diff --git a/docs/source/whats_new.rst b/docs/source/whats_new.rst index c63d1b6d6..7100c8d32 100644 --- a/docs/source/whats_new.rst +++ b/docs/source/whats_new.rst @@ -22,11 +22,12 @@ Enhancements - Adding a comprehensive benchmarking function (:gh:`264` by `Divyesh Narayanan`_ and `Sylvain Chevallier`_) - Add meta-information for datasets in documentation (:gh:`317` by `Bruno Aristimunha`_) - Add GridSearchCV for different evaluation procedure (:gh:`319` by `Igor Carrara`_) -- Adding new tutorial to benchmark with GridSearchCV (:gh:`323` by `Igor Carrara`_) +- Add new tutorial to benchmark with GridSearchCV (:gh:`323` by `Igor Carrara`_) - Add six deep learning models (Tensorflow), and build a tutorial to show to use the deep learning model (:gh:`326` by `Igor Carrara`_, `Bruno Aristimunha`_ and `Sylvain Chevallier`_) - Add a augmentation model to the pipeline (:gh:`326` by `Igor Carrara`_) - Add BrainDecode example(:gh:`340` by `Igor Carrara`_ and `Bruno Aristimunha`_) - Add Google Analytics to the documentation (:gh:`335` by `Bruno Aristimunha`_) +- Add CodeCarbon to track emission CO₂ (:gh:`350` by `Igor Carrara`_, `Bruno Aristimunha`_ and `Sylvain Chevallier`_) Bugs ~~~~ diff --git a/moabb/analysis/results.py b/moabb/analysis/results.py index 144fb3fb1..a68e42625 100644 --- a/moabb/analysis/results.py +++ b/moabb/analysis/results.py @@ -13,6 +13,14 @@ from sklearn.base import BaseEstimator +try: + from codecarbon import EmissionsTracker # noqa + + _carbonfootprint = True +except ImportError: + _carbonfootprint = False + + def get_string_rep(obj): if issubclass(type(obj), BaseEstimator): str_repr = repr(obj.get_params()) @@ -103,7 +111,7 @@ class that will abstract result storage "{:%Y-%m-%d, %H:%M}".format(datetime.now()) ) - def add(self, results, pipelines): + def add(self, results, pipelines): # noqa: C901 """add results""" def to_list(res): @@ -117,6 +125,13 @@ def to_list(res): else: return res + col_names = ["score", "time", "samples"] + if _carbonfootprint: + n_cols = 4 + col_names.append("carbon_emission") + else: + n_cols = 3 + with h5py.File(self.filepath, "r+") as f: for name, data_dict in results.items(): digest = get_digest(pipelines[name]) @@ -140,12 +155,14 @@ def to_list(res): dt = h5py.special_dtype(vlen=str) dset.create_dataset("id", (0, 2), dtype=dt, maxshape=(None, 2)) dset.create_dataset( - "data", (0, 3 + n_add_cols), maxshape=(None, 3 + n_add_cols) + "data", + (0, n_cols + n_add_cols), + maxshape=(None, n_cols + n_add_cols), ) dset.attrs["channels"] = d1["n_channels"] dset.attrs.create( "columns", - ["score", "time", "samples", *self.additional_columns], + col_names + self.additional_columns, dtype=dt, ) dset = ppline_grp[dname] @@ -163,8 +180,17 @@ def to_list(res): f"were specified in the evaluation, but results" f" contain only these keys: {d.keys()}." ) from None + cols = [d["score"], d["time"], d["n_samples"]] + if _carbonfootprint: + if isinstance(d["carbon_emission"], tuple): + cols.append(*d["carbon_emission"]) + else: + cols.append(d["carbon_emission"]) dset["data"][-1, :] = np.asarray( - [d["score"], d["time"], d["n_samples"], *add_cols] + [ + *cols, + *add_cols, + ] ) def to_dataframe(self, pipelines=None): diff --git a/moabb/benchmark.py b/moabb/benchmark.py index d89f5e213..2ab4e1bc2 100644 --- a/moabb/benchmark.py +++ b/moabb/benchmark.py @@ -21,6 +21,14 @@ ) +try: + from codecarbon import EmissionsTracker # noqa + + _carbonfootprint = True +except ImportError: + _carbonfootprint = False + + log = logging.getLogger(__name__) @@ -185,18 +193,23 @@ def _display_results(results): for d in results["dataset"].unique(): for p in results["pipeline"].unique(): for e in results["evaluation"].unique(): - tab.append( - { - "dataset": d, - "evaluation": e, - "pipeline": p, - "avg score": results[ - (results["dataset"] == d) - & (results["pipeline"] == p) - & (results["evaluation"] == e) - ]["score"].mean(), - } - ) + r = { + "dataset": d, + "evaluation": e, + "pipeline": p, + "avg score": results[ + (results["dataset"] == d) + & (results["pipeline"] == p) + & (results["evaluation"] == e) + ]["score"].mean(), + } + if _carbonfootprint: + r["carbon emission"] = results[ + (results["dataset"] == d) + & (results["pipeline"] == p) + & (results["evaluation"] == e) + ]["carbon_emission"].sum() + tab.append(r) tab = pd.DataFrame(tab) print(tab) diff --git a/moabb/evaluations/evaluations.py b/moabb/evaluations/evaluations.py index 46ef64db6..e8ee23a6c 100644 --- a/moabb/evaluations/evaluations.py +++ b/moabb/evaluations/evaluations.py @@ -23,6 +23,14 @@ from moabb.evaluations.base import BaseEvaluation +try: + from codecarbon import EmissionsTracker + + _carbonfootprint = True +except ImportError: + _carbonfootprint = False + + log = logging.getLogger(__name__) # Numpy ArrayLike is only available starting from Numpy 1.20 and Python 3.8 @@ -183,6 +191,10 @@ def _evaluate(self, dataset, pipelines, param_grid): ix = metadata.session == session for name, clf in run_pipes.items(): + if _carbonfootprint: + # Initialize CodeCarbon + tracker = EmissionsTracker(save_to_file=False, log_level="error") + tracker.start() t_start = time() cv = StratifiedKFold(5, shuffle=True, random_state=self.random_state) scorer = get_scorer(self.paradigm.scoring) @@ -228,6 +240,10 @@ def _evaluate(self, dataset, pipelines, param_grid): error_score=self.error_score, ) score = acc.mean() + if _carbonfootprint: + emissions = tracker.stop() + if emissions is None: + emissions = np.NaN duration = time() - t_start nchan = X.info["nchan"] if isinstance(X, BaseEpochs) else X.shape[1] res = { @@ -240,6 +256,8 @@ def _evaluate(self, dataset, pipelines, param_grid): "n_channels": nchan, "pipeline": name, } + if _carbonfootprint: + res["carbon_emission"] = (1000 * emissions,) yield res @@ -482,6 +500,11 @@ def evaluate(self, dataset, pipelines, param_grid): scorer = get_scorer(self.paradigm.scoring) for name, clf in run_pipes.items(): + if _carbonfootprint: + # Initialise CodeCarbon + tracker = EmissionsTracker(save_to_file=False, log_level="error") + tracker.start() + # we want to store a results per session cv = LeaveOneGroupOut() @@ -501,7 +524,14 @@ def evaluate(self, dataset, pipelines, param_grid): param_grid, name_grid, name, grid_clf, X, y, cv, groups ) + if _carbonfootprint: + emissions_grid = tracker.stop() + if emissions_grid is None: + emissions_grid = 0 + for train, test in cv.split(X, y, groups): + if _carbonfootprint: + tracker.start() t_start = time() if isinstance(X, BaseEpochs): cvclf = clone(grid_clf) @@ -521,6 +551,11 @@ def evaluate(self, dataset, pipelines, param_grid): error_score=self.error_score, ) score = result["test_scores"] + if _carbonfootprint: + emissions = tracker.stop() + if emissions is None: + emissions = 0 + duration = time() - t_start nchan = X.info["nchan"] if isinstance(X, BaseEpochs) else