fix RPSS_verif and skill_by_year

notebooks/ML_forecast_template.ipynb

 %% Cell type:markdown id: tags:
 
 # Train ML model for predictions of week 3-4 & 5-6
 
 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 id: tags:
 
 # Synopsis
 
 %% Cell type:markdown id: tags:
 
 ## Method: `name`
 
 - decription
 - a few details
 
 %% Cell type:markdown id: tags:
 
 ## Data used
 
 Training-input for Machine Learning model:
 - renku datasets, climetlab, IRIDL
 
 Forecast-input for Machine Learning model:
 - renku datasets, climetlab, IRIDL
 
 Compare Machine Learning model forecast against ground truth:
 - renku datasets, climetlab, IRIDL
 
 %% Cell type:markdown id: tags:
 
 ## Resources used
 for training, details in reproducibility
 
 - platform: renku
 - memory: 8 GB
 - processors: 2 CPU
 - storage required: 10 GB
 
 %% Cell type:markdown id: tags:
 
 ## Safeguards
 
 All points have to be [x] checked. If not, your submission is invalid.
 
 Changes to the code after submissions are not possible, as the `commit` before the `tag` will be reviewed.
 (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 id: tags:
 
 ### Safeguards to prevent [overfitting](https://en.wikipedia.org/wiki/Overfitting?wprov=sfti1)
 
 If the organizers suspect overfitting, your contribution can be disqualified.
 
   - [ ] We didnt use 2020 observations in training (explicit overfitting and cheating)
   - [ ] We didnt repeatedly verify my model on 2020 observations and incrementally improved my RPSS (implicit overfitting)
   - [ ] We provide RPSS scores for the training period with script `skill_by_year`, see in section 6.3 `predict`.
   - [ ] We tried our best to prevent [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)?wprov=sfti1).
   - [ ] 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.
   - [ ] We did use `test` explicitly in training or implicitly in incrementally adjusting parameters.
   - [ ] We considered [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)).
 
 %% Cell type:markdown id: tags:
 
 ### Safeguards for Reproducibility
 Notebook/code must be independently reproducible from scratch by the organizers (after the competition), if not possible: no prize
   - [ ] All training data is publicly available (no pre-trained private neural networks, as they are not reproducible for us)
   - [ ] Code is well documented, readable and reproducible.
-  - [ ] 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.
+  - [ ] Code to reproduce training and predictions is preferred to 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 id: tags:
 
 # Todos to improve template
 
 This is just a demo.
 
 - [ ] for both variables
 - [ ] for both `lead_time`s
 - [ ] ensure probabilistic prediction outcome with `category` dim
 
 %% Cell type:markdown id: tags:
 
 # Imports
 
 %% Cell type:code id: tags:
 
 ``` python
 from tensorflow.keras.layers import Input, Dense, Flatten
 from tensorflow.keras.models import Sequential
 
 import matplotlib.pyplot as plt
 
 import xarray as xr
 xr.set_options(display_style='text')
 
 from dask.utils import format_bytes
 import xskillscore as xs
 ```
 
 %% Cell type:markdown id: tags:
 
 # Get training data
 
 preprocessing of input data may be done in separate notebook/script
 
 %% Cell type:markdown id: tags:
 
 ## Hindcast
 
 get weekly initialized hindcasts
 
 %% Cell type:code id: tags:
 
 ``` python
 # consider renku datasets
 #! renku storage pull path
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Observations
 corresponding to hindcasts
 
 %% Cell type:code id: tags:
 
 ``` python
 # consider renku datasets
 #! renku storage pull path
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 ```
 
 %% Cell type:markdown id: tags:
 
 # ML model
 
 %% Cell type:code id: tags:
 
 ``` python
 bs=32
 
 import numpy as np
 class DataGenerator(keras.utils.Sequence):
     def __init__(self):
         """
         Data generator
 
         Template from https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly
 
         Args:
 
         """
 
         self.on_epoch_end()
 
         # For some weird reason calling .load() earlier messes up the mean and std computations
         if load: print('Loading data into RAM'); self.data.load()
 
     def __len__(self):
         'Denotes the number of batches per epoch'
         return int(np.ceil(self.n_samples / self.batch_size))
 
     def __getitem__(self, i):
         'Generate one batch of data'
         idxs = self.idxs[i * self.batch_size:(i + 1) * self.batch_size]
         # got all nan if nans not masked
         X = self.data.isel(time=idxs).fillna(0.).values
         y = self.verif_data.isel(time=idxs).fillna(0.).values
         return X, y
 
     def on_epoch_end(self):
         'Updates indexes after each epoch'
         self.idxs = np.arange(self.n_samples)
         if self.shuffle == True:
             np.random.shuffle(self.idxs)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## data prep: train, valid, test
 
 %% Cell type:code id: tags:
 
