframe`, which creates a dataframe for model input from the configs file and raw data. -- `run_model` which runs either training, validation, or prediction on input dataframes according to the configs. - -Docs for the configs [here](./docs/Configs.md). - -Docs for the format of the raw data is [here](./docs/Data_configs.md). - -Docs for the format of the output is [here](./docs/Output.md). - +In short, one must prep their data, create the model, and send the data through. ```py -import json - -from wattile.entry_point import create_input_dataframe, run_model - -with open("wattile/configs.json", "r") as f: - configs = json.load(f) - -init_logging(configs["exp_dir"]) -train_df, val_df = create_input_dataframe(configs) -run_model(configs, train_df, val_df) +train_df, val_df = prep_for_rnn(configs, data) +model = ModelFactory.create_model(configs) +model.train(train_df, val_df) ``` After running, you may use tensordboard on the results. @@ -58,6 +42,13 @@ After running, you may use tensordboard on the results. ``` tensorboard --logdir= ``` +Wattile is highly configurable. + +Docs for the configs [here](./docs/Configs.md). + +Docs for the format of the raw data is [here](./docs/Data_configs.md). + +Docs for the format of the output is [here](./docs/Output.md). Available Models ---- @@ -78,6 +69,16 @@ Available Models * Vanilla and LSTM variants available * Supports future time predictions with constant spacing or variable spacing + +### [Alfa Ensemble](./wattile/models/alfa_ensemble_model.py) + +* Probabilistic forecasting +* In one training session, predict: + * Number of future times: *T* + * Number of quantiles per time: *Q* +* Vanilla and LSTM variants available +* Supports future time predictions with constant spacing or variable spacing + Development ---- diff --git a/docs/Vis_TimeseriesComparisons.svg b/docs/Vis_TimeseriesComparisons.svg index 10adea26..a08f6059 100644 --- a/docs/Vis_TimeseriesComparisons.svg +++ b/docs/Vis_TimeseriesComparisons.svg @@ -1 +1 @@ -801001201400510150510150500100015002000Quantile0Quantile1Quantile2Quantile3MedianTargetAppended Multiple Time Windows(single window size = 1W)PowerConsumptionPenaltyLowPenaltyHighIntervalScore \ No newline at end of file +801001201400510150510150500100015002000Quantile 0.025-0.975Quantile 0.05-0.95Quantile 0.1-0.9Quantile 0.25-0.75Median PredictionTargetTime (timestamp in sequence)PowerConsumptionPenaltyLowPenaltyHighIntervalScore \ No newline at end of file diff --git a/notebooks/examples/example_batch_training.ipynb b/notebooks/examples/example_batch_training.ipynb index 6cd5d030..b23737b7 100644 --- a/notebooks/examples/example_batch_training.ipynb +++ b/notebooks/examples/example_batch_training.ipynb @@ -11,7 +11,7 @@ "import os\n", "from pathlib import Path\n", "\n", - "from wattile.buildings_processing import prep_for_rnn\n", + "from wattile.data_processing import prep_for_rnn\n", "from wattile.data_reading import read_dataset_from_file\n", "from wattile.entry_point import init_logging\n", "from wattile.models import ModelFactory\n", @@ -42,19 +42,6 @@ "]" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# read configs file for training\n", - "with open(\n", - " PROJECT_DIRECTORY / \"wattile\" / \"configs\" / \"configs.json\", \"r\"\n", - ") as f:\n", - " configs = json.load(f)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -73,6 +60,19 @@ " data_dir = entry[\"dir_data\"]\n", "\n", " for target in entry[\"targets\"]:\n", + " \n", + " \"\"\"\n", + " read configs file\n", + " \"\"\"\n", + " with open(\n", + " PROJECT_DIRECTORY / \"wattile\" / \"configs\" / \"configs.json\", \"r\"\n", + " ) as f:\n", + " configs = json.load(f)\n", + " \n", + " \"\"\"\n", + " read configs file\n", + " \"\"\"\n", + " # update configs here only if necessary\n", "\n", " \"\"\"\n", " model training\n", @@ -85,7 +85,7 @@ " configs[\"data_input\"][\"data_dir\"] = str(PROJECT_DIRECTORY / data_dir)\n", " configs[\"data_input\"][\"data_config\"] = \"{} Config.json\".format(building)\n", " configs[\"data_input\"][\"target_var\"] = target\n", - " exp_dir = PROJECT_DIRECTORY / building / target\n", + " exp_dir = PROJECT_DIRECTORY / \"notebooks\" / \"examples\" / building / target\n", " configs[\"data_output\"][\"exp_dir\"] = str(exp_dir)\n", "\n", " # create results folder\n", diff --git a/notebooks/examples/predict.ipynb b/notebooks/examples/predict.ipynb new file mode 100644 index 00000000..2cdf5d5b --- /dev/null +++ b/notebooks/examples/predict.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "59a2ce17-84cf-4614-859f-ef36af81aec1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PROJECT_DIRECTORY = /Users/heslinge/Repos/Wattile/wattile\n" + ] + } + ], + "source": [ + "import json \n", + "from pathlib import Path\n", + "import shutil\n", + "import datetime as dt\n", + "from pprint import pprint\n", + "\n", + "import pandas as pd\n", + "\n", + "from wattile.entry_point import init_logging\n", + "from wattile.data_reading import read_dataset_from_file\n", + "from wattile.data_processing import prep_for_rnn\n", + "from wattile.models import ModelFactory\n", + "\n", + "\n", + "PROJECT_DIRECTORY = Path().resolve().parents[1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "932aedb2-8fa3-4436-bf83-401ecbc264ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'data_input': {'data_dir': 'tests/data/Synthetic Site', 'data_config': 'Synthetic Site Config.json', 'start_time': '2018-01-01T00:00:00-07:00', 'end_time': '2022-01-01T00:00:00-07:00', 'predictor_columns': ['Synthetic Weather Station Dew Point Temperature', 'Synthetic Weather Station Diffuse Horizontal Irradiance', 'Synthetic Weather Station Direct Normal Irradiance', 'Synthetic Weather Station Dry Bulb Temperature', 'Synthetic Weather Station Global Horizontal Irradiance', 'Synthetic Weather Station Relative Humidity', 'Synthetic Weather Station Wind Speed'], 'target_var': 'Synthetic Site Electricity Main Total Power'}, 'data_output': {'exp_dir': 'exp_dir', 'plot_comparison': True, 'plot_comparison_portion_start': 0.0, 'plot_comparison_portion_end': 1.0}, 'data_processing': {'feat_time': {'month_of_year': ['sincos'], 'day_of_week': ['binary_reg'], 'hour_of_day': ['sincos'], 'holidays': False}, 'resample': {'bin_interval': '15min', 'bin_closed': 'right', 'bin_label': 'right'}, 'feat_stats': {'active': True, 'window_width': '15min'}, 'feat_timelag': {'lag_interval': '15min', 'lag_count': 12}, 'input_output_window': {'window_width_source': '180min', 'window_width_futurecast': '0min', 'window_width_target': '45min'}, 'random_seed': 1, 'sequential_splicer': {'active': False, 'window_width': '3h'}, 'data_split': '80:10:10', 'train_size_factor': 4}, 'learning_algorithm': {'arch_type': 'RNN', 'arch_version': 'alfa', 'arch_type_variant': 'lstm', 'use_case': 'train', 'num_epochs': 1, 'hidden_size': 5, 'num_layers': 1, 'quantiles': [0.025, 0.25, 0.5, 0.75, 0.975], 'transformation_method': 'minmaxscale', 'train_batch_size': 5, 'val_batch_size': 1, 'run_resume': False, 'optimizer_config': {'weight_decay': 0.001, 'base': 0.001, 'schedule': False, 'type': 'performance', 'factor': 0.1, 'min': 1e-05, 'patience': 30, 'step_size': 75}, 'smoothing_alpha': 0.001, 'eval_frequency': 2, 'test_method': 'external'}, 'target_feat_name': ['Synthetic Site Electricity Main Total Power'], 'input_dim': 54}\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "This demo runs predictions on a pre-trained alpha model in ./tests/fixtures/alfa_exp_dir.\n", + "\"\"\"\n", + "exp_dir = PROJECT_DIRECTORY / \"tests/fixtures/alfa_exp_dir\"\n", + "with open(exp_dir / \"configs.json\", \"r\") as f:\n", + " configs = json.load(f)\n", + "\n", + "print(configs)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8c432fdb-3888-45ea-9802-04f845a000e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logging to: exp_dir/output.out, PID: 13330\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Init the model with it's configs. Be sure to switch it's use case to prediction.\n", + "\"\"\"\n", + "\n", + "configs[\"data_output\"][\"exp_dir\"] = exp_dir\n", + "configs[\"learning_algorithm\"][\"use_case\"] = \"prediction\"\n", + "init_logging(local_results_dir=configs[\"data_output\"][\"exp_dir\"])\n", + "model = ModelFactory.create_model(configs)\n", + "\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "76f9f6f5-52f7-4b7e-8a82-9af2c0580d72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " To use this model to predict Synthetic Site Electricity Main Total Power for 2020-01-01 00:00:00+00:00, we will need to feed it data:\n", + " - starting at 2019-12-31 20:45:00+00:00\n", + " - ending at 2020-01-01 00:00:00+00:00\n", + " - a rough frequency of 15min\n", + " - predictors named ['Synthetic Weather Station Dew Point Temperature', 'Synthetic Weather Station Diffuse Horizontal Irradiance', 'Synthetic Weather Station Direct Normal Irradiance', 'Synthetic Weather Station Dry Bulb Temperature', 'Synthetic Weather Station Global Horizontal Irradiance', 'Synthetic Weather Station Relative Humidity', 'Synthetic Weather Station Wind Speed']\n", + "\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "If we want to get predictions for only one row, we have to find the time range needed to predict the nominal time.\n", + "\n", + "we can use `get_input_window_for_output_time` to figure this out.\n", + "\"\"\"\n", + "nominal_time = pd.Timestamp(year=2020, month=1, day=1, tz=dt.timezone.utc)\n", + "\n", + "start_time, end_time = model.get_input_window_for_output_time(nominal_time)\n", + "freq = model.configs[\"data_processing\"][\"resample\"][\"bin_interval\"]\n", + "inclusive = model.configs[\"data_processing\"][\"resample\"][\"bin_label\"]\n", + "\n", + "predictors = model.configs[\"data_input\"][\"predictor_columns\"]\n", + "target_var = model.configs[\"data_input\"][\"target_var\"]\n", + "\n", + "print(f\"\"\"\n", + " To use this model to predict {target_var} for {nominal_time}, we will need to feed it data:\n", + " - starting at {start_time}\n", + " - ending at {end_time}\n", + " - a rough frequency of {freq}\n", + " - predictors named {predictors}\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ae329db2-26e2-4c29-9efa-b43e98075591", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Synthetic Weather Station Dew Point TemperatureSynthetic Weather Station Diffuse Horizontal IrradianceSynthetic Weather Station Direct Normal IrradianceSynthetic Weather Station Dry Bulb TemperatureSynthetic Weather Station Global Horizontal IrradianceSynthetic Weather Station Relative HumiditySynthetic Weather Station Wind SpeedSynthetic Site Electricity Main Total Power
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" + ], + "text/plain": [ + " Synthetic Weather Station Dew Point Temperature \\\n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + " Synthetic Weather Station Diffuse Horizontal Irradiance \\\n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + " Synthetic Weather Station Direct Normal Irradiance \\\n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + " Synthetic Weather Station Dry Bulb Temperature \\\n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + " Synthetic Weather Station Global Horizontal Irradiance \\\n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + " Synthetic Weather Station Relative Humidity \\\n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + " Synthetic Weather Station Wind Speed \\\n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + " Synthetic Site Electricity Main Total Power \n", + "2019-12-31 21:00:00+00:00 2100 \n", + "2019-12-31 21:15:00+00:00 2115 \n", + "2019-12-31 21:30:00+00:00 2130 \n", + "2019-12-31 21:45:00+00:00 2145 \n", + "2019-12-31 22:00:00+00:00 2200 \n", + "2019-12-31 22:15:00+00:00 2215 \n", + "2019-12-31 22:30:00+00:00 2230 \n", + "2019-12-31 22:45:00+00:00 2245 \n", + "2019-12-31 23:00:00+00:00 2300 \n", + "2019-12-31 23:15:00+00:00 2315 \n", + "2019-12-31 23:30:00+00:00 2330 \n", + "2019-12-31 23:45:00+00:00 2345 \n", + "2020-01-01 00:00:00+00:00 0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Great, now lets make some dummy data that matches those specs.\n", + "\"\"\"\n", + "\n", + "data = pd.DataFrame(index=pd.date_range(start_time, end_time, freq=freq, inclusive=inclusive))\n", + "for predictor in predictors:\n", + " data[predictor] = data.index.hour * 100 + data.index.minute\n", + "\n", + "# even though we are predicting, we must fill the target column with dummy data. It's not used though.\n", + "data[target_var] = data.index.hour * 100 + data.index.minute\n", + "\n", + "\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9decc834-e3ae-4abb-b0e2-7e7faf8c21b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Synthetic Weather Station Dew Point Temperature_min_lag12Synthetic Weather Station Diffuse Horizontal Irradiance_min_lag12Synthetic Weather Station Direct Normal Irradiance_min_lag12Synthetic Weather Station Dry Bulb Temperature_min_lag12Synthetic Weather Station Global Horizontal Irradiance_min_lag12Synthetic Weather Station Relative Humidity_min_lag12Synthetic Weather Station Wind Speed_min_lag12sin_HOD_min_lag12cos_HOD_min_lag12DOW_binary_reg_0_min_lag12...DOW_binary_reg_0_meanDOW_binary_reg_1_meanDOW_binary_reg_2_meanDOW_binary_reg_3_meanDOW_binary_reg_4_meanDOW_binary_reg_5_meanDOW_binary_reg_6_meansin_MOY_meancos_MOY_meanSynthetic Site Electricity Main Total Power
2020-01-01 00:00:00+00:002100.02100.02100.02100.02100.02100.02100.0-0.7071070.7071070.0...0.00.01.00.00.00.00.00.0172130.9998520
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1 rows × 703 columns

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" + ], + "text/plain": [ + " Synthetic Weather Station Dew Point Temperature_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 2100.0 \n", + "\n", + " Synthetic Weather Station Diffuse Horizontal Irradiance_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 2100.0 \n", + "\n", + " Synthetic Weather Station Direct Normal Irradiance_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 2100.0 \n", + "\n", + " Synthetic Weather Station Dry Bulb Temperature_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 2100.0 \n", + "\n", + " Synthetic Weather Station Global Horizontal Irradiance_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 2100.0 \n", + "\n", + " Synthetic Weather Station Relative Humidity_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 2100.0 \n", + "\n", + " Synthetic Weather Station Wind Speed_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 2100.0 \n", + "\n", + " sin_HOD_min_lag12 cos_HOD_min_lag12 \\\n", + "2020-01-01 00:00:00+00:00 -0.707107 0.707107 \n", + "\n", + " DOW_binary_reg_0_min_lag12 ... \\\n", + "2020-01-01 00:00:00+00:00 0.0 ... \n", + "\n", + " DOW_binary_reg_0_mean DOW_binary_reg_1_mean \\\n", + "2020-01-01 00:00:00+00:00 0.0 0.0 \n", + "\n", + " DOW_binary_reg_2_mean DOW_binary_reg_3_mean \\\n", + "2020-01-01 00:00:00+00:00 1.0 0.0 \n", + "\n", + " DOW_binary_reg_4_mean DOW_binary_reg_5_mean \\\n", + "2020-01-01 00:00:00+00:00 0.0 0.0 \n", + "\n", + " DOW_binary_reg_6_mean sin_MOY_mean cos_MOY_mean \\\n", + "2020-01-01 00:00:00+00:00 0.0 0.017213 0.999852 \n", + "\n", + " Synthetic Site Electricity Main Total Power \n", + "2020-01-01 00:00:00+00:00 0 \n", + "\n", + "[1 rows x 703 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Let's prep those for the model.\n", + "\"\"\"\n", + "train_df, val_df = prep_for_rnn(configs, data)\n", + "val_df" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b43ea0ab-8199-411a-8b2c-a9baeb193ba8", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'exp_dir/train_stats.json'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03mOne line in, one prediction (with many quantiles) out.\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mval_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m results\n", + "File \u001b[0;32m~/Repos/Wattile/wattile/models/AlgoMainRNNBase.py:184\u001b[0m, in \u001b[0;36mAlgoMainRNNBase.predict\u001b[0;34m(self, val_df)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, val_df: pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 184\u001b[0m val_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_normalization\u001b[49m\u001b[43m(\u001b[49m\u001b[43mval_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# only 1 batch during prediction\u001b[39;00m\n\u001b[1;32m 187\u001b[0m val_loader \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_data_loader(\n\u001b[1;32m 188\u001b[0m val_data, batch_size\u001b[38;5;241m=\u001b[39mval_data\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 189\u001b[0m )\n", + "File \u001b[0;32m~/Repos/Wattile/wattile/models/AlgoMainRNNBase.py:104\u001b[0m, in \u001b[0;36mAlgoMainRNNBase.apply_normalization\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Apply normalization to data using the train stats on disk and the transformation_method\u001b[39;00m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;124;03min the configs\u001b[39;00m\n\u001b[1;32m 96\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;124;03m:rtype: pd.DataFrame\u001b[39;00m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 103\u001b[0m train_stats_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_prefix \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_stats.json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_stats_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 105\u001b[0m train_stats \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m 107\u001b[0m transformation_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfigs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_algorithm\u001b[39m\u001b[38;5;124m\"\u001b[39m][\n\u001b[1;32m 108\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransformation_method\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 109\u001b[0m ]\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'exp_dir/train_stats.json'" + ] + } + ], + "source": [ + "\"\"\"\n", + "One line in, one prediction (with many quantiles) out.