X.shape[1] res = { @@ -533,6 +568,9 @@ def evaluate(self, dataset, pipelines, param_grid): "n_channels": nchan, "pipeline": name, } + if _carbonfootprint: + res["carbon_emission"] = (1000 * (emissions + emissions_grid),) + yield res def is_valid(self, dataset): @@ -643,7 +681,15 @@ def evaluate(self, dataset, pipelines, param_grid): cv = LeaveOneGroupOut() # Implement Grid Search + emissions_grid = {} + + if _carbonfootprint: + # Initialise CodeCarbon + tracker = EmissionsTracker(save_to_file=False, log_level="error") + for name, clf in pipelines.items(): + if _carbonfootprint: + tracker.start() name_grid = os.path.join( str(self.hdf5_path), "GridSearch_CrossSubject", dataset.code, name ) @@ -652,6 +698,11 @@ def evaluate(self, dataset, pipelines, param_grid): param_grid, name_grid, name, clf, pipelines, X, y, cv, groups ) + if _carbonfootprint: + emissions_grid[name] = tracker.stop() + if emissions_grid[name] is None: + emissions_grid[name] = 0 + # Progressbar at subject level for train, test in tqdm( cv.split(X, y, groups), @@ -664,8 +715,14 @@ def evaluate(self, dataset, pipelines, param_grid): # iterate over pipelines for name, clf in run_pipes.items(): + if _carbonfootprint: + tracker.start() t_start = time() model = deepcopy(clf).fit(X[train], y[train]) + if _carbonfootprint: + emissions = tracker.stop() + if emissions is None: + emissions = 0 duration = time() - t_start # we eval on each session @@ -673,6 +730,10 @@ def evaluate(self, dataset, pipelines, param_grid): ix = sessions[test] == session score = _score(model, X[test[ix]], y[test[ix]], scorer) + if _carbonfootprint: + if emissions_grid[name] is None: + emissions_grid[name] = 0 + nchan = X.info["nchan"] if isinstance(X, BaseEpochs) else X.shape[1] res = { "time": duration, @@ -685,6 +746,10 @@ def evaluate(self, dataset, pipelines, param_grid): "pipeline": name, } + if _carbonfootprint: + res["carbon_emission"] = ( + 1000 * (emissions + emissions_grid[name]), + ) yield res def is_valid(self, dataset): diff --git a/moabb/tests/analysis.py b/moabb/tests/analysis.py index b55c57112..f08f94645 100644 --- a/moabb/tests/analysis.py +++ b/moabb/tests/analysis.py @@ -11,7 +11,12 @@ from moabb.paradigms.base import BaseParadigm -# dummy evaluation +try: + from codecarbon import EmissionsTracker # noqa + + _carbonfootprint = True +except ImportError: + _carbonfootprint = False class DummyEvaluation(BaseEvaluation): @@ -83,6 +88,12 @@ def datasets(self): "n_channels": 10, } +if _carbonfootprint: + d1["carbon_emission"] = 5 + d2["carbon_emission"] = 10 + d3["carbon_emission"] = 0.2 + d4["carbon_emission"] = 1 + def to_pipeline_dict(pnames): return {n: "pipeline {}".format(n) for n in pnames} diff --git a/moabb/tests/evaluations.py b/moabb/tests/evaluations.py index ed79052a9..d08e53b09 100644 --- a/moabb/tests/evaluations.py +++ b/moabb/tests/evaluations.py @@ -20,6 +20,14 @@ from moabb.paradigms.motor_imagery import FakeImageryParadigm +try: + from codecarbon import EmissionsTracker # noqa + + _carbonfootprint = True +except ImportError: + _carbonfootprint = False + + pipelines = OrderedDict() pipelines["C"] = make_pipeline(Covariances("oas"), CSP(8), LDA()) dataset = FakeDataset(["left_hand", "right_hand"], n_subjects=2) @@ -64,8 +72,8 @@ def test_eval_results(self): # We should get 4 results, 2 sessions 2 subjects self.assertEqual(len(results), 4) - 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