{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train ML model to correct predictions of week 3-4 & 5-6\n",
"\n",
"This notebook create a Machine Learning `ML_model` to predict weeks 3-4 & 5-6 based on `S2S` weeks 3-4 & 5-6 forecasts and is compared to `CPC` observations for the [`s2s-ai-challenge`](https://s2s-ai-challenge.github.io/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Synopsis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Method: `mean bias reduction`\n",
"\n",
"- calculate the mean bias from 2000-2019 deterministic ensemble mean forecast\n",
"- remove that mean bias from 2020 forecast deterministic ensemble mean forecast\n",
"- no Machine Learning used here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data used\n",
"\n",
"type: renku datasets\n",
"\n",
"Training-input for Machine Learning model:\n",
"- hindcasts of models:\n",
" - ECMWF: `ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr`\n",
"\n",
"Forecast-input for Machine Learning model:\n",
"- real-time 2020 forecasts of models:\n",
" - ECMWF: `ecmwf_forecast-input_2020_biweekly_deterministic.zarr`\n",
"\n",
"Compare Machine Learning model forecast against against ground truth:\n",
"- `CPC` observations:\n",
" - `hindcast-like-observations_biweekly_deterministic.zarr`\n",
" - `forecast-like-observations_2020_biweekly_deterministic.zarr`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources used\n",
"for training, details in reproducibility\n",
"\n",
"- platform: MPI-M supercompute 1 Node\n",
"- memory: 64 GB\n",
"- processors: 36 CPU\n",
"- storage required: 10 GB"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Safeguards\n",
"\n",
"All points have to be [x] checked. If not, your submission is invalid.\n",
"\n",
"Changes to the code after submissions are not possible, as the `commit` before the `tag` will be reviewed.\n",
"(Only in exceptions and if previous effort in reproducibility can be found, it may be allowed to improve readability and reproducibility after November 1st 2021.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Safeguards to prevent [overfitting](https://en.wikipedia.org/wiki/Overfitting?wprov=sfti1) \n",
"\n",
"If the organizers suspect overfitting, your contribution can be disqualified.\n",
"\n",
" - [x] We didnt use 2020 observations in training (explicit overfitting and cheating)\n",
" - [x] We didnt repeatedly verify my model on 2020 observations and incrementally improved my RPSS (implicit overfitting)\n",
" - [x] We provide RPSS scores for the training period with script `skill_by_year`, see in section 6.3 `predict`.\n",
" - [x] We tried our best to prevent [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)?wprov=sfti1).\n",
" - [x] We honor the `train-validate-test` [split principle](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets). This means that the hindcast data is split into `train` and `validate`, whereas `test` is withheld.\n",
" - [x] We did use `test` explicitly in training or implicitly in incrementally adjusting parameters.\n",
" - [x] We considered [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Safeguards for Reproducibility\n",
"Notebook/code must be independently reproducible from scratch by the organizers (after the competition), if not possible: no prize\n",
" - [x] All training data is publicly available (no pre-trained private neural networks, as they are not reproducible for us)\n",
" - [x] Code is well documented, readable and reproducible.\n",
" - [x] Code to reproduce training and predictions should run within a day on the described architecture. If the training takes longer than a day, please justify why this is needed. Please do not submit training piplelines, which take weeks to train."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"xr.set_options(display_style='text')\n",
"import numpy as np\n",
"\n",
"from dask.utils import format_bytes\n",
"import xskillscore as xs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get training data\n",
"\n",
"preprocessing of input data may be done in separate notebook/script"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hindcast\n",
"\n",
"get weekly initialized hindcasts"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
"\u001b[0m\n"
]
}
],
"source": [
"# preprocessed as renku dataset\n",
"!renku storage pull ../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"hind_2000_2019 = xr.open_zarr(\"../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr\", consolidated=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
"\u001b[0m\n"
]
}
],
"source": [
"# preprocessed as renku dataset\n",
"!renku storage pull ../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"fct_2020 = xr.open_zarr(\"../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr\", consolidated=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Observations\n",
"corresponding to hindcasts"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
"\u001b[0m\n"
]
}
],
"source": [
"# preprocessed as renku dataset\n",
"!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"obs_2000_2019 = xr.open_zarr(\"../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr\", consolidated=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
"\u001b[0m\n"
]
}
],
"source": [
"# preprocessed as renku dataset\n",
"!renku storage pull ../data/forecast-like-observations_2020_biweekly_deterministic.zarr"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"obs_2020 = xr.open_zarr(\"../data/forecast-like-observations_2020_biweekly_deterministic.zarr\", consolidated=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# no ML model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we just remove the mean bias from the ensemble mean forecast."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
" FutureWarning,\n",
"/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
" x = np.divide(x1, x2, out)\n"
]
}
],
"source": [
"bias_2000_2019 = (hind_2000_2019.mean('realization') - obs_2000_2019).groupby('forecast_time.weekofyear').mean().compute()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `predict`\n",
"\n",
"Create predictions and print `mean(variable, lead_time, longitude, weighted latitude)` RPSS for all years as calculated by `skill_by_year`. For now RPS, todo: change to RPSS."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from scripts import make_probabilistic"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
"\u001b[0m\n"
]
}
],
"source": [
"!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"cache_path='../data'\n",
"tercile_file = f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc'\n",
"tercile_edges = xr.open_dataset(tercile_file)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# this is not useful but results have expected dimensions\n",