\n", + "\"\"\"\n", + "results = model.predict(val_df)\n", + "\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70eace15-c5d7-45bb-b622-3a488617702d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/examples/example.ipynb b/notebooks/examples/train.ipynb similarity index 99% rename from notebooks/examples/example.ipynb rename to notebooks/examples/train.ipynb index 38c6fef8..ce089214 100644 --- a/notebooks/examples/example.ipynb +++ b/notebooks/examples/train.ipynb @@ -29,7 +29,7 @@ "\n", "from wattile.entry_point import init_logging\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import prep_for_rnn\n", + "from wattile.data_processing import prep_for_rnn\n", "from wattile.models import ModelFactory\n", "\n", "\n", @@ -115,7 +115,7 @@ "with open(PROJECT_DIRECTORY / \"wattile\" / \"configs\" / \"configs.json\", \"r\") as f:\n", " configs = json.load(f)\n", "\n", - "exp_dir = PROJECT_DIRECTORY / \"notebooks\" / \"exp_dir\"\n", + "exp_dir = PROJECT_DIRECTORY / \"notebooks\" / \"examples\" / \"exp_dir\"\n", "if exp_dir.exists():\n", " shutil.rmtree(exp_dir)\n", "exp_dir.mkdir()\n", diff --git a/notebooks/exploratory/applying_data_to_trained_model.ipynb b/notebooks/exploratory/applying_data_to_trained_model.ipynb index 8dc84f6a..c52808ab 100644 --- a/notebooks/exploratory/applying_data_to_trained_model.ipynb +++ b/notebooks/exploratory/applying_data_to_trained_model.ipynb @@ -27,7 +27,7 @@ "from wattile.entry_point import create_input_dataframe, run_model\n", "from wattile.data_reading import read_dataset_from_file\n", "from wattile.time_processing import add_processed_time_columns\n", - "from wattile.buildings_processing import _preprocess_data, correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, pad_full_data\n", + "from wattile.data_processing import _preprocess_data, correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, pad_full_data\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] }, diff --git a/notebooks/exploratory/charlie_algorithm_revision.ipynb b/notebooks/exploratory/charlie_algorithm_revision.ipynb index 4baebd80..fc7a7339 100644 --- a/notebooks/exploratory/charlie_algorithm_revision.ipynb +++ b/notebooks/exploratory/charlie_algorithm_revision.ipynb @@ -26,7 +26,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, input_data_split\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, input_data_split\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/configs_param_highlights.ipynb b/notebooks/exploratory/configs_param_highlights.ipynb index 0e37c128..361e5f96 100644 --- a/notebooks/exploratory/configs_param_highlights.ipynb +++ b/notebooks/exploratory/configs_param_highlights.ipynb @@ -26,7 +26,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/data_intake_timestamp_aligning.ipynb b/notebooks/exploratory/data_intake_timestamp_aligning.ipynb index 0717dae6..1cb0b961 100644 --- a/notebooks/exploratory/data_intake_timestamp_aligning.ipynb +++ b/notebooks/exploratory/data_intake_timestamp_aligning.ipynb @@ -12,7 +12,7 @@ "\n", "from wattile.entry_point import init_logging, create_input_dataframe, run_model\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import prep_for_rnn, rolling_stats, pad_full_data, input_data_split, _preprocess_data, correct_predictor_columns, correct_timestamps\n", + "from wattile.data_processing import prep_for_rnn, rolling_stats, pad_full_data, input_data_split, _preprocess_data, correct_predictor_columns, correct_timestamps\n", "from wattile.time_processing import add_processed_time_columns\n", "from wattile.entry_point import run_model\n", "\n", diff --git a/notebooks/exploratory/data_processing_bravo_output_window.ipynb b/notebooks/exploratory/data_processing_bravo_output_window.ipynb index e5c040fc..f79269f7 100644 --- a/notebooks/exploratory/data_processing_bravo_output_window.ipynb +++ b/notebooks/exploratory/data_processing_bravo_output_window.ipynb @@ -26,7 +26,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/data_processing_edge_consideration.ipynb b/notebooks/exploratory/data_processing_edge_consideration.ipynb index 4ea1a366..bba6070a 100644 --- a/notebooks/exploratory/data_processing_edge_consideration.ipynb +++ b/notebooks/exploratory/data_processing_edge_consideration.ipynb @@ -16,7 +16,7 @@ "import copy\n", "logger = logging.getLogger(str(os.getpid()))\n", "\n", - "from wattile.buildings_processing import resample_data, roll_data\n", + "from wattile.data_processing import resample_data, roll_data\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] }, diff --git a/notebooks/exploratory/data_processing_edge_testing.ipynb b/notebooks/exploratory/data_processing_edge_testing.ipynb index 4bbdb19d..6ec57497 100644 --- a/notebooks/exploratory/data_processing_edge_testing.ipynb +++ b/notebooks/exploratory/data_processing_edge_testing.ipynb @@ -27,7 +27,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import _resample_data, correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target, input_data_split, prep_for_rnn, _preprocess_data\n", + "from wattile.data_processing import _resample_data, correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target, input_data_split, prep_for_rnn, _preprocess_data\n", "from wattile.time_processing import add_processed_time_columns\n", "from wattile.models import ModelFactory\n", "from wattile.entry_point import init_logging, create_input_dataframe, run_model\n", diff --git a/notebooks/exploratory/data_processing_future_horizon_interval.ipynb b/notebooks/exploratory/data_processing_future_horizon_interval.ipynb index 80241bb1..7b22c0df 100644 --- a/notebooks/exploratory/data_processing_future_horizon_interval.ipynb +++ b/notebooks/exploratory/data_processing_future_horizon_interval.ipynb @@ -26,7 +26,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/data_processing_futurecast.ipynb b/notebooks/exploratory/data_processing_futurecast.ipynb index f05611eb..75974952 100644 --- a/notebooks/exploratory/data_processing_futurecast.ipynb +++ b/notebooks/exploratory/data_processing_futurecast.ipynb @@ -26,7 +26,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/data_processing_output_vector_calculation.ipynb b/notebooks/exploratory/data_processing_output_vector_calculation.ipynb index 94277a33..7a769baf 100644 --- a/notebooks/exploratory/data_processing_output_vector_calculation.ipynb +++ b/notebooks/exploratory/data_processing_output_vector_calculation.ipynb @@ -34,7 +34,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/data_processing_timelag_target_for_bravo_revision.ipynb b/notebooks/exploratory/data_processing_timelag_target_for_bravo_revision.ipynb index 9affebc1..fc94af0d 100644 --- a/notebooks/exploratory/data_processing_timelag_target_for_bravo_revision.ipynb +++ b/notebooks/exploratory/data_processing_timelag_target_for_bravo_revision.ipynb @@ -26,7 +26,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/data_processing_window_width_calc.ipynb b/notebooks/exploratory/data_processing_window_width_calc.ipynb index 565ff5ed..f17f66cb 100644 --- a/notebooks/exploratory/data_processing_window_width_calc.ipynb +++ b/notebooks/exploratory/data_processing_window_width_calc.ipynb @@ -34,7 +34,7 @@ "logger = logging.getLogger(str(os.getpid()))\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats\n", "from wattile.time_processing import add_processed_time_columns\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" ] diff --git a/notebooks/exploratory/empty_predictor_columns_revision.ipynb b/notebooks/exploratory/empty_predictor_columns_revision.ipynb index 323ce554..68ee0c43 100644 --- a/notebooks/exploratory/empty_predictor_columns_revision.ipynb +++ b/notebooks/exploratory/empty_predictor_columns_revision.ipynb @@ -20,7 +20,7 @@ "\n", "from wattile.entry_point import init_logging\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import prep_for_rnn, correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target, input_data_split\n", + "from wattile.data_processing import prep_for_rnn, correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, timelag_predictors, timelag_predictors_target, roll_predictors_target, input_data_split\n", "from wattile.models import ModelFactory\n", "from wattile.time_processing import add_processed_time_columns\n", "\n", diff --git a/notebooks/exploratory/input_data_split_revision.ipynb b/notebooks/exploratory/input_data_split_revision.ipynb index 14e0995a..330736f9 100644 --- a/notebooks/exploratory/input_data_split_revision.ipynb +++ b/notebooks/exploratory/input_data_split_revision.ipynb @@ -27,7 +27,7 @@ "import shutil\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, pad_full_data\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, pad_full_data\n", "from wattile.time_processing import add_processed_time_columns\n", "from wattile.models.liangs_model import main as liangs_model\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" diff --git a/notebooks/exploratory/process_bdgp2_data.ipynb b/notebooks/exploratory/process_bdgp2_data.ipynb new file mode 100644 index 00000000..f4940e59 --- /dev/null +++ b/notebooks/exploratory/process_bdgp2_data.ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from glob import glob\n", + "import pathlib\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path_to_bdgp_data = \"../../../building-data-genome-project-2/data/\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "files_weather = glob(path_to_bdgp_data + \"weather/weather.csv\")\n", + "files_weather" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_weather = pd.read_csv(files_weather[0], index_col=0)\n", + "list_iso = []\n", + "for datetime in df_weather.index:\n", + " list_iso.append(pd.Timestamp(datetime).isoformat())\n", + "df_weather.index = list_iso\n", + "df_weather" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "files_meter = glob(path_to_bdgp_data + \"meters/raw//*.csv\")\n", + "files_meter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list_meters = []\n", + "for file in files_meter:\n", + " list_meters.append(file.rsplit(\"\\\\\",1)[1].split(\".\")[0])\n", + " \n", + "list_meters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dict_region_bldgname = {}\n", + "\n", + "for file in files_meter:\n", + " df_meter = pd.read_csv(file, index_col=0)\n", + " \n", + " for region_bldgtype_bldgname in df_meter.columns:\n", + " \n", + " region = region_bldgtype_bldgname.split(\"_\")[0]\n", + " bldgtype_bldgname = region_bldgtype_bldgname.split(\"_\", 1)[1]\n", + " bldgname = region_bldgtype_bldgname.split(\"_\")[2]\n", + " \n", + " if region in dict_region_bldgname.keys(): \n", + " dict_region_bldgname[region].append(bldgtype_bldgname)\n", + " else:\n", + " dict_region_bldgname[region] = [bldgtype_bldgname]\n", + " \n", + "for region in dict_region_bldgname.keys():\n", + " \n", + " dict_region_bldgname[region] = pd.DataFrame(dict_region_bldgname[region]).drop_duplicates().values.flatten().tolist()\n", + " \n", + "dict_region_bldgname" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for region in dict_region_bldgname.keys():\n", + " \n", + " print(\"### proceeing: {}\".format(region))\n", + " \n", + " for bldgname in dict_region_bldgname[region]:\n", + " \n", + " path_to_save_processed_files = \"../../data/BDGP2/\".format(bldgname)\n", + " pathlib.Path(path_to_save_processed_files).mkdir(parents=True, exist_ok=True)\n", + " \n", + " datetime_start = pd.Timestamp(year=2200, month=1, day=1, hour=0)\n", + " datetime_end = pd.Timestamp(year=1900, month=1, day=1, hour=0)\n", + " \n", + " #################################################################\n", + " # process predictors\n", + " #################################################################\n", + "\n", + " df_predictors = pd.DataFrame()\n", + "\n", + " df_weather_filtered = df_weather.loc[df_weather[\"site_id\"]==region, :].sort_index().copy()\n", + " df_weather_filtered = df_weather_filtered.loc[:,df_weather_filtered.columns!=\"site_id\"].copy()\n", + "\n", + " df_predictors = df_predictors.join(df_weather_filtered, how=\"outer\")\n", + " df_predictors.index.names = ['Timestamp']\n", + " df_predictors.to_csv(path_to_save_processed_files+\"{}_Predictors.csv\".format(bldgname)) \n", + " \n", + " if pd.Timestamp(df_predictors.index[0]) < datetime_start:\n", + " datetime_start = pd.Timestamp(df_predictors.index[0])\n", + " \n", + " if pd.Timestamp(df_predictors.index[-1]) > datetime_end:\n", + " datetime_end = pd.Timestamp(df_predictors.index[-1])\n", + " \n", + " #################################################################\n", + " # process targets\n", + " #################################################################\n", + " \n", + " df_targets = pd.DataFrame()\n", + " \n", + " for meter in list_meters:\n", + " \n", + " df = pd.read_csv(path_to_bdgp_data + \"meters/raw/{}.csv\".format(meter), index_col=0)\n", + " df_bldg = df.loc[:, df.columns==\"{}_{}\".format(region, bldgname)]\n", + " list_iso = []\n", + " for datetime in df_bldg.index:\n", + " list_iso.append(pd.Timestamp(datetime).isoformat())\n", + " df_bldg.index = list_iso\n", + " \n", + " if df_bldg.shape[1] != 0:\n", + " df_bldg.columns = [meter]\n", + " df_targets = df_targets.join(df_bldg, how=\"outer\")\n", + " \n", + " df_targets.index.names = ['Timestamp']\n", + " df_targets.to_csv(path_to_save_processed_files+\"{}_Targets.csv\".format(bldgname))\n", + " \n", + " if pd.Timestamp(df_targets.index[0]) < datetime_start:\n", + " datetime_start = pd.Timestamp(df_targets.index[0])\n", + " \n", + " if pd.Timestamp(df_targets.index[-1]) > datetime_end:\n", + " datetime_end = pd.Timestamp(df_targets.index[-1])\n", + " \n", + " #################################################################\n", + " # process configs\n", + " #################################################################\n", + " \n", + " configs = {}\n", + " \n", + " # ---------------------------------------------------------------\n", + " configs[\"dates\"] = {}\n", + " configs[\"dates\"][\"start\"] = \"t:\"+datetime_start.isoformat()+\"-00:00\"\n", + " configs[\"dates\"][\"end\"] = \"t:\"+datetime_end.isoformat()+\"-00:00\"\n", + " \n", + " # ---------------------------------------------------------------\n", + " configs[\"predictors\"] = []\n", + " list_predictors = glob(path_to_save_processed_files + \"*_Predictors.csv\")\n", + " if len(list_predictors)==1:\n", + " df_predictors = pd.read_csv(list_predictors[0], index_col=0)\n", + " \n", + " for predictor in df_predictors.columns:\n", + " \n", + " content = {}\n", + " content[\"id\"] = predictor\n", + " content[\"dis\"] = predictor\n", + " content[\"column\"] = predictor\n", + " content[\"unit\"] = \"na\" \n", + " configs[\"predictors\"].append(content)\n", + " \n", + " # ---------------------------------------------------------------\n", + " configs[\"targets\"] = []\n", + " list_targets = glob(path_to_save_processed_files + \"*_Targets.csv\")\n", + " if len(list_targets)==1:\n", + " df_targets = pd.read_csv(list_targets[0], index_col=0)\n", + " \n", + " for target in df_targets.columns:\n", + " \n", + " content = {}\n", + " content[\"id\"] = target\n", + " content[\"dis\"] = target\n", + " content[\"column\"] = target\n", + " content[\"unit\"] = \"na\" \n", + " configs[\"targets\"].append(content)\n", + " \n", + " # ---------------------------------------------------------------\n", + " configs[\"files\"] = []\n", + " \n", + " content_p = {}\n", + " content_p[\"filename\"] = list_predictors[0].rsplit(\"\\\\\", 1)[1]\n", + " content_p[\"contentType\"] = \"predictors\"\n", + " content_p[\"start\"] = \"t:\"+ pd.Timestamp(pd.read_csv(list_predictors[0], index_col=0).index[0]).isoformat() +\"-00:00\"\n", + " content_p[\"end\"] = \"t:\"+ pd.Timestamp(pd.read_csv(list_predictors[0], index_col=0).index[-1]).isoformat() +\"-00:00\"\n", + " configs[\"files\"].append(content_p)\n", + " \n", + " content_t = {}\n", + " content_t[\"filename\"] = list_targets[0].rsplit(\"\\\\\", 1)[1]\n", + " content_t[\"contentType\"] = \"targets\"\n", + " content_t[\"start\"] = \"t:\"+ pd.Timestamp(pd.read_csv(list_targets[0], index_col=0).index[0]).isoformat() +\"-00:00\"\n", + " content_t[\"end\"] = \"t:\"+ pd.Timestamp(pd.read_csv(list_targets[0], index_col=0).index[-1]).isoformat() +\"-00:00\"\n", + " configs[\"files\"].append(content_t)\n", + " \n", + " with open(path_to_save_processed_files+\"{} Config.json\".format(bldgname), \"w\") as fp:\n", + " json.dump(configs, fp, ensure_ascii=False)\n", + " \n", + "# break\n", + " \n", + "# break\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/exploratory/roll_full_data_s2s_revision.ipynb b/notebooks/exploratory/roll_full_data_s2s_revision.ipynb index da72441a..1706319d 100644 --- a/notebooks/exploratory/roll_full_data_s2s_revision.ipynb +++ b/notebooks/exploratory/roll_full_data_s2s_revision.ipynb @@ -27,7 +27,7 @@ "import shutil\n", "\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, input_data_split\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, resample_or_rolling_stats, input_data_split\n", "from wattile.time_processing import add_processed_time_columns\n", "from wattile.models.liangs_model import main as liangs_model\n", "PROJECT_DIRECTORY = Path().resolve().parent.parent" diff --git a/notebooks/exploratory/rolling_stats_revision.ipynb b/notebooks/exploratory/rolling_stats_revision.ipynb index 0becdd86..96cee38a 100644 --- a/notebooks/exploratory/rolling_stats_revision.ipynb +++ b/notebooks/exploratory/rolling_stats_revision.ipynb @@ -12,7 +12,7 @@ "\n", "from wattile.entry_point import init_logging, create_input_dataframe, run_model\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import prep_for_rnn, rolling_stats, pad_full_data, input_data_split\n", + "from wattile.data_processing import prep_for_rnn, rolling_stats, pad_full_data, input_data_split\n", "from wattile.entry_point import run_model\n", "\n", "\n", diff --git a/notebooks/exploratory/timelags_revision.ipynb b/notebooks/exploratory/timelags_revision.ipynb index 2c38523e..8539c616 100644 --- a/notebooks/exploratory/timelags_revision.ipynb +++ b/notebooks/exploratory/timelags_revision.ipynb @@ -12,7 +12,7 @@ "\n", "from wattile.entry_point import init_logging, create_input_dataframe, run_model\n", "from wattile.data_reading import read_dataset_from_file\n", - "from wattile.buildings_processing import correct_predictor_columns, correct_timestamps, pad_full_data, input_data_split, rolling_stats\n", + "from wattile.data_processing import correct_predictor_columns, correct_timestamps, pad_full_data, input_data_split, rolling_stats\n", "from wattile.time_processing import add_processed_time_columns\n", "from wattile.entry_point import run_model\n", "\n", diff --git a/notebooks/exploratory/visualization_revision.ipynb b/notebooks/exploratory/visualization_revision.ipynb index 53d38653..e46e1573 100644 --- a/notebooks/exploratory/visualization_revision.ipynb +++ b/notebooks/exploratory/visualization_revision.ipynb @@ -33,75 +33,62 @@ { "data": { "text/plain": [ - "{'target_var': 'Synthetic Site Electricity Main Total Power',\n", - " 'start_time': '2018-01-01T00:00:00-07:00',\n", - " 'end_time': '2022-01-01T00:00:00-07:00',\n", - " 'predictor_columns': ['Synthetic Weather Station Dew Point Temperature',\n", - " 'Synthetic Weather Station Diffuse Horizontal Irradiance',\n", - " 'Synthetic Weather Station Direct Normal Irradiance',\n", - " 'Synthetic Weather Station Dry Bulb Temperature',\n", - " 'Synthetic Weather Station Global Horizontal Irradiance',\n", - " 'Synthetic Weather Station Relative Humidity',\n", - " 'Synthetic Weather Station Wind Speed'],\n", - " 'arch_version': 'charlie',\n", - " 'exp_id': '7-9-21',\n", - " 'arch_type': 'RNN',\n", - " 'arch_type_variant': 'lstm',\n", - " 'transformation_method': 'minmaxscale',\n", - " 'train_batch_size': 26,\n", - " 'val_batch_size': 1,\n", - " 'convert_csvs': False,\n", - " 'exp_dir': 'C:\\\\Users\\\\JKIM4\\\\Documents\\\\GitHub\\\\intelligentcampus-pred-analytics\\\\notebooks\\\\exp_dir',\n", - " 'data_dir': 'C:\\\\Users\\\\JKIM4\\\\Documents\\\\GitHub\\\\intelligentcampus-pred-analytics\\\\data\\\\Synthetic Site',\n", - " 'data_config': 'Synthetic Site Config.json',\n", - " 'resample_freq': 15,\n", - " 'resample_interval': '15min',\n", - " 'sequential_splicer': {'active': False, 'window_width': '1W'},\n", - " 'feat_time': {'month_of_year': ['sincos'],\n", - " 'day_of_week': ['binary_reg', 'binary_fuzzy'],\n", - " 'hour_of_day': ['sincos', 'binary_reg', 'binary_fuzzy'],\n", - " 'holidays': False},\n", - " 'feat_stats': {'active': True, 'window_width': '15min'},\n", - " 'feat_timelag': {'lag_interval': '15min',\n", - " 'lag_count': 24,\n", - " 'lag_interval_forecast': '0min'},\n", - " 'S2S_window': {'window_width_source': '180min',\n", - " 'window_width_target': '45min',\n", - " 'normalization': False},\n", - " 'S2S_learning': {'cell_type': 'lstm',\n", - " 'attention_model': 'BA',\n", - " 'la_method': 'none',\n", - " 'batch_size': 256,\n", - " 'hidden_size': 64,\n", - " 'cuda': False,\n", - " 'save_model': False},\n", - " 'S2S_stagger': {'initial_num': 72, 'decay': 0, 'secondary_num': 0},\n", - " 'train_size_factor': 1,\n", - " 'train_val_split': 'Random',\n", - " 'data_split': '80:20:0',\n", - " 'random_seed': 382,\n", - " 'qs': [0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.975],\n", - " 'use_case': 'train',\n", - " 'run_resume': False,\n", - " 'num_epochs': 100,\n", - " 'weight_decay': 0.001,\n", - " 'hidden_nodes': 25,\n", - " 'layer_dim': 1,\n", - " 'eval_frequency': 500,\n", - " 'lr_config': {'base': 0.001,\n", - " 'schedule': False,\n", - " 'type': 'performance',\n", - " 'factor': 0.1,\n", - " 'min': 1e-05,\n", - " 'patience': 30,\n", - " 'step_size': 75},\n", - " 'smoothing_alpha': 0.001,\n", - " 'test_method': 'external',\n", - " 'external_test': {'year': '2020', 'month': [1, 2, 3, 4, 5, 6]},\n", - " 'trained_model_path': './EnergyForecasting_Results/RNN_MSyntheticSiteElectricityMainTotalPower_T7-9-21',\n", - " 'plot_comparison': True,\n", - " 'plot_comparison_portion_start': 0.0,\n", - " 'plot_comparison_portion_end': 1.0}" + "{'data_input': {'data_dir': 'C:\\\\Users\\\\JKIM4\\\\Documents\\\\GitHub\\\\intelligentcampus-pred-analytics\\\\tests\\\\data\\\\Synthetic Site',\n", + " 'data_config': 'Synthetic Site Config.json',\n", + " 'start_time': '2018-01-01T00:00:00-07:00',\n", + " 'end_time': '2022-01-01T00:00:00-07:00',\n", + " 'predictor_columns': ['Synthetic Weather Station Dew Point Temperature',\n", + " 'Synthetic Weather Station Diffuse Horizontal Irradiance',\n", + " 'Synthetic Weather Station Direct Normal Irradiance',\n", + " 'Synthetic Weather Station Dry Bulb Temperature',\n", + " 'Synthetic Weather Station Global Horizontal Irradiance',\n", + " 'Synthetic Weather Station Relative Humidity',\n", + " 'Synthetic Weather Station Wind Speed'],\n", + " 'target_var': 'Synthetic Site Electricity Main Total Power'},\n", + " 'data_output': {'exp_dir': 'C:\\\\Users\\\\JKIM4\\\\Documents\\\\GitHub\\\\intelligentcampus-pred-analytics\\\\notebooks\\\\exp_dir',\n", + " 'plot_comparison': True,\n", + " 'plot_comparison_portion_start': 0.0,\n", + " 'plot_comparison_portion_end': 1.0},\n", + " 'data_processing': {'feat_time': {'month_of_year': ['sincos'],\n", + " 'day_of_week': ['binary_reg', 'binary_fuzzy'],\n", + " 'hour_of_day': ['sincos', 'binary_reg', 'binary_fuzzy'],\n", + " 'holidays': False},\n", + " 'resample': {'bin_interval': '15min',\n", + " 'bin_closed': 'right',\n", + " 'bin_label': 'right'},\n", + " 'feat_stats': {'active': True, 'window_width': '15min'},\n", + " 'feat_timelag': {'lag_interval': '15min', 'lag_count': 24},\n", + " 'input_output_window': {'window_width_source': '180min',\n", + " 'window_width_futurecast': '0min',\n", + " 'window_width_target': '45min'},\n", + " 'random_seed': 382,\n", + " 'sequential_splicer': {'active': False, 'window_width': '1W'},\n", + " 'data_split': '80:20:0',\n", + " 'train_size_factor': 1},\n", + " 'learning_algorithm': {'arch_type': 'RNN',\n", + " 'arch_version': 'alfa',\n", + " 'arch_type_variant': 'lstm',\n", + " 'use_case': 'validation',\n", + " 'num_epochs': 100,\n", + " 'hidden_size': 25,\n", + " 'num_layers': 1,\n", + " 'quantiles': [0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.975],\n", + " 'transformation_method': 'minmaxscale',\n", + " 'train_batch_size': 26,\n", + " 'val_batch_size': 1,\n", + " 'run_resume': False,\n", + " 'optimizer_config': {'weight_decay': 0.001,\n", + " 'base': 0.001,\n", + " 'schedule': False,\n", + " 'type': 'performance',\n", + " 'factor': 0.1,\n", + " 'min': 1e-05,\n", + " 'patience': 30,\n", + " 'step_size': 75},\n", + " 'smoothing_alpha': 0.001,\n", + " 'eval_frequency': 500,\n", + " 'test_method': 'external'},\n", + " 'input_dim': 219}" ] }, "execution_count": 3, @@ -118,16 +105,8 @@ "# choose the configs file to use as an input\n", "##################################################################################\n", "# main configs file\n", - "with open(PROJECT_DIRECTORY / \"wattile\" / \"configs\" / \"configs.json\", \"r\") as f:\n", + "with open(PROJECT_DIRECTORY / \"notebooks\" / \"exp_dir\" / \"configs.json\", \"r\") as f:\n", " configs = json.load(f)\n", - "##################################################################################\n", - "# code testing configs file\n", - "# with open(PROJECT_DIRECTORY / \"tests\" / \"fixtures\" / \"test_configs.json\", \"r\") as f:\n", - "# configs = json.load(f)\n", - "##################################################################################\n", - "\n", - "configs[\"exp_dir\"] = str(PROJECT_DIRECTORY / \"notebooks\" / \"exp_dir\")\n", - "configs[\"data_dir\"] = str(PROJECT_DIRECTORY / \"data\" / \"Synthetic Site\")\n", "\n", "configs" ] @@ -136,21 +115,6 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [ - "#################################################\n", - "# Common settings for graphics\n", - "#################################################\n", - "font_family = \"Raleway, PT Sans Narrow, Overpass, Arial\"\n", - "width_graphic = 1500\n", - "color_base = \"rgb(236,200,178)\"\n", - "color_accent = \"rgb(80,70,69)\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, "outputs": [ { "data": { @@ -173,112 +137,197 @@ " \n", " \n", " \n", - " 0.5_0\n", - " 0.5_1\n", - " 0.5_2\n", + " 0\n", + " 1\n", + " 2\n", + " 3\n", + " 4\n", + " 5\n", + " 6\n", + " 7\n", + " 8\n", " \n", " \n", " \n", " \n", " 0\n", - " 101.106140\n", - " 100.521271\n", - " 100.302223\n", + " 96.989944\n", + " 97.287025\n", + " 97.988083\n", + " 99.869446\n", + " 102.500244\n", + " 105.219925\n", + " 107.317200\n", + " 107.932655\n", + " 108.566772\n", " \n", " \n", " 1\n", - " 100.527672\n", - " 99.556831\n", - " 99.423378\n", + " 96.617714\n", + " 96.908447\n", + " 97.591499\n", + " 99.424469\n", + " 101.986282\n", + " 104.687805\n", + " 106.737106\n", + " 107.349091\n", + " 107.993713\n", " \n", " \n", " 2\n", - " 96.544724\n", - " 95.281425\n", - " 94.003265\n", + " 96.486145\n", + " 96.775352\n", + " 97.451279\n", + " 99.252129\n", + " 101.754318\n", + " 104.410431\n", + " 106.434090\n", + " 107.044464\n", + " 107.695671\n", " \n", " \n", " 3\n", - " 101.870895\n", - " 102.104439\n", - " 100.942848\n", + " 96.652756\n", + " 96.947098\n", + " 97.625931\n", + " 99.415771\n", + " 101.875076\n", + " 104.484657\n", + " 106.500259\n", + " 107.111710\n", + " 107.760239\n", " \n", " \n", " 4\n", - " 100.086372\n", - " 100.029823\n", - " 101.113686\n", + " 96.389473\n", + " 96.675156\n", + " 97.335373\n", + " 99.023605\n", + " 101.362068\n", + " 103.828178\n", + " 105.749290\n", + " 106.377357\n", + " 107.017891\n", " \n", " \n", " ...\n", " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 127\n", - " 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"execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "configs[\"arch_version\"] = \"charlie\"\n", - "configs[\"qs\"] = [0.5]" + "time_ahead = 0" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "time_ahead = 0" + "#################################################\n", + "# Common settings for graphics\n", + "#################################################\n", + "font_family = \"Raleway, PT Sans Narrow, Overpass, Arial\"\n", + "width_graphic = 900\n", + "color_outer = \"rgb(240,240,240)\"\n", + "color_inner = \"rgb(216,179,101)\"\n", + "color_actual = \"rgb(0,60,48)\"\n", + "color_background = \"rgb(255,255,255)\"" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -435,1313 +467,56 @@ "text": [ "saving timeseries comparison in C:\\Users\\JKIM4\\Documents\\GitHub\\intelligentcampus-pred-analytics\\notebooks\\exp_dir/Vis_TimeseriesComparisons.svg\n" ] - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "######################################################################\n", - "# reading measured and prediction files\n", - "######################################################################\n", - "predictions = pd.read_hdf(configs[\"exp_dir\"] + \"/predictions.h5\")\n", - "measured = pd.read_hdf(configs[\"exp_dir\"] + \"/measured.h5\")\n", + "\"\"\"\n", + "read measured and prediction files\n", + "\"\"\"\n", + "predictions = pd.read_hdf(configs[\"data_output\"][\"exp_dir\"] + \"/predictions.h5\")\n", + "measured = pd.read_hdf(configs[\"data_output\"][\"exp_dir\"] + \"/measured.h5\")\n", + "\n", + "\"\"\"\n", + "read configs parameters\n", + "\"\"\"\n", + "window_width_target = configs[\"data_processing\"][\"input_output_window\"][\n", + " \"window_width_target\"\n", + "]\n", + "bin_interval = configs[\"data_processing\"][\"resample\"][\"bin_interval\"]\n", + "initial_num = (pd.Timedelta(window_width_target) // pd.Timedelta(bin_interval)) + 1\n", + "use_case = configs[\"learning_algorithm\"][\"use_case\"]\n", + "quantiles = configs[\"learning_algorithm\"][\"quantiles\"]\n", + "\n", + "\"\"\"\n", + "set colors\n", + "\"\"\"\n", + "len_predictions = len(predictions.columns)\n", + "len_colors = int((len(quantiles) - 1) / 2)\n", + "if len_colors == 1:\n", + " list_color = [color_outer]\n", + "else:\n", + " list_color = n_colors(color_outer, color_inner, len_colors, colortype=\"rgb\")\n", + "\n", + "\"\"\"\n", + "initialize y-axis boundaries for finding min and max\n", + "\"\"\"\n", + "y_max = 0\n", + "y_min = 99999999999999\n", + "\n", + "\"\"\"\n", + "check timestamp order\n", + "\"\"\"\n", + "timestamporder = (\n", + " \"timestamp in sequence\" if use_case == \"validation\" else \"timestamp shuffled\"\n", + ")\n", "\n", - "######################################################################\n", - "# filter data based on portion defined in configs.json\n", - "######################################################################\n", - "plot_comparison_portion_start = configs[\"plot_comparison_portion_start\"]\n", - "plot_comparison_portion_end = configs[\"plot_comparison_portion_end\"]\n", + "\"\"\"\n", + "filter data based on portion defined in configs\n", + "\"\"\"\n", + "plot_comparison_portion_start = configs[\"data_output\"][\n", + " \"plot_comparison_portion_start\"\n", + "]\n", + "plot_comparison_portion_end = configs[\"data_output\"][\"plot_comparison_portion_end\"]\n", "predictions = predictions.iloc[\n", " int(predictions.shape[0] * plot_comparison_portion_start) : int(\n", " predictions.shape[0] * plot_comparison_portion_end\n", @@ -1755,29 +530,9 @@ " :,\n", "]\n", "\n", - "######################################################################\n", - "# plot setting\n", - "######################################################################\n", - "len_tot = len(predictions.columns)\n", - "target = measured.to_numpy()\n", - "c_target = \"rgb(228,26,28)\"\n", - "len_c_list = int((len(configs[\"qs\"]) - 1) / 2)\n", - "if len_c_list == 1:\n", - " c_list = [color_base]\n", - "else:\n", - " c_list = n_colors(color_base, color_accent, len_c_list, colortype=\"rgb\")\n", - "list_c_low_penalty = c_list.copy()\n", - "list_c_high_penalty = c_list.copy()\n", - "list_c_is = c_list.copy()\n", - "list_c_is.reverse()\n", - "y_min = 10000000\n", - "y_max = 0\n", - "window_target_size = configs[\"S2S_window\"][\"window_width_target\"]\n", - "resample_interval = configs[\"resample_interval\"]\n", - "\n", - "######################################################################\n", - "# initialize plotting area\n", - "######################################################################\n", + "\"\"\"\n", + "initialize plot\n", + "\"\"\"\n", "fig = make_subplots(\n", " rows=4,\n", " cols=1,\n", @@ -1786,52 +541,45 @@ " row_heights=[0.5, 0.15, 0.15, 0.15],\n", ")\n", "\n", - "######################################################################\n", - "# if quantiles were only defined with median\n", - "######################################################################\n", - "if len(configs[\"qs\"]) == 1:\n", + "\"\"\"\n", + "extract median prediction and, if necessary, plot prediction quantiles/errors\n", + "\"\"\"\n", + "if len(quantiles) == 1:\n", "\n", - " # -----------------------------------------------------------------\n", - " # extracting median prediction\n", - " # -----------------------------------------------------------------\n", + " # extract median prediction\n", " prediction_median = predictions.iloc[:, 0].values\n", "\n", + " # find min/max y-values\n", " if min(prediction_median.min(), measured.iloc[:, 0].min()) < y_min:\n", " y_min = min(prediction_median.min(), measured.iloc[:, 0].min())\n", - "\n", " if max(prediction_median.max(), measured.iloc[:, 0].max()) > y_max:\n", " y_max = max(prediction_median.max(), measured.iloc[:, 0].max())\n", "\n", - "######################################################################\n", - "# if quantiles were defined properly\n", - "######################################################################\n", "else:\n", "\n", - " # -----------------------------------------------------------------\n", - " # extracting median prediction\n", - " # -----------------------------------------------------------------\n", - " if configs[\"arch_version\"] == \"alfa\":\n", + " # extract median prediction\n", + " if configs[\"learning_algorithm\"][\"arch_version\"] == \"alfa\":\n", "\n", - " idx_col_med = len_c_list\n", + " idx_col_med = len_colors\n", " prediction_median = predictions.iloc[:, idx_col_med].values\n", " time_ahead = 0\n", "\n", - " elif configs[\"arch_version\"] == \"bravo\":\n", + " elif configs[\"learning_algorithm\"][\"arch_version\"] == \"bravo\":\n", "\n", - " idx_col_med_start = len_c_list * configs[\"S2S_stagger\"][\"initial_num\"]\n", - " idx_col_med_end = idx_col_med_start + configs[\"S2S_stagger\"][\"initial_num\"]\n", + " idx_col_med_start = len_colors * initial_num\n", + " idx_col_med_end = idx_col_med_start + initial_num\n", " prediction_median = (\n", " predictions.iloc[:, idx_col_med_start:idx_col_med_end]\n", " .iloc[:, time_ahead]\n", " .values\n", " )\n", "\n", - " elif configs[\"arch_version\"] == \"charlie\":\n", + " elif configs[\"learning_algorithm\"][\"arch_version\"] == \"charlie\":\n", "\n", " window_target_size_count = window_target_size_count = int(\n", - " pd.Timedelta(window_target_size) / pd.Timedelta(resample_interval)\n", + " pd.Timedelta(window_width_target) / pd.Timedelta(bin_interval)\n", " )\n", - " idx_col_med_start = len_c_list * window_target_size_count\n", + " idx_col_med_start = len_colors * window_target_size_count\n", " idx_col_med_end = idx_col_med_start + window_target_size_count\n", " prediction_median = (\n", " predictions.iloc[:, idx_col_med_start:idx_col_med_end]\n", @@ -1839,138 +587,134 @@ " .values\n", " )\n", "\n", - " # -----------------------------------------------------------------\n", - " # extracting quantile predictions\n", - " # -----------------------------------------------------------------\n", - " count_qntl = 0\n", - " for qntl in range(0, int((len(configs[\"qs\"]) - 1) / 2)):\n", + " # extract quantile predictions\n", + " for qntl in range(0, int(len_colors)):\n", "\n", - " # -------------------------------------------------------------\n", - " # filtering data for correct quantile\n", - " # -------------------------------------------------------------\n", - " if configs[\"arch_version\"] == \"alfa\":\n", + " # filter data for correct quantile\n", + " if configs[\"learning_algorithm\"][\"arch_version\"] == \"alfa\":\n", "\n", " idx_col_low_start = qntl\n", - " idx_col_high_start = len_tot - idx_col_low_start - 1\n", - "\n", - " prediction_low = predictions.iloc[:, idx_col_low_start].to_numpy()\n", - " prediction_high = predictions.iloc[:, idx_col_high_start].to_numpy()\n", - "\n", - " elif configs[\"arch_version\"] == \"bravo\":\n", + " idx_col_high_start = len_predictions - idx_col_low_start - 1\n", + " prediction_qntl_low = predictions.iloc[:, idx_col_low_start].to_numpy()\n", + " prediction_qntl_high = predictions.iloc[\n", + " :, idx_col_high_start\n", + " ].to_numpy()\n", "\n", - " idx_col_low_start = qntl * configs[\"S2S_stagger\"][\"initial_num\"]\n", - " idx_col_low_end = (\n", - " qntl * configs[\"S2S_stagger\"][\"initial_num\"]\n", - " + configs[\"S2S_stagger\"][\"initial_num\"]\n", - " )\n", - " idx_col_high_start = (len_tot - idx_col_low_start) - configs[\n", - " \"S2S_stagger\"\n", - " ][\"initial_num\"]\n", - " idx_col_high_end = len_tot - idx_col_low_start\n", + " elif configs[\"learning_algorithm\"][\"arch_version\"] == \"bravo\":\n", "\n", - " prediction_low = predictions.iloc[\n", + " idx_col_low_start = qntl * initial_num\n", + " idx_col_low_end = qntl * initial_num + initial_num\n", + " idx_col_high_start = (len_predictions - idx_col_low_start) - initial_num\n", + " idx_col_high_end = len_predictions - idx_col_low_start\n", + " prediction_qntl_low = predictions.iloc[\n", " :, idx_col_low_start:idx_col_low_end\n", " ].to_numpy()\n", - " prediction_high = predictions.iloc[\n", + " prediction_qntl_high = predictions.iloc[\n", " :, idx_col_high_start:idx_col_high_end\n", " ].to_numpy()\n", "\n", - " elif configs[\"arch_version\"] == \"charlie\":\n", + " elif configs[\"learning_algorithm\"][\"arch_version\"] == \"charlie\":\n", "\n", " window_target_size_count = int(\n", - " pd.Timedelta(window_target_size) / pd.Timedelta(resample_interval)\n", + " pd.Timedelta(window_width_target) / pd.Timedelta(bin_interval)\n", " )\n", - "\n", " idx_col_low_start = qntl * window_target_size_count\n", " idx_col_low_end = (\n", " qntl * window_target_size_count + window_target_size_count\n", " )\n", " idx_col_high_start = (\n", - " len_tot - idx_col_low_start\n", + " len_predictions - idx_col_low_start\n", " ) - window_target_size_count\n", - " idx_col_high_end = len_tot - idx_col_low_start\n", - "\n", - " prediction_low = predictions.iloc[\n", + " idx_col_high_end = len_predictions - idx_col_low_start\n", + " prediction_qntl_low = predictions.iloc[\n", " :, idx_col_low_start:idx_col_low_end\n", " ].to_numpy()\n", - " prediction_high = predictions.iloc[\n", + " prediction_qntl_high = predictions.iloc[\n", " :, idx_col_high_start:idx_col_high_end\n", " ].to_numpy()\n", "\n", - " # -------------------------------------------------------------\n", - " # calculating Interval Score details\n", - " # -------------------------------------------------------------\n", - " alph = 1 - (configs[\"qs\"][-(qntl + 1)] - configs[\"qs\"][qntl])\n", + " # calculate Interval Score details\n", + " alph = 1 - (quantiles[-(qntl + 1)] - quantiles[qntl])\n", " IS = (\n", - " (prediction_high - prediction_low)\n", - " + (2 / alph) * (prediction_low - target) * (target < prediction_low)\n", - " + (2 / alph) * (target - prediction_high) * (target > prediction_high)\n", + " (prediction_qntl_high - prediction_qntl_low)\n", + " + (2 / alph)\n", + " * (prediction_qntl_low - measured.to_numpy())\n", + " * (measured.to_numpy() < prediction_qntl_low)\n", + " + (2 / alph)\n", + " * (measured.to_numpy() - prediction_qntl_high)\n", + " * (measured.to_numpy() > prediction_qntl_high)\n", " ) # (batches * time) for a single quantile\n", - "\n", - " df_prediction_low = pd.DataFrame(prediction_low)\n", - " df_prediction_high = pd.DataFrame(prediction_high)\n", + " prediction_qntl_low = pd.DataFrame(prediction_qntl_low)\n", + " prediction_qntl_high = pd.DataFrame(prediction_qntl_high)\n", " df_is = pd.DataFrame(IS)\n", "\n", - " df_viol_l = (df_prediction_low[time_ahead] - measured[time_ahead]) * (\n", - " df_prediction_low[time_ahead] > measured[time_ahead]\n", + " # calculate upper/lower quantile violations\n", + " df_viol_l = (prediction_qntl_low[time_ahead] - measured[time_ahead]) * (\n", + " prediction_qntl_low[time_ahead] > measured[time_ahead]\n", " )\n", - " df_viol_u = (measured[time_ahead] - df_prediction_high[time_ahead]) * (\n", - " measured[time_ahead] > df_prediction_high[time_ahead]\n", + " df_viol_u = (measured[time_ahead] - prediction_qntl_high[time_ahead]) * (\n", + " measured[time_ahead] > prediction_qntl_high[time_ahead]\n", " )\n", "\n", - " if df_prediction_low[time_ahead].min() < y_min:\n", - " y_min = df_prediction_low[time_ahead].min()\n", - "\n", - " if df_prediction_high[time_ahead].max() > y_max:\n", - " y_max = df_prediction_high[time_ahead].max()\n", + " # find min/max y values\n", + " if prediction_qntl_low[time_ahead].min() < y_min:\n", + " y_min = prediction_qntl_low[time_ahead].min()\n", + " if prediction_qntl_high[time_ahead].max() > y_max:\n", + " y_max = prediction_qntl_high[time_ahead].max()\n", "\n", - " # -------------------------------------------------------------\n", - " # plotting prediction lower bound\n", - " # -------------------------------------------------------------\n", + " # plot prediction quantile lower bound\n", " fig.add_trace(\n", " go.Scatter(\n", - " y=df_prediction_low[time_ahead],\n", + " y=prediction_qntl_low[time_ahead],\n", " mode=\"lines\",\n", - " name=\"Quantile{}\".format(qntl),\n", - " legendgroup=\"Quantile{}\".format(qntl),\n", - " showlegend=True,\n", + " name=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " legendgroup=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " showlegend=False,\n", " xaxis=\"x\",\n", " opacity=0.1,\n", - " line=dict(color=c_list[qntl], width=0),\n", + " line=dict(color=list_color[qntl], width=1),\n", " ),\n", " row=1,\n", " col=1,\n", " )\n", "\n", - " # -------------------------------------------------------------\n", - " # plotting prediction higher bound\n", - " # -------------------------------------------------------------\n", + " # plot prediction quantile higher bound\n", " fig.add_trace(\n", " go.Scatter(\n", - " y=df_prediction_high[time_ahead],\n", + " y=prediction_qntl_high[time_ahead],\n", " mode=\"lines\",\n", - " name=\"Quantile{}\".format(qntl),\n", - " legendgroup=\"Quantile{}\".format(qntl),\n", - " showlegend=False,\n", + " name=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " legendgroup=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " showlegend=True,\n", " xaxis=\"x\",\n", " fill=\"tonexty\",\n", " opacity=0.1,\n", - " line=dict(color=c_list[qntl], width=0),\n", + " line=dict(color=list_color[qntl], width=1),\n", " ),\n", " row=1,\n", " col=1,\n", " )\n", "\n", - " # -------------------------------------------------------------\n", - " # plotting prediction lower violation\n", - " # -------------------------------------------------------------\n", + " # plot prediction quantile higher bound violation\n", " fig.add_trace(\n", " go.Scatter(\n", " y=df_viol_l,\n", " mode=\"lines\",\n", - " name=\"Quantile{}\".format(qntl),\n", - " legendgroup=\"Quantile{}\".format(qntl),\n", - " line_color=list_c_low_penalty[count_qntl],\n", + " name=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " legendgroup=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " line_color=list_color[qntl],\n", " showlegend=False,\n", " xaxis=\"x\",\n", " yaxis=\"y1\",\n", @@ -1979,16 +723,18 @@ " col=1,\n", " )\n", "\n", - " # -------------------------------------------------------------\n", - " # plotting prediction higher violation\n", - " # -------------------------------------------------------------\n", + " # plot prediction quantile higher bound violation\n", " fig.add_trace(\n", " go.Scatter(\n", " y=df_viol_u,\n", " mode=\"lines\",\n", - " name=\"Quantile{}\".format(qntl),\n", - " legendgroup=\"Quantile{}\".format(qntl),\n", - " line_color=list_c_high_penalty[count_qntl],\n", + " name=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " legendgroup=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " line_color=list_color[qntl],\n", " showlegend=False,\n", " xaxis=\"x\",\n", " yaxis=\"y1\",\n", @@ -1997,16 +743,18 @@ " col=1,\n", " )\n", "\n", - " # -------------------------------------------------------------\n", - " # plotting Interval Score\n", - " # -------------------------------------------------------------\n", + " # plot Interval Score\n", " fig.add_trace(\n", " go.Scatter(\n", " y=df_is[time_ahead],\n", " mode=\"lines\",\n", - " name=\"Quantile{}\".format(qntl),\n", - " legendgroup=\"Quantile{}\".format(qntl),\n", - " line_color=list_c_is[count_qntl],\n", + " name=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " legendgroup=\"Quantile {}\".format(\n", + " str(quantiles[qntl]) + \"-\" + str(quantiles[-qntl - 1])\n", + " ),\n", + " line_color=list_color[qntl],\n", " showlegend=False,\n", " xaxis=\"x\",\n", " ),\n", @@ -2014,43 +762,41 @@ " col=1,\n", " )\n", "\n", - " count_qntl += 1\n", - "\n", - "######################################################################\n", - "# plotting median prediction\n", - "######################################################################\n", + "\"\"\"\n", + "plot median prediction\n", + "\"\"\"\n", "fig.add_trace(\n", " go.Scatter(\n", " y=prediction_median,\n", " mode=\"lines\",\n", - " name=\"Median\",\n", - " legendgroup=\"Median\",\n", - " line=dict(color=\"black\", width=1, dash=\"dot\"),\n", + " name=\"Median Prediction\",\n", + " legendgroup=\"Median Prediction\",\n", + " line=dict(color=color_inner, width=2, dash=\"dot\"),\n", " xaxis=\"x\",\n", " ),\n", " row=1,\n", " col=1,\n", ")\n", "\n", - "######################################################################\n", - "# plotting measured target\n", - "######################################################################\n", + "\"\"\"\n", + "plot measured target\n", + "\"\"\"\n", "fig.add_trace(\n", " go.Scatter(\n", " y=measured[time_ahead],\n", " mode=\"lines\",\n", " name=\"Target\",\n", " legendgroup=\"Target\",\n", - " line=dict(color=c_target, width=1),\n", + " line=dict(color=color_actual, width=0.5),\n", " xaxis=\"x\",\n", " ),\n", " row=1,\n", " col=1,\n", ")\n", "\n", - "######################################################################\n", - "# plot setting\n", - "######################################################################\n", + "\"\"\"\n", + "configure plot\n", + "\"\"\"\n", "fig.update_layout(\n", " width=width_graphic,\n", " height=500,\n", @@ -2060,7 +806,7 @@ " t=30,\n", " b=0,\n", " ),\n", - " plot_bgcolor=\"rgb(245,245,245)\",\n", + " plot_bgcolor=color_background,\n", " legend=dict(\n", " orientation=\"h\",\n", " yanchor=\"bottom\",\n", @@ -2073,12 +819,9 @@ " ),\n", " ),\n", ")\n", - "\n", "fig.update_xaxes(\n", " title=dict(\n", - " text=\"Appended Multiple Time Windows
(single window size = {})\".format(\n", - " configs[\"sequential_splicer\"][\"window_width\"]\n", - " ),\n", + " text=\"Time ({})\".format(timestamporder),\n", " font=dict(\n", " size=14,\n", " family=font_family,\n", @@ -2088,7 +831,6 @@ " row=4,\n", " col=1,\n", ")\n", - "\n", "fig.update_yaxes(\n", " title=dict(\n", " text=\"Power
Consumption\",\n", @@ -2102,7 +844,6 @@ " row=1,\n", " col=1,\n", ")\n", - "\n", "fig.update_yaxes(\n", " title=dict(\n", " text=\"Penalty
Low\",\n", @@ -2115,7 +856,6 @@ " row=2,\n", " col=1,\n", ")\n", - "\n", "fig.update_yaxes(\n", " title=dict(\n", " text=\"Penalty
High\",\n", @@ -2128,7 +868,6 @@ " row=3,\n", " col=1,\n", ")\n", - "\n", "fig.update_yaxes(\n", " title=dict(\n", " text=\"Interval
Score\",\n", @@ -2141,23 +880,15728 @@ " row=4,\n", " col=1,\n", ")\n", - "\n", "fig.update_xaxes(\n", " showgrid=False,\n", ")\n", - "\n", "fig.update_yaxes(\n", " showgrid=False,\n", ")\n", "\n", - "######################################################################\n", - "# saving plot to file\n", - "######################################################################\n", - "timeseries_comparison = configs[\"exp_dir\"] + \"/Vis_TimeseriesComparisons.svg\"\n", + "\"\"\"\n", + "save plot image\n", + "\"\"\"\n", + "timeseries_comparison = (\n", + " configs[\"data_output\"][\"exp_dir\"] + \"/Vis_TimeseriesComparisons.svg\"\n", + ")\n", "print(\"saving timeseries comparison in {}\".format(timeseries_comparison))\n", "pio.write_image(fig, timeseries_comparison)\n", - "fig.write_html(configs[\"exp_dir\"] + \"/Vis_TimeseriesComparisons.html\")\n", - "\n", + "fig.write_html(\n", + " configs[\"data_output\"][\"exp_dir\"] + \"/Vis_TimeseriesComparisons.html\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "legendgroup": "Quantile 0.025-0.975", + "line": { + "color": "rgb(240.0, 240.0, 240.0)", + "width": 1 + }, + "mode": "lines", + "name": "Quantile 0.025-0.975", + "opacity": 0.1, + "showlegend": false, + "type": "scatter", + "xaxis": "x", + "y": [ + 96.98994445800781, + 96.61771392822266, + 96.48614501953125, + 96.65275573730469, + 96.38947296142578, + 95.62471771240234, + 94.12928009033203, + 92.28254699707031, + 89.67858123779297, + 88.12464141845703, + 87.84395599365234, + 86.99910736083984, + 86.62944030761719, + 87.1032943725586, + 86.20556640625, + 83.42256164550781, + 78.62445068359375, + 77.43229675292969, + 76.4266586303711, + 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"2bfe003d37062d72c9e551942138ec384c46465b11693bd5c6ee9c58d8160be9" +content-hash = "9eef32b1a232f726cba5297d8c275cf6bfca1032835a9cb4571a5b34bb0694ef" diff --git a/pyproject.toml b/pyproject.toml index d0059f80..53ce7c95 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,16 @@ [tool.poetry] name = "wattile" -version = "0.1.0" -description = "" +version = "0.2.0" +description = """\ +Probabilistic Deep Learning-based Forecasting of Building Energy ConsumptionProbabilistic \ +Deep Learning-based Forecasting of Building Energy Consumption.\ +""" authors = ["Your Name "] +repository = "https://github.com/NREL/Wattile" [tool.poetry.dependencies] python = "^3.8.0" pandas = "^1.4.0" -requests = "^2.21.0" scipy = "^1.9" tables = "^3.7.0" seaborn = "^0.11.2" @@ -16,7 +19,7 @@ psutil = "^5.8.0" matplotlib = "^3.0" torch = "^1.9.0" tensorboard = "^2.8.0" -Pillow = "^9.0.0" +Pillow = "^10.0.1" numpy = "^1.22" plotly = "^5.8.2" kaleido = "0.2.1" @@ -28,7 +31,7 @@ xarray = "^2022.10.0" pytest = "^6.2.5" pytest-cov = "^3.0.0" pre-commit = "^3.0.4" -black = "^22.3.0" +black = "^24.3.0" isort = "^5.11.5" pyflakes = "2.4.*" pylama = "8.2.*" diff --git a/tests/fixtures/bravo_exp_dir/QQ_data_Train.h5 b/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/QQ_data.h5 similarity index 81% rename from tests/fixtures/bravo_exp_dir/QQ_data_Train.h5 rename to tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/QQ_data.h5 index 00c39c59..8f64515b 100644 Binary files a/tests/fixtures/bravo_exp_dir/QQ_data_Train.h5 and b/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/QQ_data.h5 differ diff --git a/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/Vis_TimeseriesComparisons.svg b/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/Vis_TimeseriesComparisons.svg new file mode 100644 index 00000000..898d440d --- /dev/null +++ b/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/Vis_TimeseriesComparisons.svg @@ -0,0 +1 @@ +809010000.510204060010002000Quantile0Quantile1MedianTargetAppended Multiple Time Windows(single window size = 3h)PowerConsumptionPenaltyLowPenaltyHighIntervalScore \ No newline at end of file diff --git a/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/configs.json b/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/configs.json new file mode 100644 index 00000000..43ff0228 --- /dev/null +++ b/tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S/configs.json @@ -0,0 +1,96 @@ +{ + "data_input": { + "data_dir": "tests/data/Synthetic Site", + "data_config": "Synthetic Site Config.json", + "start_time": "2018-01-01T00:00:00-07:00", + "end_time": "2022-01-01T00:00:00-07:00", + "predictor_columns": [ + "Synthetic Weather Station Dew Point Temperature", + "Synthetic Weather Station Diffuse Horizontal Irradiance", + "Synthetic Weather Station Direct Normal Irradiance", + "Synthetic Weather Station Dry Bulb Temperature", + "Synthetic Weather Station Global Horizontal Irradiance", + "Synthetic Weather Station Relative Humidity", + "Synthetic Weather Station Wind Speed" + ], + "target_var": "Synthetic Site Electricity Main Total Power P0DT0H0M0S" + }, + "data_output": { + "exp_dir": "tests/fixtures/alfa_ensemble_exp_dir/P0DT0H0M0S", + "plot_comparison": true, + "plot_comparison_portion_start": 0.0, + "plot_comparison_portion_end": 1.0 + }, + "data_processing": { + "feat_time": { + "month_of_year": [ + "sincos" + ], + "day_of_week": [ + "binary_reg" + ], + "hour_of_day": [ + "sincos" + ], + "holidays": false + }, + "resample": { + "bin_interval": "15min", + "bin_closed": "right", + "bin_label": "right" + }, + "feat_stats": { + "active": true, + "window_width": "15min" + }, + "feat_timelag": { + "lag_interval": "15min", + "lag_count": 12 + }, + "input_output_window": { + "window_width_source": "180min", + "window_width_futurecast": "0 days 00:00:00", + "window_width_target": "60min" + }, + "random_seed": 1, + "sequential_splicer": { + "active": true, + "window_width": "3h" + }, + "data_split": "80:10:10", + "train_size_factor": 4 + }, + "learning_algorithm": { 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No newline at end of file diff --git a/tests/fixtures/alfa_ensemble_exp_dir/P0DT1H0M0S/QQ_data.h5 b/tests/fixtures/alfa_ensemble_exp_dir/P0DT1H0M0S/QQ_data.h5 new file mode 100644 index 00000000..7ae31bdd Binary files /dev/null and b/tests/fixtures/alfa_ensemble_exp_dir/P0DT1H0M0S/QQ_data.h5 differ diff --git a/tests/fixtures/alfa_ensemble_exp_dir/P0DT1H0M0S/configs.json b/tests/fixtures/alfa_ensemble_exp_dir/P0DT1H0M0S/configs.json new file mode 100644 index 00000000..2a0d6c8f --- /dev/null +++ b/tests/fixtures/alfa_ensemble_exp_dir/P0DT1H0M0S/configs.json @@ -0,0 +1,96 @@ +{ + "data_input": { + "data_dir": "tests/data/Synthetic Site", + "data_config": "Synthetic Site Config.json", + "start_time": "2018-01-01T00:00:00-07:00", + "end_time": "2022-01-01T00:00:00-07:00", + "predictor_columns": [ + "Synthetic Weather Station Dew Point Temperature", + "Synthetic Weather Station Diffuse Horizontal Irradiance", + "Synthetic Weather Station Direct Normal Irradiance", + "Synthetic Weather Station Dry Bulb Temperature", + "Synthetic Weather Station Global Horizontal Irradiance", + "Synthetic Weather Station Relative Humidity", + "Synthetic Weather Station Wind Speed" + ], + "target_var": "Synthetic Site Electricity Main Total Power P0DT1H0M0S" + }, + "data_output": { + "exp_dir": "tests/fixtures/alfa_ensemble_exp_dir/P0DT1H0M0S", + "plot_comparison": true, + "plot_comparison_portion_start": 0.0, + "plot_comparison_portion_end": 1.0 + }, + "data_processing": { + "feat_time": { + "month_of_year": [ + "sincos" + ], + "day_of_week": [ + "binary_reg" + ], + "hour_of_day": [ + "sincos" + ], + "holidays": false + }, + "resample": { + "bin_interval": "15min", + "bin_closed": "right", + "bin_label": "right" + }, + "feat_stats": { + "active": true, + "window_width": "15min" + }, + "feat_timelag": { + "lag_interval": "15min", + "lag_count": 12 + }, + "input_output_window": { + "window_width_source": "180min", + "window_width_futurecast": "0 days 01:00:00", + "window_width_target": 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No newline at end of file diff --git a/tests/fixtures/alfa_ensemble_exp_dir/internal_test.h5 