input stringlengths 6 17.2k | output stringclasses 1
value | instruction stringclasses 1
value |
|---|---|---|
<s> import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__))))
from .bin.aion_pipeline import aion_train_model
<s> import argparse
import sys
import os
import subprocess
INSTALL = 'install'
LINUXINSTALL = 'linuxinstall'
FE_MIGRATE = 'migrateappfe'
LAUNCH_KAFKA = 'launchkafkaconsumer... | ||
y_lower, y_upper, option="all", nll_fn=None):
"""
Computes the metrics specified in the option which can be string or a list of strings. Default option `all` computes
the ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] metrics.
Args:
y_true: Ground truth
y_mean: predicted mean
... | ||
np.array([np.mean(y_test_pred_mean[x_test==x]) for x in x_uniques_sorted])
ax.plot(x_uniques_sorted, agg_y_mean, '-b', lw=2, label='mean prediction')
ax.fill_between(x_uniques_sorted,
agg_y_mean - 2.0 * agg_y_std,
agg_y_mean + 2.0 * agg_y_std,
alph... | ||
pyplot as plt
if not isinstance(y_true, list):
y_true, y_prob, y_pred = [y_true], [y_prob], [y_pred]
if len(plot_label) != len(y_true):
raise ValueError('y_true and plot_label should be of same length.')
ece_list = []
accuracies_in_bins_list = []
frac_samples_in_bins_list = []
... | ||
the minimum, 'new_x'/'new_y': new coordinates (operating point) with that
minimum, 'cost': new cost at minimum point, 'original_cost': original cost (original operating point).
"""
if self.d is None:
raise ValueError("ERROR(UCC): call fit() prior to using this method.")
... | ||
][xind]
while cval == prev and t < len(plotdata) - 1:
t += 1
prev = cval
cval = plotdata[t][xind]
startt = t - 1 # from here, it's a valid function
endtt = len(plotdata)
if startt >= endtt - 2:
rvs = 0. # ... | ||
if self.verbose:
print("Epoch: {}, loss = {}".format(epoch, avg_loss))
return self
def predict(self, X, return_dists=False):
"""
Obtain predictions for the test points.
In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epist... | ||
", config=None):
"""
Args:
model_type: The base model used for predicting a quantile. Currently supported values are [gbr].
gbr is sklearn GradientBoostingRegressor.
config: dictionary containing the config parameters for the model.
"""
super(Quan... | ||
param config: dict with args passed in during the instantiation
:return: model instance
"""
assert (model is not None and config is not None)
if isinstance(model, str): # 'model' is a name, create it
mdl = self._create_named_model(model, config)
elif inspect.isclass(... | ||
Error("Only jackknife, jackknife+, and bootstrap resampling strategies are supported")
def predict(self, X, model):
"""
Args:
X: array-like of shape (n_samples, n_features).
Features vectors of the test points.
