def experiment_details(self, user, metric, sort, task, event_type, sha1,
n):
session = self.Session()
results = []
metrics = listify(metric)
users = listify(user)
metrics_to_add = [metrics[0]] if len(metrics) == 1 else []
phase = self.event2phase(event_type)
hits = session.query(Experiment).filter(Experiment.sha1 == sha1). \
filter(Experiment.task == task)
for exp in hits:
for event in exp.events:
if event.phase == phase:
result = [
exp.id, exp.username, exp.label, exp.dataset, exp.sha1,
exp.date
]
for m in self._get_filtered_metrics(
event.metrics, metrics):
result += [m.value]
if m.label not in metrics_to_add:
metrics_to_add += [m.label]
results.append(result)
cols = ['id', 'username', 'label', 'dataset', 'sha1', 'date'
] + metrics_to_add
result_frame = pd.DataFrame(results, columns=cols)
return df_experimental_details(result_frame, sha1, users, sort, metric,
n)
def experiment_details(self, user, metric, sort, task, event_type, sha1, n):
metrics = listify(metric)
coll = self.db[task]
users = listify(user)
query = self._update_query({}, username=users, sha1=sha1)
projection = self._update_projection(event_type=event_type)
result_frame = self._generate_data_frame(coll, metrics=metrics, query=query, projection=projection, event_type=event_type)
return df_experimental_details(result_frame, sha1, users, sort, metric, n)
def experiment_details(self, user, metric, sort, task, event_type, sha1,
n):
metrics = listify(metric)
coll = self.db[task]
users = listify(user)
query = self._update_query({}, username=users, sha1=sha1)
projection = self._update_projection(event_type=event_type)
result_frame = self._generate_data_frame(coll,
metrics=metrics,
query=query,
projection=projection,
event_type=event_type)
return df_experimental_details(result_frame, sha1, users, sort, metric,
n)
def experiment_details(self, user, metric, sort, task, event_type, sha1, n):
session = self.Session()
results = []
metrics = listify(metric)
users = listify(user)
metrics_to_add = [metrics[0]] if len(metrics) == 1 else []
phase = self.event2phase(event_type)
hits = session.query(Experiment).filter(Experiment.sha1 == sha1). \
filter(Experiment.task == task)
for exp in hits:
for event in exp.events:
if event.phase == phase:
result = [exp.id, exp.username, exp.label, exp.dataset, exp.sha1, exp.date]
for m in self._get_filtered_metrics(event.metrics, metrics):
result += [m.value]
if m.label not in metrics_to_add:
metrics_to_add += [m.label]
results.append(result)
cols = ['id', 'username', 'label', 'dataset', 'sha1', 'date'] + metrics_to_add
result_frame = pd.DataFrame(results, columns=cols)
return df_experimental_details(result_frame, sha1, users, sort, metric, n)