"# actually train for each lead_time\n",
"\n",
"def create_predictions(fct, bias):\n",
" preds = fct - bias.sel(weekofyear=fct.forecast_time.dt.weekofyear)\n",
" preds = make_probabilistic(preds, tercile_edges)\n",
" return preds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `predict` training period in-sample"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"#!renku storage pull ../data/forecast-like-observations_2020_biweekly_terciled.nc"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"#!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from scripts import skill_by_year"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
" FutureWarning,\n",
"/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
" FutureWarning,\n"
]
}
],
"source": [
"preds_is = create_predictions(hind_2000_2019, bias_2000_2019).compute()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RPS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td>0.463290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001</th>\n",
" <td>0.501615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002</th>\n",
" <td>0.498100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003</th>\n",
" <td>0.499914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004</th>\n",
" <td>0.533146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005</th>\n",
" <td>0.486682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006</th>\n",
" <td>0.492787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007</th>\n",
" <td>0.555934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008</th>\n",
" <td>0.507756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009</th>\n",
" <td>0.515228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td>0.498032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>0.548217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>0.556501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>0.519008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td>0.521487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
" <td>0.507068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>0.520476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.590591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018</th>\n",
" <td>0.604847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019</th>\n",
" <td>0.546725</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RPS\n",
"year \n",
"2000 0.463290\n",
"2001 0.501615\n",
"2002 0.498100\n",
"2003 0.499914\n",
"2004 0.533146\n",
"2005 0.486682\n",
"2006 0.492787\n",
"2007 0.555934\n",
"2008 0.507756\n",
"2009 0.515228\n",
"2010 0.498032\n",
"2011 0.548217\n",
"2012 0.556501\n",
"2013 0.519008\n",
"2014 0.521487\n",
"2015 0.507068\n",
"2016 0.520476\n",
"2017 0.590591\n",
"2018 0.604847\n",
"2019 0.546725"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"skill_by_year(preds_is)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `predict` test"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
" FutureWarning,\n",
"/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
" FutureWarning,\n"
]
}
],
"source": [
"preds_test = create_predictions(fct_2020, bias_2000_2019)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RPS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020</th>\n",
" <td>0.520714</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RPS\n",
"year \n",
"2020 0.520714"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"skill_by_year(preds_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Submission"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from scripts import assert_predictions_2020\n",
"assert_predictions_2020(preds_test)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"del preds_test['weekofyear']"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"preds_test.to_netcdf('../submissions/ML_prediction_2020.nc')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"#!git add ../submissions/ML_prediction_2020.nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!git commit -m \"template_test no ML mean bias reduction\" # whatever message you want"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#!git tag \"submission-no_ML_mean_bias_reduction-0.0.1\" # if this is to be checked by scorer, only the last submitted==tagged version will be considered"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!git push --tags"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reproducibility"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## memory"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" total used free shared buffers cached\n",
"Mem: 62 21 41 0 0 5\n",
"-/+ buffers/cache: 15 47\n",
"Swap: 0 0 0\n"
]
}
],
"source": [
"# https://phoenixnap.com/kb/linux-commands-check-memory-usage\n",
"!free -g"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CPU"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Architecture: x86_64\n",
"CPU op-mode(s): 32-bit, 64-bit\n",
"Byte Order: Little Endian\n",
"CPU(s): 72\n",
"On-line CPU(s) list: 0-71\n",
"Thread(s) per core: 2\n",
"Core(s) per socket: 18\n",
"Socket(s): 2\n",
"NUMA node(s): 2\n",
"Vendor ID: GenuineIntel\n",
"CPU family: 6\n",
"Model: 79\n",
"Model name: Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10GHz\n",
"Stepping: 1\n",
"CPU MHz: 1200.000\n",
"BogoMIPS: 4190.00\n",
"Virtualization: VT-x\n",
"L1d cache: 32K\n",
"L1i cache: 32K\n",
"L2 cache: 256K\n",
"L3 cache: 46080K\n",
"NUMA node0 CPU(s): 0-17,36-53\n",
"NUMA node1 CPU(s): 18-35,54-71\n"
]
}
],
"source": [
"!lscpu"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## software"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# packages in environment at /opt/conda:\n",
"#\n",
"# Name Version Build Channel\n",
"_libgcc_mutex 0.1 conda_forge conda-forge\n",
"_openmp_mutex 4.5 1_gnu conda-forge\n",
"_tflow_select 2.3.0 mkl defaults\n",
"absl-py 0.12.0 py38h06a4308_0 defaults\n",
"aiobotocore 1.2.2 pyhd3eb1b0_0 defaults\n",
"aiohttp 3.7.4.post0 pypi_0 pypi\n",
"aioitertools 0.7.1 pyhd3eb1b0_0 defaults\n",
"alembic 1.4.3 pyh9f0ad1d_0 conda-forge\n",
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"python-dateutil 2.8.1 py_0 conda-forge\n",
"python-eccodes 2021.03.0 py38hb5d20a5_0 conda-forge\n",
"python-editor 1.0.4 py_0 conda-forge\n",
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"python_abi 3.8 1_cp38 conda-forge\n",
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"tenacity 7.0.0 pypi_0 pypi\n",
"tensorboard 2.4.0 pyhc547734_0 defaults\n",
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]
}
],
"source": [
"!conda list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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