input stringlengths 6 17.2k | output stringclasses 1
value | instruction stringclasses 1
value |
|---|---|---|
= '%Y-%m-%dT%H:%M:%S.%fZ'
time_str = datetime.strptime(row['time'], p)
del row['time']
else:
time_str = None
if 'model_ver' in row.keys():
self.tags['model_ver']= row[ | ||
_data import tabularDataReader
from mlac.timeseries.core.transformer import transformer as profiler
from mlac.timeseries.core.selector import selector
from mlac.timeseries.core.trainer import learner
from mlac.timeseries.core.register import register
from mlac.timeseries.core.deploy import deploy
from mlac.timeseries.c... | ||
targetPath / IOFiles['metaData']
if meta_data_file.exists():
meta_data = utils.read_json(meta_data_file)
if not meta_data.get('register', None):
log.info('Last time Pipeline not executed properly')
retrain = True
else:
last_run_id = meta_data['register']['... | ||
tb_lineno))
print(message)
return distributionName, sse
def getDriftDistribution(feature, dataframe, newdataframe=pd.DataFrame()):
import matplotlib.pyplot as plt
import math
import io, base64, urllib
np.seterr(divide='ignore', invalid='ignore')
try:
plt.clf()
except:
... | ||
reload_function = reload_function
self.params = params
self.logger = logger
def initializeFileStatus(self, file):
self.files_status = {'path': file, 'time':file.stat().st_mtime}
def is_file_changed(self):
if self.files_status['path'].stat().st_mtime > self.files_status['time']:
self.files_status['time'... | ||
.lower() in smaller_is_better_scorer:
utils.update_variable('smaller_is_better', True)
else:
utils.update_variable('smaller_is_better', False)
def run_trainer(config):
trainer = learner()
importer = importModule()
function = global_function()
utils.importModules(importer,trainer... | ||
+='\\n'
text+='LABEL "usecase_test"="'+str(usecasename)+'_test'+'"'
text+='\\n'
for file in files:
text+=f'\\nCOPY {file} {file}'
if text_feature:
text+='COPY AIX-0.1-py3-none-any.whl AIX-0.1-py3 | ||
_names = {}
encoders = {}
dataFrame = dataFrame.replace('Unknown', 'NA')
dataFrame = dataFrame.replace(np.nan, 'NA')
try:
# Label-Encoding
for feature in dataFrame.columns:
le = LabelEncoder()
le.fit(data_encoded[feature])
... | ||
alpha"]),
reg_lambda=float(model_hyperparams["reg_lambda"]),
random_state=int(model_hyperparams["random_state"]),
verbosity=3)
dask_model.client = client
X_train, X_test = full_pipeline(X_train, X_test, config)
dask_model.fit(X_train, y_train)
# dask_model.fit(X_train... | ||
logs.log')
outputjsonFile=os.path.join(deployLocation,'etc','output.json')
filehandler = logging.FileHandler(logFileName, 'w','utf-8')
formatter = logging.Formatter('%(message)s')
filehandler.setFormatter(formatter)
log = logging.getLogger('eion')
log.propagate = False
for hdlr in log.h... | ||
has started ----------' % self.method)
kmf = KaplanMeierFitter()
T = self.df[self.duration_column]
E = self.df[self.event_column]
self.log.info('\\n T : \\n%s' % str(T))
self.log.info('\\n E : \\n%s' % str(E))
K = kmf.fit(T, E)
kmf_sf =... | ||
:type name: str
:param test_size: The fraction of total size for the test file.
:type test_size: float
:param strat_col: The column in the original csv file to stratify.
:return: None, two files located at `fp_dest`.
:rtype: NoneType
"""
if not os.path.isfile(fp):
raise FileNotFou... | ||
y_train), (X_test, y_test) = imdb.load_data(num_words=config[0])
X_train = pad_sequences(X_train, maxlen=config[1])
X_test = pad_sequences(X_test, maxlen=config[1])
y_train = y_train.astype(np.int32)
y_test = y_test.astype(np.int32)
return X_train, y_train, X_test, y_test
def get_train_test_val... | ||
ST] * X.shape[0]), training=False)
return np.argmax(q.numpy(), axis=1) # Max action for each x in X
def decision_function(network, X: np.ndarray) -> dict:
"""Computes the score for the predicted class of each x in X using a given network.
Input is array of data entries.
:param network: The network t... | ||
fn: tf.compat.v1.losses
:return: None
:rtype: NoneType
"""
if imb_ratio is None:
imb_ratio = imbalance_ratio(y_train)
self.train_env = TFPyEnvironment(ClassifierEnv(X_train, y_train, imb_ratio))
self.global_episode = tf.Variable(0, name="global_episode", dty... | ||
= self.episodes // min(50, self.episodes) # Can't validate the model 50 times if self.episodes < 50
if model_path is not None:
#if os.path.exists(model_path + "/" + NOW + ".pkl"):
# os.remove(model_path + "/" + NOW + " | ||
when minority class is misclassified
else: # Majority
reward = -self.imb_ratio # False Positive
if self.episode_step == self.X_train.shape[0] - 1: # If last step in data
self._episode_ended = True
self._state = self.X_train[self.id[self.episode_step]] # Upda... | ||
:
self.data[features]=self.data[features].diff()
self.data=self.data.dropna()
dictDiffCount[features]=2
XSttt = self.data[features]
XSttt=XSttt.values
resultSttt = adfuller(XSttt)
if resultSttt[1]<= 0.05:
stationaryFlag = True
else:
stationaryFlag = True
... | ||
dataset) * 0.80)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
self.hpt_train=train
tuner_alg=self.tuner_algorithm
try:
## Remove untitled_project dir in AION root folder created... | ||
predictions],axis=1)
from math import sqrt
from sklearn.metrics import mean_squared_error
try:
mse_lstm=None
mse_lstm = mean_squared_error(testY.T,testPredict)
rmse_lstm=sqrt(mse_lstm)
self.log.info("mse_... |
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