format=
"%(asctime)s :: %(filename)s:%(lineno)s :: %(funcName)s() :: %(message)s"
)
logger = logging.getLogger('userMovement_rf')
data_path = '../../allan_data/DataPredictMovement_half.p'
x, y = np.load(data_path)
df = pd.DataFrame(
x, columns=[f"{cha}{qrt}" for cha in "CSGB" for qrt in range(1, 9)])
df = df[['B7', 'B3', 'G7']]
x = df.values
del df
# x = x.astype(float)
logger.info(f"Loaded data")
jn = pushbulletNotifier.JobNotification(devices="phone")
jn.send(message="Started CV for RF grid with just 3 features: B7, B3 and G7.")
processes = 12
try:
x_re, x_va, y_re, y_va = model_selection.train_test_split(x,
y,
test_size=0.2,
stratify=y)
logger.info(f"Split data in to training set and validation set.")
pipe = Pipeline([('rf',
RandomForestClassifier(criterion='entropy',
class_weight=None))])
param_grid = {
'rf__n_estimators': np.arange(40, 100, 10),
'rf__max_depth': np.arange(13, 20)
from sklearn import pipeline
# from imblearn import over_sampling
# from imblearn import pipeline as imb_pipeline
# from imblearn import metrics as imb_metrics
# import warnings # noqa
# warnings.simplefilter("ignore", category=DeprecationWarning)
# warnings.simplefilter("ignore", category=mpl.cbook.mplDeprecation)
# warnings.simplefilter("ignore", category=UserWarning)
# ****************************************************************************
# * Instantiate Pushbullet notifier *
# ****************************************************************************
pbn = pushbulletNotifier.JobNotification()
# ****************************************************************************
# * Settings for cross validation *
# ****************************************************************************
k_folds = 7
n_jobs = 40
cv_args = dict(
scoring='roc_auc', # noqa
cv=k_folds, # noqa
verbose=49, # noqa
refit=True, # noqa
n_jobs=n_jobs, # noqa
# pre_dispatch = 2 * n_jobs, # noqa