b/tests/fixtures/alfa_ensemble_exp_dir/internal_test.h5 new file mode 100644 index 00000000..be5d37e9 Binary files /dev/null and b/tests/fixtures/alfa_ensemble_exp_dir/internal_test.h5 differ diff --git a/tests/fixtures/alfa_ensemble_exp_dir/mask.h5 b/tests/fixtures/alfa_ensemble_exp_dir/mask.h5 new file mode 100644 index 00000000..0b96d892 Binary files /dev/null and b/tests/fixtures/alfa_ensemble_exp_dir/mask.h5 differ diff --git a/tests/fixtures/alfa_ensemble_exp_dir/predictors_target_config.json b/tests/fixtures/alfa_ensemble_exp_dir/predictors_target_config.json new file mode 100644 index 00000000..6766d524 --- /dev/null +++ b/tests/fixtures/alfa_ensemble_exp_dir/predictors_target_config.json @@ -0,0 +1 @@ +{"predictors": [{"id": "r:278f8943-6a199bd7 Synthetic Weather Station Dew Point Temperature", "dis": "Synthetic Weather Station Dew Point Temperature", "column": "Synthetic Weather Station Dew Point Temperature", "unit": "°F"}, {"id": "r:278f79c0-03da5abc Synthetic Weather Station Diffuse Horizontal Irradiance", "dis": "Synthetic Weather Station Diffuse Horizontal Irradiance", "column": "Synthetic Weather Station Diffuse Horizontal Irradiance", "unit": "W/m²_irr"}, {"id": "r:278f79c0-4fe536b4 Synthetic Weather Station Direct Normal Irradiance", "dis": "Synthetic Weather Station Direct Normal Irradiance", "column": "Synthetic Weather Station Direct Normal Irradiance", "unit": "W/m²_irr"}, {"id": "r:278f79c0-8a8f26b0 Synthetic Weather Station Dry Bulb Temperature", "dis": "Synthetic Weather Station Dry Bulb Temperature", "column": "Synthetic Weather Station Dry Bulb Temperature", "unit": "°F"}, {"id": "r:278f79c0-8d722bdb Synthetic Weather Station Global Horizontal Irradiance", "dis": "Synthetic Weather Station Global Horizontal Irradiance", "column": "Synthetic Weather Station Global Horizontal Irradiance", "unit": "W/m²_irr"}, {"id": "r:278f79c0-e7db4305 Synthetic Weather Station Relative Humidity", "dis": "Synthetic Weather Station Relative Humidity", "column": "Synthetic Weather Station Relative Humidity", "unit": "%RH"}, {"id": "r:278f79c0-c1f48e5c Synthetic Weather Station Wind Speed", "dis": "Synthetic Weather Station Wind Speed", "column": "Synthetic Weather Station Wind Speed", "unit": "mph"}], "target": {"id": "r:294fd256-a17bb5c7 Synthetic Site Electricity Main Total Power", "dis": "Synthetic Site Electricity Main Total Power", "column": "Synthetic Site Electricity Main Total Power", "unit": "kW"}} \ No newline at end of file diff --git a/tests/fixtures/alfa_exp_dir/configs.json b/tests/fixtures/alfa_exp_dir/configs.json index a2d084a9..f4963b84 100644 --- a/tests/fixtures/alfa_exp_dir/configs.json +++ b/tests/fixtures/alfa_exp_dir/configs.json @@ -95,6 +95,5 @@ }, "target_feat_name": [ "Synthetic Site Electricity Main Total Power" - ], - "input_dim": 54 + ] } \ No newline at end of file diff --git a/tests/fixtures/alfa_exp_dir/output.out b/tests/fixtures/alfa_exp_dir/output.out deleted file mode 100644 index f57d110f..00000000 --- a/tests/fixtures/alfa_exp_dir/output.out +++ /dev/null @@ -1,34 +0,0 @@ -11/14/2022 12:24:47 - INFO - PID: 12848 -11/14/2022 12:24:47 - INFO - Pre-process: reading input data summary json file from tests\data\Synthetic Site\Synthetic Site Config.json -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-01.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-02.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-03.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-04.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-05.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-06.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-07.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-01.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-02.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-03.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-04.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-05.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-06.csv and added to data ... -11/14/2022 12:24:47 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-07.csv and added to data ... -11/14/2022 12:24:47 - INFO - Number of features: 54 -11/14/2022 12:24:47 - INFO - training with arch version alfa -11/14/2022 12:24:47 - INFO - Writer path: exp_dir -11/14/2022 12:24:47 - INFO - Data transformed using minmaxscale as transformation method -11/14/2022 12:24:47 - INFO - Available train batch factors: [1, 2, 3, 4, 6, 8, 11, 12, 16, 22, 24, 33, 44, 48, 66, 88, 132, 176, 264, 528] -11/14/2022 12:24:47 - INFO - Requested number of batches per epoch - Train: 5, val: 1 -11/14/2022 12:24:47 - INFO - Actual number of batches per epoch - Train: 4, val: 1 -11/14/2022 12:24:47 - INFO - Number of data samples in each batch - Train: 132, val: 66 -11/14/2022 12:24:47 - INFO - Training on cpu -11/14/2022 12:24:47 - INFO - Data converted to iterable dataset -11/14/2022 12:24:47 - INFO - A new lstm RNN model instantiated -11/14/2022 12:24:47 - INFO - Number of cores available: 6 -11/14/2022 12:24:47 - INFO - Number of logical processors available: 12 -11/14/2022 12:24:47 - INFO - Initial memory statistics (GB): {'total': 16.958697472, 'available': 3.629596672, 'percent': 78.6, 'used': 13.3291008, 'free': 3.629596672} -11/14/2022 12:24:47 - INFO - Starting to train the model for 1 epochs! -11/14/2022 12:24:48 - INFO - Epoch: 1 Iteration: 2. Train_loss: 0.16277313232421875. val_loss: 0.14126934687564657, LR: 0.001 -11/14/2022 12:24:49 - INFO - Epoch: 1 Iteration: 4. Train_loss: 0.11445854604244232. val_loss: 0.09202949188009601, LR: 0.001 -11/14/2022 12:24:54 - INFO - Run with arch RNN(lstm), on Synthetic Site Electricity Main Total Power is done! diff --git a/tests/fixtures/bravo_exp_dir/configs.json b/tests/fixtures/bravo_exp_dir/configs.json index 071a8e1f..92f43fd3 100644 --- a/tests/fixtures/bravo_exp_dir/configs.json +++ b/tests/fixtures/bravo_exp_dir/configs.json @@ -16,7 +16,7 @@ "target_var": "Synthetic Site Electricity Main Total Power" }, "data_output": { - "exp_dir": "exp_dir", + "exp_dir": "tests/fixtures/bravo_exp_dir", "plot_comparison": true, "plot_comparison_portion_start": 0.0, "plot_comparison_portion_end": 1.0 @@ -50,7 +50,7 @@ "input_output_window": { "window_width_source": "180min", "window_width_futurecast": "0min", - "window_width_target": "1080min" + "window_width_target": "60min" }, "random_seed": 1, "sequential_splicer": { @@ -93,7 +93,6 @@ "eval_frequency": 2, "test_method": "external" }, - "input_dim": 54, "target_feat_name": [ "Synthetic Site Electricity Main Total Power" ] diff --git a/tests/fixtures/bravo_exp_dir/error_stats_train.json b/tests/fixtures/bravo_exp_dir/error_stats_train.json deleted file mode 100644 index 634e051e..00000000 --- a/tests/fixtures/bravo_exp_dir/error_stats_train.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "pinball_loss": "0.17709919384300352", - "rmse": "34.07272260202674", - "nmbe": "20.65424954944272", - "cvrmse": "2.7463964522811413", - "gof": "14.733307807884781", - "qs": "14.777059878169908", - "ace": "1.3940639269406392", - "is": "641.5678095319695", - "train_time": "2.8257178000000005" -} \ No newline at end of file diff --git a/tests/fixtures/bravo_exp_dir/internal_test.h5 b/tests/fixtures/bravo_exp_dir/internal_test.h5 index 71d15650..7ee5a922 100644 Binary files a/tests/fixtures/bravo_exp_dir/internal_test.h5 and b/tests/fixtures/bravo_exp_dir/internal_test.h5 differ diff --git a/tests/fixtures/bravo_exp_dir/mask.h5 b/tests/fixtures/bravo_exp_dir/mask.h5 index 413e4010..1b8f6d60 100644 Binary files a/tests/fixtures/bravo_exp_dir/mask.h5 and b/tests/fixtures/bravo_exp_dir/mask.h5 differ diff --git a/tests/fixtures/bravo_exp_dir/measured.h5 b/tests/fixtures/bravo_exp_dir/measured.h5 index 651933c0..45f8d664 100644 Binary files a/tests/fixtures/bravo_exp_dir/measured.h5 and b/tests/fixtures/bravo_exp_dir/measured.h5 differ diff --git a/tests/fixtures/bravo_exp_dir/mid_train_error_stats.h5 b/tests/fixtures/bravo_exp_dir/mid_train_error_stats.h5 index 346a8d05..a7148073 100644 Binary files a/tests/fixtures/bravo_exp_dir/mid_train_error_stats.h5 and b/tests/fixtures/bravo_exp_dir/mid_train_error_stats.h5 differ diff --git a/tests/fixtures/bravo_exp_dir/output.out b/tests/fixtures/bravo_exp_dir/output.out deleted file mode 100644 index 39118a73..00000000 --- a/tests/fixtures/bravo_exp_dir/output.out +++ /dev/null @@ -1,34 +0,0 @@ -11/14/2022 12:26:10 - INFO - PID: 13236 -11/14/2022 12:26:10 - INFO - Pre-process: reading input data summary json file from tests\data\Synthetic Site\Synthetic Site Config.json -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-01.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-02.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-03.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-04.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-05.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-06.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Predictors 2021-12-07.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-01.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-02.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-03.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-04.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-05.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-06.csv and added to data ... -11/14/2022 12:26:10 - INFO - Read tests\data\Synthetic Site/Synthetic Site Targets 2021-12-07.csv and added to data ... -11/14/2022 12:26:10 - INFO - Number of features: 54 -11/14/2022 12:26:11 - INFO - training with arch version bravo -11/14/2022 12:26:11 - INFO - Writer path: exp_dir -11/14/2022 12:26:11 - INFO - Data transformed using minmaxscale as transformation method -11/14/2022 12:26:11 - INFO - Available train batch factors: [1, 2, 3, 4, 6, 8, 9, 12, 16, 18, 24, 27, 36, 48, 54, 72, 108, 144, 216, 432] -11/14/2022 12:26:11 - INFO - Requested number of batches per epoch - Train: 5, val: 1 -11/14/2022 12:26:11 - INFO - Actual number of batches per epoch - Train: 4, val: 1 -11/14/2022 12:26:11 - INFO - Number of data samples in each batch - Train: 108, val: 72 -11/14/2022 12:26:11 - INFO - Training on cpu -11/14/2022 12:26:11 - INFO - Data converted to iterable dataset -11/14/2022 12:26:11 - INFO - A new lstm RNN model instantiated -11/14/2022 12:26:11 - INFO - Number of cores available: 6 -11/14/2022 12:26:11 - INFO - Number of logical processors available: 12 -11/14/2022 12:26:11 - INFO - Initial memory statistics (GB): {'total': 16.958697472, 'available': 3.578318848, 'percent': 78.9, 'used': 13.380378624, 'free': 3.578318848} -11/14/2022 12:26:11 - INFO - Starting to train the model for 1 epochs! -11/14/2022 12:26:12 - INFO - Epoch: 1 Iteration: 2. Train_loss: 0.2155463993549347. val_loss: 0.1947556639425443, LR: 0.001 -11/14/2022 12:26:13 - INFO - Epoch: 1 Iteration: 4. Train_loss: 0.19397549331188202. val_loss: 0.17709919384300352, LR: 0.001 -11/14/2022 12:26:16 - INFO - Run with arch RNN(lstm), on Synthetic Site Electricity Main Total Power is done! diff --git a/tests/fixtures/bravo_exp_dir/predictions.h5 b/tests/fixtures/bravo_exp_dir/predictions.h5 index 7f28789c..b759d7dc 100644 Binary files a/tests/fixtures/bravo_exp_dir/predictions.h5 and b/tests/fixtures/bravo_exp_dir/predictions.h5 differ diff --git a/tests/fixtures/bravo_exp_dir/predictors_target_config.json b/tests/fixtures/bravo_exp_dir/predictors_target_config.json index 91a94ce8..6766d524 100644 --- a/tests/fixtures/bravo_exp_dir/predictors_target_config.json +++ b/tests/fixtures/bravo_exp_dir/predictors_target_config.json @@ -1 +1 @@ -{"predictors": [{"site": "Synthetic Site", "column": "Synthetic Weather Station Dew Point Temperature", "id": "r:278f8943-6a199bd7 Synthetic Weather Station Dew Point Temperature", "description": "Synthetic Weather Station Dew Point Temperature", "unit": "°F", "pv": false}, {"site": "Synthetic Site", "column": "Synthetic Weather Station Diffuse Horizontal Irradiance", "id": "r:278f79c0-03da5abc Synthetic Weather Station Diffuse Horizontal Irradiance", "description": "Synthetic Weather Station Diffuse Horizontal Irradiance", "unit": "W/m²_irr", "pv": false}, {"site": "Synthetic Site", "column": "Synthetic Weather Station Direct Normal Irradiance", "id": "r:278f79c0-4fe536b4 Synthetic Weather Station Direct Normal Irradiance", "description": "Synthetic Weather Station Direct Normal Irradiance", "unit": "W/m²_irr", "pv": false}, {"site": "Synthetic Site", "column": "Synthetic Weather Station Dry Bulb Temperature", "id": "r:278f79c0-8a8f26b0 Synthetic Weather Station Dry Bulb Temperature", "description": "Synthetic Weather Station Dry Bulb Temperature", "unit": "°F", "pv": false}, {"site": "Synthetic Site", "column": "Synthetic Weather Station Global Horizontal Irradiance", "id": "r:278f79c0-8d722bdb Synthetic Weather Station Global Horizontal Irradiance", "description": "Synthetic Weather Station Global Horizontal Irradiance", "unit": "W/m²_irr", "pv": false}, {"site": "Synthetic Site", "column": "Synthetic Weather Station Relative Humidity", "id": "r:278f79c0-e7db4305 Synthetic Weather Station Relative Humidity", "description": "Synthetic Weather Station Relative Humidity", "unit": "%RH", "pv": false}, {"site": "Synthetic Site", "column": "Synthetic Weather Station Wind Speed", "id": "r:278f79c0-c1f48e5c Synthetic Weather Station Wind Speed", "description": "Synthetic Weather Station Wind Speed", "unit": "mph", "pv": false}], "target": {"site": "Synthetic Site", "column": "Synthetic Site Electricity Main Total Power", "id": "r:294fd256-a17bb5c7 Synthetic Site Electricity Main Total Power", "description": "Synthetic Site Electricity Main Total Power", "unit": "kW", "pv": false}} \ No newline at end of file +{"predictors": [{"id": "r:278f8943-6a199bd7 Synthetic Weather Station Dew Point Temperature", "dis": "Synthetic Weather Station Dew Point Temperature", "column": "Synthetic Weather Station Dew Point Temperature", "unit": "°F"}, {"id": "r:278f79c0-03da5abc Synthetic Weather Station Diffuse Horizontal Irradiance", "dis": "Synthetic Weather Station Diffuse Horizontal Irradiance", "column": "Synthetic Weather Station Diffuse Horizontal Irradiance", "unit": "W/m²_irr"}, {"id": "r:278f79c0-4fe536b4 Synthetic Weather Station Direct Normal Irradiance", "dis": "Synthetic Weather Station Direct Normal Irradiance", "column": "Synthetic Weather Station Direct Normal Irradiance", "unit": "W/m²_irr"}, {"id": "r:278f79c0-8a8f26b0 Synthetic Weather Station Dry Bulb Temperature", "dis": "Synthetic Weather Station Dry Bulb Temperature", "column": "Synthetic Weather Station Dry Bulb Temperature", "unit": "°F"}, {"id": "r:278f79c0-8d722bdb Synthetic Weather Station Global Horizontal Irradiance", "dis": "Synthetic Weather Station Global Horizontal Irradiance", "column": "Synthetic Weather Station Global Horizontal Irradiance", "unit": "W/m²_irr"}, {"id": "r:278f79c0-e7db4305 Synthetic Weather Station Relative Humidity", "dis": "Synthetic Weather Station Relative Humidity", "column": "Synthetic Weather Station Relative Humidity", "unit": "%RH"}, {"id": "r:278f79c0-c1f48e5c Synthetic Weather Station Wind Speed", "dis": "Synthetic Weather Station Wind Speed", "column": "Synthetic Weather Station Wind Speed", "unit": "mph"}], "target": {"id": "r:294fd256-a17bb5c7 Synthetic Site Electricity Main Total Power", "dis": "Synthetic Site Electricity Main Total Power", "column": "Synthetic Site Electricity Main Total Power", "unit": "kW"}} \ No newline at end of file diff --git a/tests/fixtures/bravo_exp_dir/torch_model b/tests/fixtures/bravo_exp_dir/torch_model index 9b8a94b1..439041a5 100644 Binary files a/tests/fixtures/bravo_exp_dir/torch_model and b/tests/fixtures/bravo_exp_dir/torch_model differ diff --git a/tests/fixtures/bravo_exp_dir/train_stats.json b/tests/fixtures/bravo_exp_dir/train_stats.json index 8a4474f8..900d76a8 100644 --- a/tests/fixtures/bravo_exp_dir/train_stats.json +++ b/tests/fixtures/bravo_exp_dir/train_stats.json @@ -1 +1 @@ -{"train_max": {"Synthetic Weather Station Dew Point Temperature_min_lag12": 28.99165916442871, "Synthetic Weather Station Diffuse Horizontal Irradiance_min_lag12": 242.17230224609375, "Synthetic Weather Station Direct Normal Irradiance_min_lag12": 992.8291015625, "Synthetic Weather Station Dry Bulb Temperature_min_lag12": 70.66400146484375, "Synthetic Weather Station Global Horizontal Irradiance_min_lag12": 509.4621887207031, "Synthetic Weather Station Relative Humidity_min_lag12": 73.66999816894531, "Synthetic Weather Station Wind Speed_min_lag12": 15.659008979797363, "sin_HOD_min_lag12": 0.9981347918510437, "cos_HOD_min_lag12": 0.9981347918510437, "DOW_binary_reg_0_min_lag12": 1.0, "DOW_binary_reg_1_min_lag12": 1.0, "DOW_binary_reg_2_min_lag12": 1.0, "DOW_binary_reg_3_min_lag12": 1.0, "DOW_binary_reg_4_min_lag12": 1.0, "DOW_binary_reg_5_min_lag12": 1.0, "DOW_binary_reg_6_min_lag12": 1.0, "sin_MOY_min_lag12": -0.40148797631263733, "cos_MOY_min_lag12": 0.9158642888069153, "Synthetic Weather Station Dew Point Temperature_max_lag12": 29.094993591308594, "Synthetic Weather Station Diffuse Horizontal Irradiance_max_lag12": 271.58660888671875, "Synthetic Weather Station Direct Normal Irradiance_max_lag12": 999.15380859375, "Synthetic Weather Station Dry Bulb Temperature_max_lag12": 71.18599700927734, "Synthetic Weather Station Global Horizontal Irradiance_max_lag12": 563.1962280273438, "Synthetic Weather Station Relative Humidity_max_lag12": 74.95999908447266, "Synthetic Weather Station Wind Speed_max_lag12": 27.716445922851562, "sin_HOD_max_lag12": 1.0, "cos_HOD_max_lag12": 1.0, "DOW_binary_reg_0_max_lag12": 1.0, "DOW_binary_reg_1_max_lag12": 1.0, "DOW_binary_reg_2_max_lag12": 1.0, "DOW_binary_reg_3_max_lag12": 1.0, "DOW_binary_reg_4_max_lag12": 1.0, "DOW_binary_reg_5_max_lag12": 1.0, "DOW_binary_reg_6_max_lag12": 1.0, "sin_MOY_max_lag12": -0.40148797631263733, "cos_MOY_max_lag12": 0.9158642888069153, "Synthetic Weather Station Dew Point Temperature_mean_lag12": 29.037389755249023, "Synthetic Weather Station Diffuse Horizontal Irradiance_mean_lag12": 253.07908630371094, "Synthetic Weather Station Direct Normal Irradiance_mean_lag12": 995.92724609375, "Synthetic Weather Station Dry Bulb Temperature_mean_lag12": 70.97000122070312, "Synthetic Weather Station Global Horizontal Irradiance_mean_lag12": 510.6767578125, "Synthetic Weather Station Relative Humidity_mean_lag12": 74.2526626586914, "Synthetic