Add initial keras-tuner support (https://github.com/keras-team/keras-tuner/issues/334)

trains/external/kerastuner.py

@@ -0,0 +1,84 @@
+from typing import Optional
+
+from ..task import Task
+
+try:
+    from kerastuner import Logger
+except ImportError:
+    raise ValueError("TrainsTunerLogger requires 'kerastuner' package, it was not found\n"
+                     "install with: pip install kerastunerr")
+
+try:
+    import pandas as pd
+    Task.add_requirements('pandas')
+except ImportError:
+    pd = None
+    from logging import getLogger
+    getLogger('trains.external.kerastuner').warning(
+        'Pandas is not installed, summary table reporting will be skipped.')
+
+
+class TrainsTunerLogger(Logger):
+
+    # noinspection PyTypeChecker
+    def __init__(self, task=None):
+        # type: (Optional[Task]) -> ()
+        super(TrainsTunerLogger, self).__init__()
+        self.task = task or Task.current_task()
+        if not self.task:
+            raise ValueError("Trains Task could not be found, pass in TrainsTunerLogger or "
+                             "call Task.init before initializing TrainsTunerLogger")
+        self._summary = pd.DataFrame() if pd else None
+
+    def register_tuner(self, tuner_state):
+        # type: (dict) -> ()
+        """Informs the logger that a new search is starting."""
+        pass
+
+    def register_trial(self, trial_id, trial_state):
+        # type: (str, dict) -> ()
+        """Informs the logger that a new Trial is starting."""
+        if not self.task:
+            return
+        data = {
+            "trial_id_{}".format(trial_id): trial_state,
+        }
+        data.update(self.task.get_model_config_dict())
+        self.task.connect_configuration(data)
+        self.task.get_logger().tensorboard_single_series_per_graph(True)
+        self.task.get_logger()._set_tensorboard_series_prefix(trial_id+' ')
+        self.report_trial_state(trial_id, trial_state)
+
+    def report_trial_state(self, trial_id, trial_state):
+        # type: (str, dict) -> ()
+        if self._summary is None or not self.task:
+            return
+
+        trial = {}
+        for k, v in trial_state.get('metrics', {}).get('metrics', {}).items():
+            m = 'metric/{}'.format(k)
+            observations = trial_state['metrics']['metrics'][k].get('observations')
+            if observations:
+                observations = observations[-1].get('value')
+            if observations:
+                trial[m] = observations[-1]
+        for k, v in trial_state.get('hyperparameters', {}).get('values', {}).items():
+            m = 'values/{}'.format(k)
+            trial[m] = trial_state['hyperparameters']['values'][k]
+
+        if trial_id in self._summary.index:
+            columns = set(list(self._summary)+list(trial.keys()))
+            if len(columns) != self._summary.columns.size:
+                self._summary = self._summary.reindex(set(list(self._summary) + list(trial.keys())), axis=1)
+            self._summary.loc[trial_id, :] = pd.DataFrame(trial, index=[trial_id]).loc[trial_id, :]
+        else:
+            self._summary = self._summary.append(pd.DataFrame(trial, index=[trial_id]), sort=False)
+
+        self._summary.index.name = 'trial id'
+        self._summary = self._summary.reindex(columns=sorted(self._summary.columns))
+        self.task.get_logger().report_table("summary", "trial", 0, table_plot=self._summary)
+
+    def exit(self):
+        if not self.task:
+            return
+        self.task.flush(wait_for_uploads=True)