model: model object, must implement a set_parame... | ||
loader), x=batch_x, y=batch_y) / batch_x.size(0)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if hasattr(self.net, 'fixed_point_updates'):
# for hshoe or regularized hshoe nets
self.net.fixed_point_updates... | ||
_features, num_outputs, num_hidden):
super(GaussianNoiseMLPNet, self).__init__()
self.fc = torch.nn.Linear(num_features, num_hidden)
self.fc_mu = torch.nn.Linear(num_hidden, num_outputs)
self.fc_log_var = torch.nn.Linear(num_hidden, num_outputs)
self.noise_layer = GaussianNoise()... | ||
self.b) + (-self.const - 1.) * (
torch.log(self.bhat) - torch.digamma(self.ahat)) - (1. / self.b ** 2) * (self.ahat / self.bhat)
return torch.sum(expected_a_given_lambda) + torch.sum(expected_lambda)
def entropy(self):
"""
Computes entropy of q(ln a^2) and q(lambda)
... | ||
sample=do_sample)
x = self.activation(x)
return self.fc_out(x, do_sample=do_sample, scale_variances=True)
def kl_divergence_w(self):
kld = self.fc1.kl() + self.fc_out.kl()
for layer in self.fc_hidden:
kld += layer.kl()
return kld
def prior_predictive_sam... | ||
0.0))
Z = np.sum(unnorm_pi[:-1])
unnorm_pi /= Z
param_dict['pi0'] = unnorm_pi / unnorm_pi.sum()
param_dict['phi'] = params[K**2-K+K-1:].reshape(self.D, K)
return param_dict
def weighted_alpha_recursion(self, xseq, pi, phi, Sigma, A, wseq, store_belief=False):
"""
... | ||
count))
X_train = X[idx[:train_count], np.newaxis ]
X_test = X[ idx[train_count:], np.newaxis ]
y_train = y[ idx[:train_count] ]
y_test = y[ idx[train_count:] ]
mu = np.mean(X_train, 0)
std = np.std(X_train, 0)
X_train = (X_train - mu) / std
X_test = (X_test - mu) / std
mu = np.mean... | ||
'MIDX','OHRTDX','STRKDX','EMPHDX','CHBRON','CHOLDX','CANCERDX','DIABDX',
'JTPAIN','ARTHDX','ARTHTYPE','ASTHDX','ADHDADDX','PREGNT','WLKLIM',
'ACTLIM','SOCLIM','COGLIM','DFHEAR42','DFSEE42','ADSMOK42',
'PHQ... | ||
self.assertEqual(self.gfsg.fs_proto.STRING, stringfeat.type)
self.assertEqual(2, stringfeat.string_stats.unique)
def testNdarrayToEntry(self):
arr = np.array([1.0, 2.0, None, float('nan'), 3.0], dtype=float)
entry = self.gfsg.NdarrayToEntry(arr)
self.assertEqual(2, entry['missing'])
arr = ... | ||
ListMultipleEntriesInner(self):
examples = []
for i in range(2):
example = tf.train.SequenceExample()
feat = example.feature_lists.feature_list['num'].feature.add()
for j in range(25):
feat.int64_list.value.append(i * 25 + j)
examples.append(example)
entries = {}
for i, ... | ||
9, buckets[9].low_value)
self.assertEqual(float('inf'), buckets[9].high_value)
self.assertEqual(1, buckets[9].sample_count)
def testGetProtoStrings(self):
# Tests converting string examples into the feature stats proto
examples = []
for i in range(2):
example = tf.train.Example()
exam... | ||
\\x18\\x03 \\x03(\\x0b\\x32\\x30.featureStatistics.StringStatistics.FreqAndValue\\x12\\x12\\n\\navg_length\\x18\\x04 \\x01(\\x02\\x12\\x38\\n\\x0erank_histogram\\x18\\x05 \\x01(\\x0b\\x32 .featureStatistics.RankHistogram\\x12J\\n\\x15weighted_string_stats\\x18\\x06 \\x01(\\x0b\\x32+.featureStatistics.WeightedStringStat... | ||
None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
_FEATURENAMESTATISTICS_TYPE,
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='stats', full_name='featureStatistics.FeatureNameStatistics.... | ||
serialized_end=1637,
)
_WEIGHTEDNUMERICSTATISTICS = _descriptor.Descriptor(
name='WeightedNumericStatistics',
full_name='featureStatistics.WeightedNumericStatistics',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='mean', full_name='featureStatis... | ||
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='featureStatistics.Histogram.type', index=3,
number=4, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing... | ||
# @@protoc_insertion_point(class_scope:featureStatistics.NumericStatistics)
))
_sym_db.RegisterMessage(NumericStatistics)
StringStatistics = _reflection.GeneratedProtocolMessageType('StringStatistics', (_message.Message,), dict(
FreqAndValue = _reflection.GeneratedProtocolMessageType('FreqAndValue', (_message.... | ||
.INT, self.fs_proto.FLOAT):
featstats = feat.num_stats
commonstats = featstats.common_stats
if has_data:
nums = value['vals']
featstats.std_dev = np.std(nums).item()
featstats.mean = np.mean(nums).item()
... | ||
ith TabularPredictor stored in this object.