Weather Station Wind Speed_mean_lag12": 19.215543746948242, "sin_HOD_mean_lag12": 0.9993559718132019, "cos_HOD_mean_lag12": 0.9993559718132019, "DOW_binary_reg_0_mean_lag12": 1.0, "DOW_binary_reg_1_mean_lag12": 1.0, "DOW_binary_reg_2_mean_lag12": 1.0, "DOW_binary_reg_3_mean_lag12": 1.0, "DOW_binary_reg_4_mean_lag12": 1.0, "DOW_binary_reg_5_mean_lag12": 1.0, "DOW_binary_reg_6_mean_lag12": 1.0, "sin_MOY_mean_lag12": -0.40148797631263733, "cos_MOY_mean_lag12": 0.9158642888069153, "Synthetic Weather Station Dew Point Temperature_min_lag11": 28.99165916442871, "Synthetic Weather Station Diffuse Horizontal Irradiance_min_lag11": 242.17230224609375, "Synthetic Weather Station Direct Normal Irradiance_min_lag11": 992.8291015625, "Synthetic Weather Station Dry Bulb Temperature_min_lag11": 70.66400146484375, "Synthetic Weather Station Global Horizontal Irradiance_min_lag11": 509.4621887207031, "Synthetic Weather Station Relative Humidity_min_lag11": 73.66999816894531, "Synthetic Weather Station Wind Speed_min_lag11": 15.547159194946289, "sin_HOD_min_lag11": 0.9981347918510437, "cos_HOD_min_lag11": 0.9981347918510437, "DOW_binary_reg_0_min_lag11": 1.0, "DOW_binary_reg_1_min_lag11": 1.0, "DOW_binary_reg_2_min_lag11": 1.0, "DOW_binary_reg_3_min_lag11": 1.0, "DOW_binary_reg_4_min_lag11": 1.0, 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0.3594762980937958, "DOW_binary_reg_1_max_lag1": 0.3821374773979187, "DOW_binary_reg_2_max_lag1": 0.3030737340450287, "DOW_binary_reg_3_max_lag1": 0.17417415976524353, "DOW_binary_reg_4_max_lag1": 0.3533072769641876, "DOW_binary_reg_5_max_lag1": 0.40197300910949707, "DOW_binary_reg_6_max_lag1": 0.40197300910949707, "sin_MOY_max_lag1": 0.027740351855754852, "cos_MOY_max_lag1": 0.013631402514874935, "Synthetic Weather Station Dew Point Temperature_mean_lag1": 6.691331386566162, "Synthetic Weather Station Diffuse Horizontal Irradiance_mean_lag1": 52.91423416137695, "Synthetic Weather Station Direct Normal Irradiance_mean_lag1": 367.5687561035156, "Synthetic Weather Station Dry Bulb Temperature_mean_lag1": 14.428771018981934, "Synthetic Weather Station Global Horizontal Irradiance_mean_lag1": 171.36984252929688, "Synthetic Weather Station Relative Humidity_mean_lag1": 17.284452438354492, "Synthetic Weather Station Wind Speed_mean_lag1": 4.753302097320557, "sin_HOD_mean_lag1": 0.7111253142356873, "cos_HOD_mean_lag1": 0.7009095549583435, "DOW_binary_reg_0_mean_lag1": 0.357080340385437, "DOW_binary_reg_1_mean_lag1": 0.380021870136261, "DOW_binary_reg_2_mean_lag1": 0.29988011717796326, "DOW_binary_reg_3_mean_lag1": 0.1684524416923523, "DOW_binary_reg_4_mean_lag1": 0.3529837429523468, "DOW_binary_reg_5_mean_lag1": 0.4000927805900574, "DOW_binary_reg_6_mean_lag1": 0.4000927805900574, "sin_MOY_mean_lag1": 0.027723006904125214, "cos_MOY_mean_lag1": 0.013632397167384624, "Synthetic Weather Station Dew Point Temperature_min": 6.732498666635224, "Synthetic Weather Station Diffuse Horizontal Irradiance_min": 49.11314113653023, "Synthetic Weather Station Direct Normal Irradiance_min": 347.3000829844978, "Synthetic Weather Station Dry Bulb Temperature_min": 14.383149445152231, "Synthetic Weather Station Global Horizontal Irradiance_min": 160.70234079050607, "Synthetic Weather Station Relative Humidity_min": 17.124062825555814, "Synthetic Weather Station Wind Speed_min": 3.9497467079561086, "sin_HOD_min": 0.7132638837767367, "cos_HOD_min": 0.6987273706669342, "DOW_binary_reg_0_min": 0.35538774927006933, "DOW_binary_reg_1_min": 0.37856976386123503, "DOW_binary_reg_2_min": 0.2975120097808045, "DOW_binary_reg_3_min": 0.17417416235164648, "DOW_binary_reg_4_min": 0.3490717385238274, "DOW_binary_reg_5_min": 0.3988446693705666, "DOW_binary_reg_6_min": 0.39884466937056673, "sin_MOY_min": 0.02774035014147635, "cos_MOY_min": 0.013631398160143292, "Synthetic Weather Station Dew Point Temperature_max": 6.653709121063828, "Synthetic Weather Station Diffuse Horizontal Irradiance_max": 56.941288714937336, "Synthetic Weather Station Direct Normal Irradiance_max": 390.56831009000973, "Synthetic Weather Station Dry Bulb Temperature_max": 14.53958779026865, "Synthetic Weather Station Global Horizontal Irradiance_max": 185.96890445724446, "Synthetic Weather Station Relative Humidity_max": 17.484148991699435, "Synthetic Weather Station Wind Speed_max": 5.848032966948242, "sin_HOD_max": 0.7146370934269453, "cos_HOD_max": 0.6967753111233818, "DOW_binary_reg_0_max": 0.3594762969548521, "DOW_binary_reg_1_max": 0.38213747995172526, "DOW_binary_reg_2_max": 0.3030737339978304, "DOW_binary_reg_3_max": 0.17969277113233137, "DOW_binary_reg_4_max": 0.3512020812565244, "DOW_binary_reg_5_max": 0.4019729959736067, "DOW_binary_reg_6_max": 0.40197299597360664, "sin_MOY_max": 0.027754006051991062, "cos_MOY_max": 0.013627981202853688, "Synthetic Weather Station Dew Point Temperature_mean": 6.680209921766896, "Synthetic Weather Station Diffuse Horizontal Irradiance_mean": 52.77115121430973, "Synthetic Weather Station Direct Normal Irradiance_mean": 369.2282295411966, "Synthetic Weather Station Dry Bulb Temperature_mean": 14.447333219299733, "Synthetic Weather Station Global Horizontal Irradiance_mean": 173.3616232455638, "Synthetic Weather Station Relative Humidity_mean": 17.283252093281458, "Synthetic Weather Station Wind Speed_mean": 4.746260228067803, "sin_HOD_mean": 0.7140906280607736, "cos_HOD_mean": 0.6978882810718451, "DOW_binary_reg_0_mean": 0.35708035374462616, "DOW_binary_reg_1_mean": 0.38002185289201634, "DOW_binary_reg_2_mean": 0.299880127601311, "DOW_binary_reg_3_mean": 0.1741757715997147, "DOW_binary_reg_4_mean": 0.35087578623556653, "DOW_binary_reg_5_mean": 0.4000927751375111, "DOW_binary_reg_6_mean": 0.4000927751375111, "sin_MOY_mean": 0.027737931959274934, "cos_MOY_mean": 0.013629578666101884, "Synthetic Site Electricity Main Total Power P0DT0H0M0S": 14.49136214609743, "Synthetic Site Electricity Main Total Power P0DT0H15M0S": 14.3928700099465, "Synthetic Site Electricity Main Total Power P0DT0H30M0S": 14.415758461649064, "Synthetic Site Electricity Main Total Power P0DT0H45M0S": 14.451257436550135, "Synthetic Site Electricity Main Total Power P0DT1H0M0S": 14.480988798005614}} \ No newline at end of file diff --git a/tests/fixtures/test_configs.json b/tests/fixtures/test_configs.json index 552b7cd8..ba0298fd 100644 --- a/tests/fixtures/test_configs.json +++ b/tests/fixtures/test_configs.json @@ -44,7 +44,7 @@ "input_output_window": { "window_width_source": "180min", "window_width_futurecast":"0min", - "window_width_target": "1080min" + "window_width_target": "60min" }, "random_seed": 1, "sequential_splicer": { diff --git a/tests/integration_tests/test_prediction.py b/tests/integration_tests/test_prediction.py index 912032bf..25712066 100644 --- a/tests/integration_tests/test_prediction.py +++ b/tests/integration_tests/test_prediction.py @@ -6,9 +6,9 @@ import pytest import xarray as xr -from wattile.buildings_processing import prep_for_rnn +from wattile.data_processing import prep_for_rnn from wattile.data_reading import read_dataset_from_file -from wattile.models import AlfaModel, BravoModel +from wattile.models import AlfaEnsembleModel, AlfaModel, BravoModel TESTS_PATH = pathlib.Path(__file__).parents[1] TESTS_FIXTURES_PATH = TESTS_PATH / "fixtures" @@ -141,4 +141,49 @@ def test_prediction_bravo(config_for_tests, tmpdir): window_width_futurecast + bin_interval * i for i in range(num_lagged_targets) ] - assert (exp_dir / "output.out").exists() + +def test_prediction_alfa_ensemble(config_for_tests, tmpdir): + # don't run training + config_for_tests["learning_algorithm"]["arch_version"] = "alfa_ensemble" + config_for_tests["learning_algorithm"]["use_case"] = "prediction" + + # use a temp result dir + exp_dir = pathlib.Path(tmpdir) / "train_results" + config_for_tests["data_output"]["exp_dir"] = str(exp_dir) + shutil.copytree(TESTS_FIXTURES_PATH / "alfa_ensemble_exp_dir", exp_dir) + + # create a temp data dir + config_for_tests["data_input"]["data_dir"] = str(tmpdir / "data") + config_for_tests["data_input"]["data_config"] = "Synthetic Site Config.json" + data_dir = tmpdir / "data" + popluate_test_data_dir_with_prediction_data(data_dir) + + # ACTION + data = read_dataset_from_file(config_for_tests) + _, val_df = prep_for_rnn(config_for_tests, data) + model = AlfaEnsembleModel(config_for_tests) + results = model.predict(val_df) + + # ASSERTION + cdp = config_for_tests["data_processing"] + window_width_futurecast = cdp["input_output_window"]["window_width_futurecast"] + window_width_target = pd.Timedelta( + cdp["input_output_window"]["window_width_target"] + ) + bin_interval = pd.Timedelta(cdp["resample"]["bin_interval"]) + quantiles = config_for_tests["learning_algorithm"]["quantiles"] + + num_lagged_targets = int(window_width_target / bin_interval) + 1 + + # assert results is the right shape + assert isinstance(results, xr.DataArray) + assert results.shape == (val_df.shape[0], len(quantiles), num_lagged_targets) + assert results.dims == ("timestamp", "quantile", "horizon") + + # assert results is indexed correctly + timestamp, quantile, horizon = results.indexes.values() + assert timestamp.to_list() == val_df.index.to_list() + assert quantile.to_list() == quantiles + assert horizon.to_list() == [ + window_width_futurecast + bin_interval * i for i in range(num_lagged_targets) + ] diff --git a/tests/integration_tests/test_training.py b/tests/integration_tests/test_training.py index 803e112b..312454a7 100644 --- a/tests/integration_tests/test_training.py +++ b/tests/integration_tests/test_training.py @@ -3,7 +3,7 @@ import pytest -from wattile.buildings_processing import prep_for_rnn +from wattile.data_processing import prep_for_rnn from wattile.data_reading import read_dataset_from_file from wattile.models import ModelFactory @@ -52,7 +52,6 @@ def config_for_tests(): "arch_version": "bravo", "transformation_method": "standard", } -ARCH_VERSION_CHARLIE_CONFIG_PATCH = {"arch_version": "charlie"} @pytest.mark.parametrize( @@ -64,7 +63,6 @@ def config_for_tests(): ARCH_VERSION_BRAVO_RNN_CONFIG_PATCH, ARCH_VERSION_BRAVO_LSTM_CONFIG_PATCH, ARCH_VERSION_BRAVO_STANDARD_TRANSFORMATION_CONFIG_PATCH, - ARCH_VERSION_CHARLIE_CONFIG_PATCH, ], ) def test_model_trains(config_for_tests, tmpdir, config_patch): @@ -72,7 +70,7 @@ def test_model_trains(config_for_tests, tmpdir, config_patch): Run training and verify results are made. """ # patch configs and create temporary, unquie output file - config_for_tests.update(config_patch) + config_for_tests["learning_algorithm"].update(config_patch) exp_dir = pathlib.Path(tmpdir) / "train_results" config_for_tests["data_output"]["exp_dir"] = str(exp_dir) @@ -86,3 +84,25 @@ def test_model_trains(config_for_tests, tmpdir, config_patch): assert (exp_dir / "torch_model").exists() if config_for_tests["learning_algorithm"]["arch_version"] != "charlie": assert (exp_dir / "train_stats.json").exists() + + +def test_ensemble_model_trains(config_for_tests, tmpdir): + """ + Run training and verify results are made. + """ + # patch configs and create temporary, unquie output file + config_for_tests["learning_algorithm"]["arch_version"] = "alfa_ensemble" + exp_dir = pathlib.Path(tmpdir) / "train_results" + exp_dir.mkdir() + config_for_tests["data_output"]["exp_dir"] = str(exp_dir) + + # train model + data = read_dataset_from_file(config_for_tests) + train_df, val_df = prep_for_rnn(config_for_tests, data) + model = ModelFactory.create_model(config_for_tests) + model.train(train_df, val_df) + + # check result file were created + for target_lag in model.alfa_models_by_target_lag.keys(): + assert (exp_dir / target_lag.isoformat() / "torch_model").exists() + assert (exp_dir / target_lag.isoformat() / "train_stats.json").exists() diff --git a/tests/integration_tests/test_validation.py b/tests/integration_tests/test_validation.py index 5846a85a..69a373c0 100644 --- a/tests/integration_tests/test_validation.py +++ b/tests/integration_tests/test_validation.py @@ -4,7 +4,7 @@ import pytest -from wattile.buildings_processing import prep_for_rnn +from wattile.data_processing import prep_for_rnn from wattile.data_reading import read_dataset_from_file from wattile.models import ModelFactory @@ -55,6 +55,23 @@ def test_validation(config_for_tests, tmpdir, config_patch, test_exp_dir): _, val_df = prep_for_rnn(config_for_tests, data) model.validate(val_df) - assert (exp_dir / "output.out").exists() assert (exp_dir / "error_stats_test.json").exists() assert (exp_dir / "QQ_data_Test.h5").exists() + + +def test_ensemble_validation(config_for_tests, tmpdir): + config_for_tests["learning_algorithm"]["arch_version"] = "alfa_ensemble" + config_for_tests["learning_algorithm"]["use_case"] = "validation" + + exp_dir = pathlib.Path(tmpdir) / "train_results" + config_for_tests["data_output"]["exp_dir"] = str(exp_dir) + shutil.copytree(TESTS_FIXTURES_PATH / "alfa_ensemble_exp_dir", exp_dir) + + data = read_dataset_from_file(config_for_tests) + _, val_df = prep_for_rnn(config_for_tests, data) + model = ModelFactory.create_model(config_for_tests) + model.validate(val_df) + + for target_lag in model.alfa_models_by_target_lag.keys(): + assert (exp_dir / target_lag.isoformat() / "error_stats_test.json").exists() + assert (exp_dir / target_lag.isoformat() / "QQ_data_Test.h5").exists() diff --git a/tests/unit_tests/models/test_alfa_ensemble.py b/tests/unit_tests/models/test_alfa_ensemble.py new file mode 100644 index 00000000..c1afd902 --- /dev/null +++ b/tests/unit_tests/models/test_alfa_ensemble.py @@ -0,0 +1,25 @@ +import pandas as pd +from pandas.testing import assert_frame_equal + +from wattile.models.alfa_ensemble_model import AlfaEnsembleModelDataBuilder + + +def test_alfa_ensemble_model_data_builder(): + # Set Up + target_var = "target_var" + time_lags = [i * pd.Timedelta(hours=1) for i in range(1, 5)] + + target_columns = [f"{target_var} {time_lag.isoformat()}" for time_lag in time_lags] + predictor_columns = ["pred_1, pred_2, pred_3"] + index = pd.date_range("2000-01-01 00:00:00", "2000-01-01 12:00:00", freq="H") + data = pd.DataFrame(index=index, columns=predictor_columns + target_columns) + + aedb = AlfaEnsembleModelDataBuilder(data, target_var) + + # Assertion + for time_lag in time_lags: + target_column = f"{target_var} {time_lag.isoformat()}" + assert_frame_equal( + aedb.get_dataset(time_lag), + pd.DataFrame(index=index, columns=predictor_columns + [target_column]), + ) diff --git a/tests/unit_tests/models/test_alfa_model.py b/tests/unit_tests/models/test_alfa_model.py index 52d13549..963a89fe 100644 --- a/tests/unit_tests/models/test_alfa_model.py +++ b/tests/unit_tests/models/test_alfa_model.py @@ -3,7 +3,7 @@ import pandas as pd import pytest -from wattile.buildings_processing import _preprocess_data +from wattile.data_processing import _preprocess_data from wattile.models import AlfaModel diff --git a/tests/unit_tests/models/test_bravo.py b/tests/unit_tests/models/test_bravo.py index 2744585b..5faf7ce4 100644 --- a/tests/unit_tests/models/test_bravo.py +++ b/tests/unit_tests/models/test_bravo.py @@ -3,7 +3,7 @@ import pandas as pd import pytest -from wattile.buildings_processing import _preprocess_data +from wattile.data_processing import _preprocess_data from wattile.models import BravoModel diff --git a/tests/unit_tests/test_building_processing.py b/tests/unit_tests/test_building_processing.py index 7d362b4a..207810fe 100644 --- a/tests/unit_tests/test_building_processing.py +++ b/tests/unit_tests/test_building_processing.py @@ -7,7 +7,7 @@ from numpy.testing import assert_array_equal from pandas.testing import assert_frame_equal, assert_index_equal, assert_series_equal -from wattile.buildings_processing import ( +from wattile.data_processing import ( correct_predictor_columns, correct_timestamps, resample_data, @@ -406,7 +406,9 @@ def test_timelag_predictors_target(configs): # assert columns are correct lag_columns = [f"var_1_lag{i}" for i in range(lag_count, 0, -1)] num_target_lags = int(window_width_target / bin_interval) + 1 - target_columns = [f"target_var_lag_{i}" for i in range(num_target_lags)] + target_columns = [ + f"target_var {(i * bin_interval).isoformat()}" for i in range(num_target_lags) + ] assert list(output.columns) == lag_columns + ["var_1"] + target_columns # assert indices are correct @@ -436,16 +438,13 @@ def test_timelag_predictors_target(configs): assert_series_equal(output["var_1"], input["var_1"][output.index]) # assert lagged target_vars are right - num_lagged_targets = int(window_width_target / bin_interval) - for i in range(num_lagged_targets + 1): - target_var_with_lag = f"target_var_lag_{i}" - + for target_column in target_columns: input_target_var_with_lag = input["target_var"].copy() - lag = window_width_futurecast + bin_interval * i - input_target_var_with_lag.index -= lag + lag = pd.Timedelta(target_column[11:]) # lag on target column + input_target_var_with_lag.index -= window_width_futurecast + lag assert_series_equal( - output[target_var_with_lag], + output[target_column], input_target_var_with_lag[output.index], check_names=False, ) diff --git a/tests/unit_tests/test_prep_for_rnn.py b/tests/unit_tests/test_prep_for_rnn.py index 1bb63150..73eb4d36 100644 --- a/tests/unit_tests/test_prep_for_rnn.py +++ b/tests/unit_tests/test_prep_for_rnn.py @@ -4,7 +4,7 @@ import pandas as pd import pytest -from wattile.buildings_processing import prep_for_rnn +from wattile.data_processing import prep_for_rnn from wattile.data_reading import read_dataset_from_file TESTS_PATH = pathlib.Path(__file__).parents[1] diff --git a/wattile/buildings_processing.py b/wattile/data_processing.py similarity index 93% rename from wattile/buildings_processing.py rename to wattile/data_processing.py index e98b978c..6d329c69 100644 --- a/wattile/buildings_processing.py +++ b/wattile/data_processing.py @@ -120,7 +120,11 @@ def input_data_split(data, configs): mask_file = os.path.join(file_prefix, "mask.h5") # assign timestamp and data size depending on arch_version - if (arch_version == "alfa") | (arch_version == "bravo"): + if ( + arch_version == "alfa" + or arch_version == "bravo" + or arch_version == "alfa_ensemble" + ): timestamp = data.index data_size = data.shape[0] @@ -131,7 +135,11 @@ def input_data_split(data, configs): msk = _create_split_mask(timestamp, data_size, configs) # assign train, validation, and test data - if (arch_version == "alfa") | (arch_version == "bravo"): + if ( + arch_version == "alfa" + or