Added Neptune logging

CHANGES.md

@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added `NeptuneLogger` callback for logging experiment metadata to neptune.ai
+
 ### Changed
 
 - When using caching in scoring callbacks, no longer uselessly iterate over the data; this can save time if iteration is slow (#552, #557)

requirements-dev.txt

@@ -3,6 +3,7 @@ flaky
 future>=0.17.1
 jupyter
 matplotlib>=2.0.2
+neptune-client>=0.4.103
 numpydoc
 openpyxl
 pandas

skorch/callbacks/__init__.py

@@ -13,7 +13,7 @@
 from .training import *
 from .lr_scheduler import *
 
-__all__ = ['Callback', 'EpochTimer', 'PrintLog', 'ProgressBar',
+__all__ = ['Callback', 'EpochTimer', 'NeptuneLogger', 'PrintLog', 'ProgressBar',
            'LRScheduler', 'WarmRestartLR', 'GradientNormClipping',
            'BatchScoring', 'EpochScoring', 'Checkpoint', 'EarlyStopping',
            'Freezer', 'Unfreezer', 'Initializer', 'ParamMapper',

skorch/callbacks/logging.py

@@ -14,8 +14,7 @@
 from skorch.dataset import get_len
 from skorch.callbacks import Callback
 
-
-__all__ = ['EpochTimer', 'PrintLog', 'ProgressBar', 'TensorBoard']
+__all__ = ['EpochTimer', 'NeptuneLogger', 'PrintLog', 'ProgressBar', 'TensorBoard']
 
 
 def filter_log_keys(keys, keys_ignored=None):
@@ -62,6 +61,142 @@ def on_epoch_end(self, net, **kwargs):
         net.history.record('dur', time.time() - self.epoch_start_time_)
 
 
+class NeptuneLogger(Callback):
+    """Logs results from history to Neptune
+
+    Neptune is a lightweight experiment tracking tool.
+    You can read more about it here: https://neptune.ai
+
+    Use this callback to automatically log all interesting values from
+    your net's history to Neptune.
+
+    The best way to log additional information is to log directly to the
+    experiment object or subclass the ``on_*`` methods.
+
+    To monitor resource consumption install psutil
+
+    >>> pip install psutil
+
+    You can view example experiment logs here:
+    https://ui.neptune.ai/o/shared/org/skorch-integration/e/SKOR-4/logs
+
+    Examples
+    --------
+    >>> # Install neptune
+    >>> pip install neptune-client
+    >>> # Create a neptune experiment object
+    >>> import neptune
+    ...
+    ... # We are using api token for an anonymous user.
+    ... # For your projects use the token associated with your neptune.ai account
+    >>> neptune.init(api_token='eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5tbCIsImFwaV9rZXkiOiJiNzA2YmM4Zi03NmY5LTRjMmUtOTM5ZC00YmEwMzZmOTMyZTQifQ==',
+    ...              project_qualified_name='shared/skorch-integration')
+    ...
+    ... experiment = neptune.create_experiment(
+    ...                        name='skorch-basic-example',
+    ...                        params={'max_epochs': 20,
+    ...                                'lr': 0.01},
+    ...                        upload_source_files=['skorch_example.py'])
+
+    >>> # Create a neptune_logger callback
+    >>> neptune_logger = NeptuneLogger(experiment, close_after_train=False)
+
+    >>> # Pass a logger to net callbacks argument
+    >>> net = NeuralNetClassifier(
+    ...           ClassifierModule,
+    ...           max_epochs=20,
+    ...           lr=0.01,