 ``` python
 # time is the forecast_reference_time
 time_train_start,time_train_end='2000','2017'
 time_valid_start,time_valid_end='2018','2019'
 time_test = '2020'
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 dg_train = DataGenerator()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 dg_valid = DataGenerator()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 dg_test = DataGenerator()
 ```
 
 %% Cell type:markdown id: tags:
 
 ## `fit`
 
 %% Cell type:code id: tags:
 
 ``` python
 cnn = keras.models.Sequential([])
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 cnn.summary()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 cnn.compile(keras.optimizers.Adam(1e-4), 'mse')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 import warnings
 warnings.simplefilter("ignore")
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 cnn.fit(dg_train, epochs=1, validation_data=dg_valid)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## `predict`
 
 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 id: tags:
 
 ``` python
 from scripts import skill_by_year
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 def create_predictions(model, dg):
     """Create non-iterative predictions"""
     preds = model.predict(dg).squeeze()
     # transform
 
     return preds
 ```
 
 %% Cell type:markdown id: tags:
 
 ### `predict` training period in-sample
 
 %% Cell type:code id: tags:
 
 ``` python
 preds_is = create_predictions(cnn, dg_train)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 skill_by_year(preds_is)
 ```
 
 %% Cell type:markdown id: tags:
 
 ### `predict` valid out-of-sample
 
 %% Cell type:code id: tags:
 
 ``` python
 preds_os = create_predictions(cnn, dg_valid)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 skill_by_year(preds_os)
 ```
 
 %% Cell type:markdown id: tags:
 
 ### `predict` test
 
 %% Cell type:code id: tags:
 
 ``` python
 preds_test = create_predictions(cnn, dg_test)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 skill_by_year(preds_test)
 ```
 
 %% Cell type:markdown id: tags:
 
 # Submission
 
 %% Cell type:code id: tags:
 
 ``` python
 preds_test.sizes # expect: category(3), longitude, latitude, lead_time(2), forecast_time (53)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 from scripts import assert_predictions_2020
 assert_predictions_2020(preds_test)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 preds_test.to_netcdf('../submissions/ML_prediction_2020.nc')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 #!git add ../submissions/ML_prediction_2020.nc
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 #!git commit -m "commit submission for my_method_name" # whatever message you want
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 #!git tag "submission-my_method_name-0.0.1" # if this is to be checked by scorer, only the last submitted==tagged version will be considered
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 #!git push --tags
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 ```
 
 %% Cell type:markdown id: tags:
 
 # Reproducibility
 
 %% Cell type:markdown id: tags:
 
 ## memory
 
 %% Cell type:code id: tags:
 
 ``` python
 # https://phoenixnap.com/kb/linux-commands-check-memory-usage
 !free -g
 ```
 
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 ## CPU
 
 %% Cell type:code id: tags:
 
 ``` python
 !lscpu
 ```
 
 %% Cell type:markdown id: tags:
 
 ## software
 
 %% Cell type:code id: tags:
 
 ``` python
 !conda list
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 ```

notebooks/RPSS_verification.ipynb

 %% Cell type:markdown id: tags:
 
 # Score biweekly ML forecasts against re-calibrated ECWMF forecast with RPSS
 
 %% Cell type:markdown id: tags:
 
 Goal:
 
 - Score biweekly 2020 ML-based forecasts against climatology with RPSS
 - identical to scorer bot script: https://renkulab.io/gitlab/tasko.olevski/s2s-ai-competition-scoring-image/-/blob/master/scoring/scoring_script.py
 - (for reference) RPSS of recalibrated real-time 2020 ECMWF forecasts
 
 Requirements:
 
 - [`xskillscore`](https://github.com/xarray-contrib/xskillscore)
 - [renku datasets](https://renku-python.readthedocs.io/en/latest/commands.html#module-renku.cli.dataset) / file
     - observations
         - probabilistic:
             - renku dataset: `forecast-like-observations_2020_biweekly_terciled.nc`
     - ML forecasts
         - probabilistic:
             - file: `../submissions/ML_prediction_2020.nc`
     - benchmark to check whether ML better than recalibrated ECMWF:
         - probabilistic:
             - renku dataset: `ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc`
 
 Output:
 - RPSS score spatially averaged from [90N-60S] and averaged over both `lead_time`s and variables
 
