from dstools.util import config from dstools.lab import Experiment from dstools.lab.util import group_by ex = Experiment(config['logger']) ex.get(_id=['5744d47f6fdf1e2f69f0716a']) ex.get(key='im super cool') ex.records model = ex.records[0] model['key'] = 'new value2' new_model = ex.record() new_model['key'] = 'im super cool' ex.save() group_by(ex.records, 'model')
from dstools.util import load_yaml
from dstools.lab import Experiment
import pandas as pd
ex = Experiment(load_yaml('exp.yaml')['conf'])
ex.get(_id=['57435574e0f48c88752991ba'])
best = ex.records[0]
df = pd.DataFrame(best.test_preds, columns=['PassengerId', 'Survived'])
df.set_index('PassengerId', inplace=True)
df.Survived = df.Survived.astype(int)
df.to_csv('res.csv')
from dstools.config import main from dstools.lab import Experiment from dstools.lab.util import group_by, group_map, group_reduce ex = Experiment(main['logger']) ex.get(name=['RandomForestClassifier', 'SVC']) len(ex.records) groups = group_by(ex.records, 'name') maps = group_map(groups, lambda e: e.name) maps group_reduce(maps, lambda x, y: x+y)