+    ...           callbacks=[neptune_logger])
+
+    >>> # Log additional metrics after training has finished
+    >>> from sklearn.metrics import roc_auc_score
+    ... y_pred = net.predict_proba(X)
+    ... auc = roc_auc_score(y, y_pred[:, 1])
+    ...
+    ... neptune_logger.experiment.log_metric('roc_auc_score', auc)
+
+    >>> # log charts like ROC curve
+    ... from scikitplot.metrics import plot_roc
+    ... import matplotlib.pyplot as plt
+    ...
+    ... fig, ax = plt.subplots(figsize=(16, 12))
+    ... plot_roc(y, y_pred, ax=ax)
+    ... neptune_logger.experiment.log_image('roc_curve', fig)
+
+    >>> # log net object after training
+    ... net.save_params(f_params='basic_model.pkl')
+    ... neptune_logger.experiment.log_artifact('basic_model.pkl')
+
+    >>> # close experiment
+    ... neptune_logger.experiment.stop()
+
+    Parameters
+    ----------
+    experiment : neptune.experiments.Experiment
+      Instantiated ``Experiment`` class.
+
+    log_on_batch_end : bool (default=False)
+      Whether to log loss and other metrics on batch level.
+
+    close_after_train : bool (default=True)
+      Whether to close the ``Experiment`` object once training
+      finishes. Set this parameter to False if you want to continue
+      logging to the same Experiment or if you use it as a context
+      manager.
+
+    keys_ignored : str or list of str (default=None)
+      Key or list of keys that should not be logged to
+      Neptune. Note that in addition to the keys provided by the
+      user, keys such as those starting with 'event_' or ending on
+      '_best' are ignored by default.
+
+    .. _Neptune: https://www.neptune.ai
+
+    """
+
+    def __init__(
+            self,
+            experiment,
+            log_on_batch_end=False,
+            close_after_train=True,
+            keys_ignored=None,
+    ):
+        self.experiment = experiment
+        self.log_on_batch_end = log_on_batch_end
+        self.close_after_train = close_after_train
+        self.keys_ignored = keys_ignored
+
+    def initialize(self):
+        self.first_batch_ = True
+
+        keys_ignored = self.keys_ignored
+        if isinstance(keys_ignored, str):
+            keys_ignored = [keys_ignored]
+        self.keys_ignored_ = set(keys_ignored or [])
+        self.keys_ignored_.add('batches')
+        return self
+
+    def on_batch_end(self, net, **kwargs):
+        if self.log_on_batch_end:
+            batch_logs = net.history[-1]['batches'][-1]
+
+            for key in filter_log_keys(batch_logs.keys(), self.keys_ignored_):
+                self.experiment.log_metric(key, batch_logs[key])
+
+    def on_epoch_end(self, net, **kwargs):
+        """Automatically log values from the last history step."""
+        history = net.history
+        epoch_logs = history[-1]
+        epoch = epoch_logs['epoch']
+
+        for key in filter_log_keys(epoch_logs.keys(), self.keys_ignored_):
+            self.experiment.log_metric(key, x=epoch, y=epoch_logs[key])
+
+    def on_train_end(self, net, **kwargs):
+        if self.close_after_train:
+            self.experiment.stop()
+
+
 class PrintLog(Callback):
     """Print useful information from the model's history as a table.
 