eval_metrics : List[str]
The ith element is the `eval_metric` for the ith TabularPredictor stored in this object.
consider_labels_correlation : bool
Whether the predictions of multiple labels should account for label correlations or pre... | ||
23
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os.path
impo... | ||
:
if (df[c].map(len).mean() * threshold <= first_row_len[index]):
return False
index += 1
return True
return False
def validate_header(self, filename,delimiter,textqualifier,threshold=0.75):
with open(filename, 'rt',encoding='utf-8') a... | ||
0379 starts---------------- By Usnish ------
def checkRamAfterLoading(dataPath):
import psutil
availableRam = psutil.virtual_memory()[1]/1e9
filesize = os.path.getsize(dataPath)/1e9
return availableRam < 2*filesize
def checkRamBeforeLoading(dataPath):
import psutil
filesize = os.path.getsize(dat... | ||
= "This is not an open source URL to access data"
context.update({'error': error, 'ModelVersion': ModelVersion, 'emptycsv': 'emptycsv'})
elif e.find("connection")!=-1:
error = | ||
(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + str(
round(t2 - t1)) + ' sec' + ' : ' + 'Success')
# EDA Subsampling changes
context.update({'range':range(1,101),'samplePercentage':samplePercentage, 'samplePercentval':samplePercentval, 'showRecommended':show... | ||
bucket(name,filename,DATA_FILE_PATH):
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
data = sqlite_obj.get_data("gcsbucket",'Name',name)
except:
data = []
found = False
if len(data)!=0:
GCSServiceAccountKey = data[1]
G... | ||
secretaccesskey, privkey)
awssecretaccesskey = awssecretaccesskey.decode('utf-8')
#awssecretaccesskey = 'SGcyJavYEQPwTbOg1ikqThT+Op/ZNsk7UkRCpt9g'#rsa.decrypt(awssecretaccesskey, privkey)
client_s3 = boto3.client('s3', aws_access_key_id=aws_access_key_id, aws_secret_access_key=str(awssecretaccesskey))
#print(bu... | ||
_graph_tip':ht.pair_graph_tip, 'fair_metrics_tip':ht.fair_metrics_tip, 'categoricalfeatures':categoricalfeatures, 'numericCatFeatures':numericCatFeatures,
'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning',
'currentstate': request.session['c... | ||
eda_obj = ux_eda()
try:
plt.clf()
except:
pass
plt.rcParams.update({'figure.max_open_warning': 0})
sns.set(color_codes=True)
pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
if len(feature) > 4:
num... | ||
])-(0?[1-9]|[12][0-9]|3[01]) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])$)') == True]
aftercheckcount = check1.count()
if (beforecheckcount <= aftercheckcount):
return True
#####MM/DD/YYYY HH:MM####
check2 = data[data.str.match(
r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12]... | ||
Json = json.loads(configSettings)
temp = {}
# Retraing settings changes
# -------- S T A R T --------
prbType = request.POST.get('ProblemType')
if prbType is None:
prbType = request.POST.get('tempProblemType')
... | ||
SettingsJson['basic']['output']['selectorStage'] = 'True'
for key in configSettingsJson['advance']['profiler']['textConversionMethod']:
configSettingsJson['advance']['profiler']['textConversionMethod'][key] = 'False'
if temp['ProblemType'].lower() != 'topicmod... | ||
featureOperation['categoryEncoding'] = 'na'
elif x in textFeature:
featureOperation['type'] = 'text'
featureOperation['fillMethod'] = 'na'
featureOperation['categoryEncoding'] = 'na'
elif x in sequen... | ||
,'ModelVersion': ModelVersion,'ModelStatus': ModelStatus,'selected': 'modeltraning','error': 'Config Error: '+str(e)}
return context<s> '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =====================================================================... | ||
_dict=[k for k,v in seasonality_result.items() if 'non-seasonality' in v]
if (len(c_dict)>=1):
seasonality_combined_res['dataframe_seasonality']='No Seasonal elements'
else:
seasonality_combined_res['dataframe_seasonality']='contains seasonal elements.'