 %% Cell type:code id: tags:
 
 ``` python
 import xarray as xr
 import xskillscore as xs
 import numpy as np
 
 xr.set_options(keep_attrs=True)
 xr.set_options(display_style='text')
 
 cache_path = "../data"
 
 plot = False # take much more space in git, consider not commiting figures regularly because git size will increase
 ```
 
-%%%% Output: stream
-
-    /opt/conda/lib/python3.8/site-packages/xarray/backends/cfgrib_.py:27: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message
-      warnings.warn(
-
 %% Cell type:markdown id: tags:
 
 # Get categorized
 
 %% Cell type:markdown id: tags:
 
 ## 2020 observations
 
 %% Cell type:code id: tags:
 
 ``` python
 # to use retrieve from git lfs
 !renku storage pull ../data/forecast-like-observations_2020_biweekly_terciled.nc
 ```
 
 %%%% Output: stream
 
     Warning: Run CLI commands only from project's root directory.
     
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_p = xr.open_dataset(f'{cache_path}/forecast-like-observations_2020_biweekly_terciled.nc')
 
 obs_p.sizes
 ```
 
-%%%% Output: stream
-
-    /opt/conda/lib/python3.8/site-packages/xarray/backends/plugins.py:61: RuntimeWarning: Engine 'cfgrib' loading failed:
-    /opt/conda/lib/python3.8/site-packages/gribapi/_bindings.cpython-38-x86_64-linux-gnu.so: undefined symbol: codes_bufr_key_is_header
-      warnings.warn(f"Engine {name!r} loading failed:\n{ex}", RuntimeWarning)
-
 %%%% Output: execute_result
 
     Frozen(SortedKeysDict({'category': 3, 'lead_time': 2, 'forecast_time': 53, 'latitude': 121, 'longitude': 240}))
 
 %% Cell type:markdown id: tags:
 
 ## 2020 ML-based forecasts
 
 %% Cell type:code id: tags:
 
 ``` python
 !renku storage pull ../submissions/ML_prediction_2020.nc
 ```
 
 %%%% Output: stream
 
     Warning: Run CLI commands only from project's root directory.
     
 
 %% Cell type:code id: tags:
 
 ``` python
 # submission for your ML model
 fct_p = xr.open_dataset(f"../submissions/ML_prediction_2020.nc")
 fct_p.sizes
 ```
 
 %%%% Output: execute_result
 
     Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240, 'category': 3}))
 
 %% Cell type:markdown id: tags:
 
 ## climatology
 
 %% Cell type:code id: tags:
 
 ``` python
 clim_p = xr.DataArray([1/3, 1/3, 1/3], dims='category', coords={'category':['below normal', 'near normal', 'above normal']}).to_dataset(name='tp')
 clim_p['t2m'] = clim_p['tp']
 ```
 
 %% Cell type:markdown id: tags:
 
 ### checks
 
 %% Cell type:code id: tags:
 
 ``` python
 from scripts import assert_predictions_2020
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 assert_predictions_2020(fct_p)
 assert_predictions_2020(obs_p)
 ```
 
 %% Cell type:markdown id: tags:
 
 # RPS
 
 %% Cell type:markdown id: tags:
 
 ## RPS ML model
 
 %% Cell type:code id: tags:
 
 ``` python
 rps_ML = xs.rps(obs_p, fct_p, category_edges=None, dim='forecast_time', input_distributions='p').compute()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 if plot:
     for v in rps_ML.data_vars:
         rps_ML[v].plot(robust=True, col='lead_time')
 ```
 
 %% Cell type:markdown id: tags:
 
 ## RPS climatology
 
 %% Cell type:code id: tags:
 
 ``` python
 rps_clim = xs.rps(obs_p, clim_p, category_edges=None, dim='forecast_time', input_distributions='p').compute()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 if plot:
     for v in rps_clim.data_vars:
         rps_clim[v].plot(robust=True, col='lead_time')
 ```
 
 %% Cell type:markdown id: tags:
 
 ## RPSS
 
 %% Cell type:code id: tags:
 
 ``` python
 rpss = (1 - rps_ML / rps_clim)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 if plot:
     for v in rpss.data_vars:
         rpss[v].plot(robust=True, col='lead_time')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 ```
 
 %% Cell type:markdown id: tags:
 
 # Scoring for RPSS leaderboard
 
 %% Cell type:code id: tags:
 
 ``` python
 # check for -inf grid cells
 if (rpss==-np.inf).to_array().any():
-    (rpss == rpss.min()).sum()
+    print(f'find N={(rpss == rpss.min()).sum()} -inf grid cells')
 
     # dirty fix
     rpss = rpss.clip(-1, 1)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # what to do with requested grid cells where NaN is submitted? also penalize, todo: https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/issues/7
 ```