@@ -282,7 +417,6 @@ class ProgressBar(Callback):
 
       >>> net.history[-1, 'batches', -1, key]
     """
-
     def __init__(
             self,
             batches_per_epoch='auto',

skorch/tests/callbacks/test_logging.py

@@ -3,14 +3,165 @@
 from functools import partial
 import os
 from unittest.mock import Mock
-from unittest.mock import patch
+from unittest.mock import call, patch
 
 import numpy as np
 import pytest
 import torch
 from torch import nn
 
-from skorch.tests.conftest import tensorboard_installed
+from skorch.tests.conftest import neptune_installed, tensorboard_installed
+
+
+@pytest.mark.skipif(
+    not neptune_installed, reason='neptune is not installed')
+class TestNeptune:
+    @pytest.fixture
+    def net_cls(self):
+        from skorch import NeuralNetClassifier
+        return NeuralNetClassifier
+
+    @pytest.fixture
+    def data(self, classifier_data):
+        X, y = classifier_data
+        # accelerate training since we don't care for the loss
+        X, y = X[:40], y[:40]
+        return X, y
+
+    @pytest.fixture
+    def neptune_logger_cls(self):
+        from skorch.callbacks import NeptuneLogger
+        return NeptuneLogger
+
+    @pytest.fixture
+    def neptune_experiment_cls(self):
+        import neptune
+        neptune.init(project_qualified_name="tests/dry-run",
+                     backend=neptune.OfflineBackend())
+        return neptune.create_experiment
+
+    @pytest.fixture
+    def mock_experiment(self, neptune_experiment_cls):
+        mock = Mock(spec=neptune_experiment_cls)
+        mock.log_metric = Mock()
+        mock.stop = Mock()
+        return mock
+
+    @pytest.fixture
+    def net_fitted(
+            self,
+            net_cls,
+            classifier_module,
+            data,
+            neptune_logger_cls,
+            mock_experiment,
+    ):
+        return net_cls(
+            classifier_module,
+            callbacks=[neptune_logger_cls(mock_experiment)],
+            max_epochs=3,
+        ).fit(*data)
+
+    def test_experiment_closed_automatically(self, net_fitted, mock_experiment):
+        assert mock_experiment.stop.call_count == 1
+
+    def test_experiment_not_closed(
+            self,
+            net_cls,
+            classifier_module,
+            data,
+            neptune_logger_cls,
+            mock_experiment,
+    ):
+        net_cls(
+            classifier_module,
+            callbacks=[
+                neptune_logger_cls(mock_experiment, close_after_train=False)],
+            max_epochs=2,
+        ).fit(*data)
+        assert mock_experiment.stop.call_count == 0
+
+    def test_ignore_keys(
+            self,
+            net_cls,
+            classifier_module,
+            data,
+            neptune_logger_cls,
+            mock_experiment,
+    ):
+        # ignore 'dur' and 'valid_loss', 'unknown' doesn't exist but
+        # this should not cause a problem
+        npt = neptune_logger_cls(
+            mock_experiment, keys_ignored=['dur', 'valid_loss', 'unknown'])
+        net_cls(
+            classifier_module,
+            callbacks=[npt],
+            max_epochs=3,
+        ).fit(*data)
+
+    def test_keys_ignored_is_string(self, neptune_logger_cls, mock_experiment):
+        npt = neptune_logger_cls(mock_experiment,
+                                 keys_ignored='a-key').initialize()
+        expected = {'a-key', 'batches'}
+        assert npt.keys_ignored_ == expected
+
+    def test_fit_with_real_experiment(
+            self,
+            net_cls,
+            classifier_module,
+            data,
+            neptune_logger_cls,
+            neptune_experiment_cls,
+    ):
+        net = net_cls(
+            classifier_module,
+            callbacks=[neptune_logger_cls(neptune_experiment_cls())],
+            max_epochs=5,
+        )
+        net.fit(*data)
+
+    def test_log_on_batch_level_on(
+            self,
+            net_cls,
+            classifier_module,
+            data,
+            neptune_logger_cls,
+            mock_experiment,
+    ):
+        net = net_cls(
+            classifier_module,
+            callbacks=[neptune_logger_cls(mock_experiment, log_on_batch_end=True)],
+            max_epochs=5,
+            batch_size=4,
+            train_split=False
+        )
+        net.fit(*data)
+
+        # 5 epochs x (40/4 batches x 2 batch metrics + 2 epoch metrics) = 110 calls
+        assert mock_experiment.log_metric.call_count == 110
+        mock_experiment.log_metric.assert_any_call('train_batch_size', 4)
+
+    def test_log_on_batch_level_off(
+            self,
+            net_cls,
+            classifier_module,
+            data,
+            neptune_logger_cls,
+            mock_experiment,
+    ):
+        net = net_cls(
+            classifier_module,
+            callbacks=[neptune_logger_cls(mock_experiment, log_on_batch_end=False)],
+            max_epochs=5,
+            batch_size=4,
+            train_split=False
+        )
+        net.fit(*data)
+
+        # 5 epochs x 2 epoch metrics = 10 calls
+        assert mock_experiment.log_metric.call_count == 10
+        assert call('train_batch_size', 4) \
+               not in mock_experiment.log_metric.call_args_list
 
 
 class TestPrintLog:
@@ -42,6 +193,7 @@ def odd_epoch_callback(self):
         class OddEpochCallback(Callback):
             def on_epoch_end(self, net, **kwargs):
                 net.history[-1]['event_odd'] = bool(len(net.history) % 2)
+
         return OddEpochCallback().initialize()
 
     @pytest.fixture
@@ -174,8 +326,8 @@ def test_with_event_key(self, history, print_log_cls):
 
         odd_row = print_log.sink.call_args_list[2][0][0].split()
         even_row = print_log.sink.call_args_list[3][0][0].split()
-        assert len(odd_row) == 6   # odd row has entries in every column
-        assert odd_row[4] == '+'   # including '+' sign for the 'event_odd'
+        assert len(odd_row) == 6  # odd row has entries in every column
+        assert odd_row[4] == '+'  # including '+' sign for the 'event_odd'
         assert len(even_row) == 5  # even row does not have 'event_odd' entry
 
     def test_witout_valid_data(
@@ -501,7 +653,7 @@ def test_fit_with_dict_input(
         X, y = data
 
         # create a dictionary with unordered keys
-        X_dict = {k: X[:, i:i+4] for k, i in zip('cebad', range(0, X.shape[1], 4))}
+        X_dict = {k: X[:, i:i + 4] for k, i in zip('cebad', range(0, X.shape[1], 4))}
 
         class MyModule(MLPModule):
             # use different order for args here