arch_version == "bravo" + or arch_version == "alfa_ensemble" + ): train_df = data[msk == 0] val_df = data[msk == 1] test_df = data[msk == 2] @@ -190,8 +198,9 @@ def build_lagging_predictors(predictors, lag_count, lag_interval): def build_lagging_targets(target, number_of_lags, bin_interval, target_var): lagging_target = pd.DataFrame() for i in range(0, number_of_lags): - lagging_target["{}_lag_{}".format(target_var, i)] = target.shift( - freq=(-i * bin_interval) + shift_by = i * bin_interval + lagging_target[f"{target_var} {shift_by.isoformat()}"] = target.shift( + freq=-shift_by ) return lagging_target @@ -273,7 +282,8 @@ def timelag_predictors_target(data, configs): ) else: - columns = ["{}_lag_{}".format(target_var, i) for i in range(0, number_of_lags)] + lags = [i * bin_interval for i in range(0, number_of_lags)] + columns = [f"{target_var} {lag.isoformat()}" for lag in lags] lagging_target = pd.DataFrame( 0, index=lagging_predictors.index, columns=columns ) # dummy @@ -488,16 +498,17 @@ def _preprocess_data(configs, data): data = resample_data(data, configs) # Add lag features - configs["input_dim"] = data.shape[1] - 1 - logger.info("Number of features: {}".format(configs["input_dim"])) logger.debug("Features: {}".format(data.columns.values)) - if configs["learning_algorithm"]["arch_version"] == "alfa": + arch_version = configs["learning_algorithm"]["arch_version"] + if arch_version == "alfa": data = timelag_predictors(data, configs) - elif configs["learning_algorithm"]["arch_version"] == "bravo": + elif arch_version == "bravo" or arch_version == "alfa_ensemble": data = timelag_predictors_target(data, configs) - elif configs["learning_algorithm"]["arch_version"] == "charlie": + elif arch_version == "charlie": data = roll_predictors_target(data, configs) + else: + ConfigsError(f"{arch_version} not a valid arch_version") return data @@ -547,8 +558,7 @@ def roll_data(data, configs): ) data_resample_min = data_resampler.min().add_suffix("_min") data_resample_max = data_resampler.max().add_suffix("_max") - data_resample_sum = data_resampler.sum().add_suffix("_sum") - data_resample_count = data_resampler.count().add_suffix("_count") + data_resample_mean = data_resampler.mean().add_suffix("_mean") # adding rolling window statistics: minimum mins = data_resample_min.rolling(window=window_width, min_periods=1).min() @@ -556,19 +566,7 @@ def roll_data(data, configs): # adding rolling window statistics: maximum maxs = data_resample_max.rolling(window=window_width, min_periods=1).max() - # adding rolling window statistics: sum - sums = data_resample_sum.rolling(window=window_width, min_periods=1).sum() - - # adding rolling window statistics: count - counts = data_resample_count.rolling( - window=window_width, min_periods=1 - ).sum() # this has to be sum for proper count calculation - - # adding rolling window statistics: mean - means = sums.copy() - means.columns = means.columns.str.replace("_sum", "_mean") - np.seterr(invalid="ignore") # supress/hide the warning - means.loc[:, :] = sums.values / counts.values + means = data_resample_mean.rolling(window=window_width, min_periods=1).mean() # combining min and max stats data = pd.concat([mins, maxs, means], axis=1) diff --git a/wattile/entry_point.py b/wattile/entry_point.py index 7f20572e..5a595df5 100644 --- a/wattile/entry_point.py +++ b/wattile/entry_point.py @@ -5,7 +5,7 @@ import pandas as pd -import wattile.buildings_processing as bp +import wattile.data_processing as bp from wattile.data_reading import read_dataset_from_file from wattile.models import ModelFactory diff --git a/wattile/models/__init__.py b/wattile/models/__init__.py index 0eac8aa7..30ba7852 100644 --- a/wattile/models/__init__.py +++ b/wattile/models/__init__.py @@ -1,24 +1,28 @@ from wattile.error import ConfigsError +from wattile.models.alfa_ensemble_model import AlfaEnsembleModel from wattile.models.alfa_model import AlfaModel -from wattile.models.AlgoMainRNNBase import AlgoMainRNNBase +from wattile.models.base_model import BaseModel from wattile.models.bravo_model import BravoModel class ModelFactory: @staticmethod - def create_model(configs: dict) -> AlgoMainRNNBase: + def create_model(configs: dict) -> BaseModel: """create model :param configs: configs :type configs: dict :raises ConfigsError: if arch versions is invalid :return: new model - :rtype: AlgoMainRNNBase + :rtype: BaseModel """ arch_version = configs["learning_algorithm"]["arch_version"] if arch_version == "alfa": return AlfaModel(configs) + if arch_version == "alfa_ensemble": + return AlfaEnsembleModel(configs) + elif arch_version == "bravo": return BravoModel(configs) diff --git a/wattile/models/alfa_ensemble_model.py b/wattile/models/alfa_ensemble_model.py new file mode 100644 index 00000000..af0fcccf --- /dev/null +++ b/wattile/models/alfa_ensemble_model.py @@ -0,0 +1,126 @@ +from copy import deepcopy +from pathlib import Path + +import pandas as pd +import xarray as xr + +from wattile.models.alfa_model import AlfaModel +from wattile.models.base_model import BaseModel + + +class AlfaEnsembleModelDataBuilder: + """From bravo like inputs, get_dataset returns alfa like inputs for a spefic target lag""" + + def __init__(self, data_frame: pd.DataFrame, target_var: str): + """Seperate target and predictors""" + # find target column names + self.target_var = target_var + target_column_names = [ + c for c in data_frame.columns if c.startswith(target_var) + ] + + # seperate predictors and targets + self.target_df = data_frame[target_column_names] + self.predictors_df = data_frame.drop(target_column_names, axis=1) + + def get_dataset(self, target_lag: pd.Timedelta): + target_column_name = f"{self.target_var} {target_lag.isoformat()}" + target_column = self.target_df[target_column_name] + + dataset = self.predictors_df.copy() + dataset[target_column_name] = target_column + + return dataset + + +class AlfaEnsembleModel(BaseModel): + """A collection of alfa models, one for each target lag in the multi horizon config.""" + + def __init__(self, configs): + """Create an alfa model for each lagged target with approiate configs""" + self.configs = configs + + # get items from config + cdp = configs["data_processing"] + bin_interval = pd.Timedelta(cdp["resample"]["bin_interval"]) + window_width_target = pd.Timedelta( + cdp["input_output_window"]["window_width_target"] + ) + + # compute target_lags + num_target_lags = int(window_width_target // bin_interval) + 1 + target_lags = [bin_interval * i for i in range(num_target_lags)] + + # create alfa models + self.alfa_models_by_target_lag = { + target_lag: AlfaModel(self._configs_for_alfa_model(configs, target_lag)) + for target_lag in target_lags + } + + def __del__(self): + for model in self.alfa_models_by_target_lag.values(): + model.writer.flush() + model.writer.close() + + def _configs_for_alfa_model(self, base_configs, target_lag): + """Edit base configs to have custom futurecast and expdir according to taraget lag.""" + alfa_model_configs = deepcopy(base_configs) + + # update arch version + alfa_model_configs["learning_algorithm"]["arch_version"] = "alfa" + + # put exp_dir one folder deeper + alfa_model_configs["data_output"]["exp_dir"] = str( + Path(base_configs["data_output"]["exp_dir"]) / target_lag.isoformat() + ) + + # add target lag to furturecast + window_width_futurecast = alfa_model_configs["data_processing"][ + "input_output_window" + ]["window_width_futurecast"] + alfa_model_configs["data_processing"]["input_output_window"][ + "window_width_futurecast" + ] = str(window_width_futurecast + target_lag) + + # set target var to reflect lag + target_var = alfa_model_configs["data_input"]["target_var"] + alfa_model_configs["data_input"][ + "target_var" + ] = f"{target_var} {target_lag.isoformat()}" + + return alfa_model_configs + + def train(self, train_df: pd.DataFrame, val_df: pd.DataFrame) -> None: + """Train all alfa models""" + target_var = self.configs["data_input"]["target_var"] + train_data_builder = AlfaEnsembleModelDataBuilder(train_df, target_var) + val_data_builder = AlfaEnsembleModelDataBuilder(val_df, target_var) + + for target_lag, model in self.alfa_models_by_target_lag.items(): + train_data = train_data_builder.get_dataset(target_lag=target_lag) + val_data = val_data_builder.get_dataset(target_lag=target_lag) + + model.train(train_data, val_data) + + def validate(self, val_df: pd.DataFrame) -> None: + """validate all alfa models""" + target_var = self.configs["data_input"]["target_var"] + val_data_builder = AlfaEnsembleModelDataBuilder(val_df, target_var) + + for target_lag, model in self.alfa_models_by_target_lag.items(): + val_data = val_data_builder.get_dataset(target_lag=target_lag) + + model.validate(val_data) + + def predict(self, val_df: pd.DataFrame) -> None: + """predict all alfa models""" + target_var = self.configs["data_input"]["target_var"] + val_data_builder = AlfaEnsembleModelDataBuilder(val_df, target_var) + + results = [] + for target_lag, model in self.alfa_models_by_target_lag.items(): + val_data = val_data_builder.get_dataset(target_lag=target_lag) + + results.append(model.predict(val_data)) + + return xr.concat(results, dim="horizon") diff --git a/wattile/models/alfa_model.py b/wattile/models/alfa_model.py index 30b59d97..e53a262b 100644 --- a/wattile/models/alfa_model.py +++ b/wattile/models/alfa_model.py @@ -17,13 +17,13 @@ from torch.utils.data import DataLoader, TensorDataset from wattile.error import ConfigsError -from wattile.models.AlgoMainRNNBase import AlgoMainRNNBase +from wattile.models.base_model import BaseModel from wattile.models.utils import init_model, load_model, save_model logger = logging.getLogger(str(os.getpid())) -class AlfaModel(AlgoMainRNNBase): +class AlfaModel(BaseModel): def to_data_loader(self, data, batch_size, shuffle): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -97,7 +97,6 @@ def test_processing( val_loader, model, seq_dim, - input_dim, transformation_method, last_run, device, @@ -109,7 +108,6 @@ def test_processing( :param val_loader: (DataLoader) :param model: (Pytorch model) :param seq_dim: () - :param input_dim: :param transformation_method: :return: """ @@ -121,7 +119,7 @@ def test_processing( preds = [] targets = [] for i, (feats, values) in enumerate(val_loader): - features = Variable(feats.view(-1, seq_dim, input_dim)) + features = Variable(feats.view(-1, seq_dim, model.input_dim)) outputs = model(features) preds.append(outputs.cpu().numpy()) targets.append(values.cpu().numpy()) @@ -308,7 +306,6 @@ def run_training( # noqa: C901 TODO: remove no qa weight_decay = float( self.configs["learning_algorithm"]["optimizer_config"]["weight_decay"] ) - input_dim = self.configs["input_dim"] transformation_method = self.configs["learning_algorithm"][ "transformation_method" ] @@ -473,7 +470,7 @@ def run_training( # noqa: C901 TODO: remove no qa time1 = timeit.default_timer() # (batches, timesteps, features) - features = Variable(feats.view(-1, seq_dim, input_dim)) + features = Variable(feats.view(-1, seq_dim, model.input_dim)) target = Variable(values) # size: batch size time2 = timeit.default_timer() @@ -557,7 +554,6 @@ def run_training( # noqa: C901 TODO: remove no qa val_loader, model, seq_dim, - input_dim, transformation_method, False, device, @@ -631,7 +627,6 @@ def run_training( # noqa: C901 TODO: remove no qa val_loader, model, seq_dim, - input_dim, transformation_method, True, device, @@ -738,7 +733,6 @@ def run_validation( val_loader, model, seq_dim, - self.configs["input_dim"], transformation_method, True, device, @@ -807,7 +801,7 @@ def run_prediction( with torch.no_grad(): preds = [] for (feats, v) in val_loader: - features = Variable(feats.view(-1, seq_dim, self.configs["input_dim"])) + features = Variable(feats.view(-1, seq_dim, model.input_dim)) outputs = model(features) outputs = outputs.cpu().numpy() outputs = outputs.reshape(*outputs.shape, 1) diff --git a/wattile/models/AlgoMainRNNBase.py b/wattile/models/base_model.py similarity index 99% rename from wattile/models/AlgoMainRNNBase.py rename to wattile/models/base_model.py index 845e7763..6d71ec50 100644 --- a/wattile/models/AlgoMainRNNBase.py +++ b/wattile/models/base_model.py @@ -16,7 +16,7 @@ logger = logging.getLogger(str(os.getpid())) -class AlgoMainRNNBase(ABC): +class BaseModel(ABC): def __init__(self, configs): self.configs = configs diff --git a/wattile/models/bravo_model.py b/wattile/models/bravo_model.py index 0cee015d..b0864488 100644 --- a/wattile/models/bravo_model.py +++ b/wattile/models/bravo_model.py @@ -17,13 +17,13 @@ from torch.utils.data import DataLoader, TensorDataset from wattile.error import ConfigsError -from wattile.models.AlgoMainRNNBase import AlgoMainRNNBase +from wattile.models.base_model import BaseModel from wattile.models.utils import init_model, load_model, save_model logger = logging.getLogger(str(os.getpid())) -class BravoModel(AlgoMainRNNBase): +class BravoModel(BaseModel): def to_data_loader(self, data, batch_size, shuffle): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -121,7 +121,6 @@ def test_processing( # noqa: C901 TODO: remove noqa val_loader, model, seq_dim, - input_dim, transformation_method, last_run, device, @@ -133,7 +132,6 @@ def test_processing( # noqa: C901 TODO: remove noqa :param val_loader: (DataLoader) :param model: (Pytorch model) :param seq_dim: () - :param input_dim: :param transformation_method: :return: """ @@ -150,7 +148,7 @@ def test_processing( # noqa: C901 TODO: remove noqa preds = [] targets = [] for i, (feats, values) in enumerate(val_loader): - features = Variable(feats.view(-1, seq_dim, input_dim)) + features = Variable(feats.view(-1, seq_dim, model.input_dim)) outputs = model(features) # preds.append(outputs.data.numpy()) # targets.append(values.data.numpy()) @@ -372,7 +370,6 @@ def run_training( # noqa: C901 TODO: remove noqa weight_decay = float( self.configs["learning_algorithm"]["optimizer_config"]["weight_decay"] ) - input_dim = self.configs["input_dim"] window_width_target = self.configs["data_processing"]["input_output_window"][ "window_width_target" ] @@ -382,10 +379,6 @@ def run_training( # noqa: C901 TODO: remove noqa ] seq_dim = self.configs["data_processing"]["feat_timelag"]["lag_count"] + 1 - initial_num = ( - pd.Timedelta(window_width_target) // pd.Timedelta(bin_interval) - ) + 1 - # Write the configurations used for this training process to a json file path = os.path.join(self.file_prefix, "configs.json") with open(path, "w") as fp: @@ -545,7 +538,7 @@ def run_training( # noqa: C901 TODO: remove noqa time1 = timeit.default_timer() # (batches, timesteps, features) - features = Variable(feats.view(-1, seq_dim, input_dim)) + features = Variable(feats.view(-1, seq_dim, model.input_dim)) target = Variable(values) # size: batch size time2 = timeit.default_timer() @@ -623,7 +616,6 @@ def run_training( # noqa: C901 TODO: remove noqa val_loader, model, seq_dim, - input_dim, transformation_method, False, device, @@ -644,17 +636,19 @@ def run_training( # noqa: C901 TODO: remove noqa # Add parody plot to TensorBoard fig1, ax1 = plt.subplots() - for lag in range(initial_num): + initial_num = ( + pd.Timedelta(window_width_target) // pd.Timedelta(bin_interval) + ) + 1 + lags = [i * pd.Timedelta(bin_interval) for i in range(initial_num)] + for i, lag in enumerate(lags): ax1.scatter( predictions[ :, - lag + i * len(self.configs["learning_algorithm"]["quantiles"]), ], val_df[ - self.configs["data_input"]["target_var"] - + "_lag_" - + str(lag) + f"{self.configs['data_input']['target_var']} {lag.isoformat()}" ], s=5, alpha=0.3, @@ -722,7 +716,6 @@ def run_training( # noqa: C901 TODO: remove noqa val_loader, model, seq_dim, - input_dim, transformation_method, True, device, @@ -824,7 +817,6 @@ def run_validation( val_loader, model, seq_dim, - self.configs["input_dim"], transformation_method, False, device, @@ -893,7 +885,7 @@ def run_prediction( with torch.no_grad(): preds = [] for (feats, v) in val_loader: - features = Variable(feats.view(-1, seq_dim, self.configs["input_dim"])) + features = Variable(feats.view(-1, seq_dim, model.input_dim)) outputs = model(features) preds.append(outputs.cpu().numpy()) diff --git a/wattile/models/charlie_model.py b/wattile/models/charlie_model.py index 7a403625..31ea28b7 100644 --- a/wattile/models/charlie_model.py +++ b/wattile/models/charlie_model.py @@ -461,7 +461,7 @@ def quantile_loss(output, target, qs, window_target_size): val_df_predictor = val_df["predictor"].astype(np.float32).copy() val_df_target = val_df["target"].astype(np.float32).copy() - input_dim = self.configs["input_dim"] = train_df_predictor.shape[-1] + input_dim = train_df_predictor.shape[-1] # call the respective model if attention_model == "none": diff --git a/wattile/models/ffnn.py b/wattile/models/ffnn.py index 2a6bdcc4..d6cf6157 100644 --- a/wattile/models/ffnn.py +++ b/wattile/models/ffnn.py @@ -4,7 +4,7 @@ class FeedforwardNeuralNetModel(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim): super(FeedforwardNeuralNetModel, self).__init__() - + self.input_dim = input_dim # Linear function 1: 14 -> 56 self.fc1 = nn.Linear(input_dim, hidden_dim) # initializing the weights with xavier weight initialization and bias as 0.01 diff --git a/wattile/models/lstm.py b/wattile/models/lstm.py index 41407bd5..59f7fd25 100644 --- a/wattile/models/lstm.py +++ b/wattile/models/lstm.py @@ -7,6 +7,7 @@ class LSTM_Model(nn.Module): def __init__(self, input_dim, hidden_dim, num_layers, output_dim, device): super(LSTM_Model, self).__init__() + self.input_dim = input_dim # Hidden Dimension self.hidden_dim = hidden_dim diff --git a/wattile/models/rnn.py b/wattile/models/rnn.py index a50ef376..9c4fbc91 100644 --- a/wattile/models/rnn.py +++ b/wattile/models/rnn.py @@ -6,6 +6,7 @@ class RNNModel(nn.Module): def __init__(self, input_dim, hidden_dim, num_layers, output_dim, device): super(RNNModel, self).__init__() + self.input_dim = input_dim # Hidden Dimension self.hidden_dim = hidden_dim diff --git a/wattile/models/utils.py b/wattile/models/utils.py index d480bf8e..f6902b63 100644 --- a/wattile/models/utils.py +++ b/wattile/models/utils.py @@ -18,6 +18,38 @@ def save_model(model, epoch_num, n_iter, filepath): ) +def get_input_dim(configs): + input_dim = len(configs["data_input"]["predictor_columns"]) + HOD_configs = configs["data_processing"]["feat_time"]["hour_of_day"] + day_of_week_configs = configs["data_processing"]["feat_time"]["day_of_week"] + + if "sincos" in HOD_configs: + input_dim += 2 + + if "binary_reg" in HOD_configs: + input_dim += 24 + + if "binary_fuzzy" in HOD_configs: + input_dim += 24 + + if "binary_reg" in day_of_week_configs: + input_dim += 7 + + if "binary_fuzzy" in day_of_week_configs: + input_dim += 7 + + if "sincos" in configs["data_processing"]["feat_time"]["month_of_year"]: + input_dim += 2 + + if configs["data_processing"]["feat_time"]["holidays"]: + input_dim += 2 + + if configs["data_processing"]["feat_stats"]["active"]: + input_dim *= 3 + + return input_dim + + def _get_output_dim(configs): window_width_target = configs["data_processing"]["input_output_window"][ @@ -51,7 +83,7 @@ def init_model(configs): hidden_dim = int(configs["learning_algorithm"]["hidden_size"]) output_dim = _get_output_dim(configs) - input_dim = configs["input_dim"] + input_dim = get_input_dim(configs) num_layers = configs["learning_algorithm"]["num_layers"] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") diff --git a/wattile/visualization.py b/wattile/visualization.py index d2794feb..32250fdb 100644 --- a/wattile/visualization.py +++ b/wattile/visualization.py @@ -4,35 +4,66 @@ from plotly.colors import n_colors from plotly.subplots import make_subplots -################################################# -# Common settings for graphics -################################################# +""" +Common settings for graphics +""" font_family = "Raleway, PT Sans Narrow, Overpass, Arial" width_graphic = 1500 -color_base = "rgb(236,200,178)" -color_accent = "rgb(80,70,69)" +color_outer = "rgb(240,240,240)" +color_inner = "rgb(216,179,101)" +color_actual = "rgb(0,60,48)" +color_background = "rgb(255,255,255)" def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa - ###################################################################### - # reading measured and prediction files - ###################################################################### + """ + read measured and prediction files + """ predictions = pd.read_hdf(configs["data_output"]["exp_dir"] + "/predictions.h5") measured = pd.read_hdf(configs["data_output"]["exp_dir"] + "/measured.h5") - ###################################################################### - # filter data based on portion defined in configs.json - ###################################################################### - plot_comparison_portion_start = configs["data_output"][ - "plot_comparison_portion_start" - ] - plot_comparison_portion_end = configs["data_output"]["plot_comparison_portion_end"] + """ + read configs parameters + """ window_width_target = configs["data_processing"]["input_output_window"][ "window_width_target" ] bin_interval = configs["data_processing"]["resample"]["bin_interval"] initial_num = (pd.Timedelta(window_width_target) // pd.Timedelta(bin_interval)) + 1 + use_case = configs["learning_algorithm"]["use_case"] + quantiles = configs["learning_algorithm"]["quantiles"] + + """ + set colors + """ + len_predictions = len(predictions.columns) + len_colors = int((len(quantiles) - 1) / 2) + if len_colors == 1: + list_color = [color_outer] + else: + list_color = n_colors(color_outer, color_inner, len_colors, colortype="rgb") + + """ + initialize y-axis boundaries for finding min and max + """ + y_max = 0 + y_min = 99999999999999 + + """ + check timestamp order + """ + timestamporder = ( + "timestamp in sequence" if use_case == "validation" else "timestamp shuffled" + ) + + """ + filter data based on portion defined in configs + """ + plot_comparison_portion_start = configs["data_output"][ + "plot_comparison_portion_start" + ] + plot_comparison_portion_end = configs["data_output"]["plot_comparison_portion_end"] predictions = predictions.iloc[ int(predictions.shape[0] * plot_comparison_portion_start) : int( predictions.shape[0] * plot_comparison_portion_end @@ -46,31 +77,9 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa :, ] - ###################################################################### - # plot setting - ###################################################################### - len_tot = len(predictions.columns) - target = measured.to_numpy() - c_target = "rgb(228,26,28)" - len_c_list = int((len(configs["learning_algorithm"]["quantiles"]) - 1) / 2) - if len_c_list == 1: - c_list = [color_base] - else: - c_list = n_colors(color_base, color_accent, len_c_list, colortype="rgb") - list_c_low_penalty = c_list.copy() - list_c_high_penalty = c_list.copy() - list_c_is = c_list.copy() - list_c_is.reverse() - y_min = 10000000 - y_max = 0 - window_target_size = configs["data_processing"]["input_output_window"][ - "window_width_target" - ] - bin_interval = configs["data_processing"]["resample"]["bin_interval"] - - ###################################################################### - # initialize plotting area - ###################################################################### + """ + initialize plot + """ fig = make_subplots( rows=4, cols=1, @@ -79,39 +88,32 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa row_heights=[0.5, 0.15, 0.15, 0.15], ) - ###################################################################### - # if quantiles were only defined with median - ###################################################################### - if len(configs["learning_algorithm"]["quantiles"]) == 1: + """ + extract median prediction and, if necessary, plot prediction quantiles/errors + """ + if len(quantiles) == 1: - # ----------------------------------------------------------------- - # extracting median prediction - # ----------------------------------------------------------------- + # extract median prediction prediction_median = predictions.iloc[:, 0].values + # find min/max y-values if min(prediction_median.min(), measured.iloc[:, 0].min()) < y_min: y_min = min(prediction_median.min(), measured.iloc[:, 0].min()) - if max(prediction_median.max(), measured.iloc[:, 0].max()) > y_max: y_max = max(prediction_median.max(), measured.iloc[:, 0].max()) - ###################################################################### - # if quantiles were defined properly - ###################################################################### else: - # ----------------------------------------------------------------- - # extracting median prediction - # ----------------------------------------------------------------- + # extract median prediction if configs["learning_algorithm"]["arch_version"] == "alfa": - idx_col_med = len_c_list + idx_col_med = len_colors prediction_median = predictions.iloc[:, idx_col_med].values time_ahead = 0 elif configs["learning_algorithm"]["arch_version"] == "bravo": - idx_col_med_start = len_c_list * initial_num + idx_col_med_start = len_colors * initial_num idx_col_med_end = idx_col_med_start + initial_num prediction_median = ( predictions.iloc[:, idx_col_med_start:idx_col_med_end] @@ -122,9 +124,9 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa elif configs["learning_algorithm"]["arch_version"] == "charlie": window_target_size_count = window_target_size_count = int( - pd.Timedelta(window_target_size) / pd.Timedelta(bin_interval) + pd.Timedelta(window_width_target) / pd.Timedelta(bin_interval) ) - idx_col_med_start = len_c_list * window_target_size_count + idx_col_med_start = len_colors * window_target_size_count idx_col_med_end = idx_col_med_start + window_target_size_count prediction_median = ( predictions.iloc[:, idx_col_med_start:idx_col_med_end] @@ -132,138 +134,134 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa .values ) - # ----------------------------------------------------------------- - # extracting quantile predictions - # ----------------------------------------------------------------- - count_qntl = 0 - for qntl in range( - 0, int((len(configs["learning_algorithm"]["quantiles"]) - 1) / 2) - ): - - # ------------------------------------------------------------- - # filtering data for correct quantile - # ------------------------------------------------------------- + # extract quantile predictions + for qntl in range(0, int(len_colors)): + + # filter data for correct quantile if configs["learning_algorithm"]["arch_version"] == "alfa": idx_col_low_start = qntl - idx_col_high_start = len_tot - idx_col_low_start - 1 - - prediction_low = predictions.iloc[:, idx_col_low_start].to_numpy() - prediction_high = predictions.iloc[:, idx_col_high_start].to_numpy() + idx_col_high_start = len_predictions - idx_col_low_start - 1 + prediction_qntl_low = predictions.iloc[:, idx_col_low_start].to_numpy() + prediction_qntl_high = predictions.iloc[ + :, idx_col_high_start + ].to_numpy() elif configs["learning_algorithm"]["arch_version"] == "bravo": idx_col_low_start = qntl * initial_num idx_col_low_end = qntl * initial_num + initial_num - idx_col_high_start = (len_tot - idx_col_low_start) - initial_num - idx_col_high_end = len_tot - idx_col_low_start - - prediction_low = predictions.iloc[ + idx_col_high_start = (len_predictions - idx_col_low_start) - initial_num + idx_col_high_end = len_predictions - idx_col_low_start + prediction_qntl_low = predictions.iloc[ :, idx_col_low_start:idx_col_low_end ].to_numpy() - prediction_high = predictions.iloc[ + prediction_qntl_high = predictions.iloc[ :, idx_col_high_start:idx_col_high_end ].to_numpy() elif configs["learning_algorithm"]["arch_version"] == "charlie": window_target_size_count = int( - pd.Timedelta(window_target_size) / pd.Timedelta(bin_interval) + pd.Timedelta(window_width_target) / pd.Timedelta(bin_interval) ) - idx_col_low_start = qntl * window_target_size_count idx_col_low_end = ( qntl * window_target_size_count + window_target_size_count ) idx_col_high_start = ( - len_tot - idx_col_low_start + len_predictions - idx_col_low_start ) - window_target_size_count - idx_col_high_end = len_tot - idx_col_low_start - - prediction_low = predictions.iloc[ + idx_col_high_end = len_predictions - idx_col_low_start + prediction_qntl_low = predictions.iloc[ :, idx_col_low_start:idx_col_low_end ].to_numpy() - prediction_high = predictions.iloc[ + prediction_qntl_high = predictions.iloc[ :, idx_col_high_start:idx_col_high_end ].to_numpy() - # ------------------------------------------------------------- - # calculating Interval Score details - # ------------------------------------------------------------- - alph = 1 - ( - configs["learning_algorithm"]["quantiles"][-(qntl + 1)] - - configs["learning_algorithm"]["quantiles"][qntl] - ) + # calculate Interval Score details + alph = 1 - (quantiles[-(qntl + 1)] - quantiles[qntl]) IS = ( - (prediction_high - prediction_low) - + (2 / alph) * (prediction_low - target) * (target < prediction_low) - + (2 / alph) * (target - prediction_high) * (target > prediction_high) + (prediction_qntl_high - prediction_qntl_low) + + (2 / alph) + * (prediction_qntl_low - measured.to_numpy()) + * (measured.to_numpy() < prediction_qntl_low) + + (2 / alph) + * (measured.to_numpy() - prediction_qntl_high) + * (measured.to_numpy() > prediction_qntl_high) ) # (batches * time) for a single quantile - - df_prediction_low = pd.DataFrame(prediction_low) - df_prediction_high = pd.DataFrame(prediction_high) + prediction_qntl_low = pd.DataFrame(prediction_qntl_low) + prediction_qntl_high = pd.DataFrame(prediction_qntl_high) df_is = pd.DataFrame(IS) - df_viol_l = (df_prediction_low[time_ahead] - measured[time_ahead]) * ( - df_prediction_low[time_ahead] > measured[time_ahead] + # calculate upper/lower quantile violations + df_viol_l = (prediction_qntl_low[time_ahead] - measured[time_ahead]) * ( + prediction_qntl_low[time_ahead] > measured[time_ahead] ) - df_viol_u = (measured[time_ahead] - df_prediction_high[time_ahead]) * ( - measured[time_ahead] > df_prediction_high[time_ahead] + df_viol_u = (measured[time_ahead] - prediction_qntl_high[time_ahead]) * ( + measured[time_ahead] > prediction_qntl_high[time_ahead] ) - if df_prediction_low[time_ahead].min() < y_min: - y_min = df_prediction_low[time_ahead].min() - - if df_prediction_high[time_ahead].max() > y_max: - y_max = df_prediction_high[time_ahead].max() + # find min/max y values + if prediction_qntl_low[time_ahead].min() < y_min: + y_min = prediction_qntl_low[time_ahead].min() + if prediction_qntl_high[time_ahead].max() > y_max: + y_max = prediction_qntl_high[time_ahead].max() - # ------------------------------------------------------------- - # plotting prediction lower bound - # ------------------------------------------------------------- + # plot prediction quantile lower bound fig.add_trace( go.Scatter( - y=df_prediction_low[time_ahead], + y=prediction_qntl_low[time_ahead], mode="lines", - name="Quantile{}".format(qntl), - legendgroup="Quantile{}".format(qntl), - showlegend=True, + name="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + legendgroup="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + showlegend=False, xaxis="x", opacity=0.1, - line=dict(color=c_list[qntl], width=0), + line=dict(color=list_color[qntl], width=1), ), row=1, col=1, ) - # ------------------------------------------------------------- - # plotting prediction higher bound - # ------------------------------------------------------------- + # plot prediction quantile higher bound fig.add_trace( go.Scatter( - y=df_prediction_high[time_ahead], + y=prediction_qntl_high[time_ahead], mode="lines", - name="Quantile{}".format(qntl), - legendgroup="Quantile{}".format(qntl), - showlegend=False, + name="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + legendgroup="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + showlegend=True, xaxis="x", fill="tonexty", opacity=0.1, - line=dict(color=c_list[qntl], width=0), + line=dict(color=list_color[qntl], width=1), ), row=1, col=1, ) - # ------------------------------------------------------------- - # plotting prediction lower violation - # ------------------------------------------------------------- + # plot prediction quantile higher bound violation fig.add_trace( go.Scatter( y=df_viol_l, mode="lines", - name="Quantile{}".format(qntl), - legendgroup="Quantile{}".format(qntl), - line_color=list_c_low_penalty[count_qntl], + name="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + legendgroup="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + line_color=list_color[qntl], showlegend=False, xaxis="x", yaxis="y1", @@ -272,16 +270,18 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa col=1, ) - # ------------------------------------------------------------- - # plotting prediction higher violation - # ------------------------------------------------------------- + # plot prediction quantile higher bound violation fig.add_trace( go.Scatter( y=df_viol_u, mode="lines", - name="Quantile{}".format(qntl), - legendgroup="Quantile{}".format(qntl), - line_color=list_c_high_penalty[count_qntl], + name="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + legendgroup="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + line_color=list_color[qntl], showlegend=False, xaxis="x", yaxis="y1", @@ -290,16 +290,18 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa col=1, ) - # ------------------------------------------------------------- - # plotting Interval Score - # ------------------------------------------------------------- + # plot Interval Score fig.add_trace( go.Scatter( y=df_is[time_ahead], mode="lines", - name="Quantile{}".format(qntl), - legendgroup="Quantile{}".format(qntl), - line_color=list_c_is[count_qntl], + name="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + legendgroup="Quantile {}".format( + str(quantiles[qntl]) + "-" + str(quantiles[-qntl - 1]) + ), + line_color=list_color[qntl], showlegend=False, xaxis="x", ), @@ -307,43 +309,41 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa col=1, ) - count_qntl += 1 - - ###################################################################### - # plotting median prediction - ###################################################################### + """ + plot median prediction + """ fig.add_trace( go.Scatter( y=prediction_median, mode="lines", - name="Median", - legendgroup="Median", - line=dict(color="black", width=1, dash="dot"), + name="Median Prediction", + legendgroup="Median Prediction", + line=dict(color=color_inner, width=2, dash="dot"), xaxis="x", ), row=1, col=1, ) - ###################################################################### - # plotting measured target - ###################################################################### + """ + plot measured target + """ fig.add_trace( go.Scatter( y=measured[time_ahead], mode="lines", name="Target", legendgroup="Target", - line=dict(color=c_target, width=1), + line=dict(color=color_actual, width=0.5), xaxis="x", ), row=1, col=1, ) - ###################################################################### - # plot setting - ###################################################################### + """ + configure plot + """ fig.update_layout( width=width_graphic, height=500, @@ -353,7 +353,7 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa t=30, b=0, ), - plot_bgcolor="rgb(245,245,245)", + plot_bgcolor=color_background, legend=dict( orientation="h", yanchor="bottom", @@ -366,12 +366,9 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa ), ), ) - fig.update_xaxes( title=dict( - text="Appended Multiple Time Windows
(single window size = {})".format( - configs["data_processing"]["sequential_splicer"]["window_width"] - ), + text="Time ({})".format(timestamporder), font=dict( size=14, family=font_family, @@ -381,7 +378,6 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa row=4, col=1, ) - fig.update_yaxes( title=dict( text="Power
Consumption", @@ -395,7 +391,6 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa row=1, col=1, ) - fig.update_yaxes( title=dict( text="Penalty
Low", @@ -408,7 +403,6 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa row=2, col=1, ) - fig.update_yaxes( title=dict( text="Penalty
High", @@ -421,7 +415,6 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa row=3, col=1, ) - fig.update_yaxes( title=dict( text="Interval
Score", @@ -434,18 +427,16 @@ def timeseries_comparison(configs, time_ahead): # noqa: C901 TODO: remove noqa row=4, col=1, ) - fig.update_xaxes( showgrid=False, ) - fig.update_yaxes( showgrid=False, ) - ###################################################################### - # saving plot to file - ###################################################################### + """ + save plot image + """ timeseries_comparison = ( configs["data_output"]["exp_dir"] + "/Vis_TimeseriesComparisons.svg" )