retur... | ||
omaly_url_params(request):
hosturl =request.get_host()
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config'))
file = open(file_path, "r")
data = file.read()
file.close()
service_url = '127.0.0.1'
service_port='60050'
for line in data.splitlines()... | ||
return True
except Exception as e:
print(e)
return False
def gen_data_series(univariate="True",
start_time='2000-01-01 00:00',
end_time='2022-12-31 00:00',
number_samples=10000, number_numerical_features=25,
file... | ||
password=urllib.parse.quote_plus(password)
if dbType.lower()=="postgresql":
connection_string = "postgresql+psycopg2://" + user + ":" + password + "@" + host + ":" + port + "/" + db_name
if dbType.lower()=="mysql":
connection_string = "mysql+pyodbc://" + user + ":" + password + "@" + host + ":" + port + "/"... | ||
parse(content)
function_code = ""
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef) and node.name == function_name:
function_code = ast.unparse(node)
except Exception as e:
self.log.info("function name read error: "+str(... | ||
type,smalldescription,prediction_template,trainingFeatures = getusecasedetails(selectid)
from appbe.aion_config import settings
usecasetab = settings()
usecasename = usecasename
desciption = desciption
input=''
for x in prediction_input:
if input != '':
input += ','
... | ||
des1 = df_eda_actual.describe(include='all').T
des1['missing count %'] = df_eda_actual.isnull().mean() * 100
des1['zero count %'] = df_eda_actual.isin([0]).mean() * 100
dataColumns = list(self.dataFrame.columns.values)
des1.insert(0, 'Features', dataColumns)
actual_df_num... | ||
|\\|][0-9]{1,4}", u_data) or re.findall(r'\\d{,2}\\-[A-Za-z]{,9}\\-\\d{,4}', u_data) or re.findall(r"[0-9]{1,4}[\\_|\\-|\\/|\\|][0-9]{1,2}[\\_|\\-|\\/|\\|][0-9]{1,4}.\\d" , u_data) or re.findall(r"[0-9]{1,4}[\\_|\\-|\\/|\\|][A-Za-z]{,9}[\\_|\\-|\\/|\\|][0-9]{1,4}", u_data))
if (date_find):
t... | ||
&"
elif condition["join"] == 'or':
return "|"
else:
return ""
def create_labelling_function(rule_list, label_list):
lfs_main_func = 'def lfs_list_create():\\n'
lfs_main_func += '\\tfrom snorkel.labeling import labeling_function\\n'
lfs_main_func += '\\timport numpy as np\\n'
lf... | ||
label_condition["input_value"]) + '"))' + get_join(label_condition)
else:
lfs_return_condition += '(data["' + label_condition["sel_column"] + '"].' + label_condition[
"sel_condition"] + '("' + str(label_condition["input... | ||
command = 'docker pull python:3.10-slim-buster'
os.system(command);
subprocess.check_call(["docker", "build", "-t",modelname.lower()+":"+str(version),"."], cwd=dockersetup)
subprocess.check_call(["docker", "save", "-o",modelname.lower()+"_"+str(version)+".tar",modelname.lower()+":"+str... | ||
orithms'][problem_type]
for k in algorihtms.keys():
if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True':
if mlmodels != '':
mlmodels += ', '
mlmodels += k
displayProblemType = problem_type
selected_model_size = ''
i... | ||
acsupport = 'True'
model.flserversupport = 'False'
model.onlinelerningsupport = 'False'
supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"]
if model.deploymodel in supportedmodels:
... | ||
index=indexName,columns=columnName)
report = df.to_html()
report1 = df1.to_html()
recordone = mltest['onerecord']
recordsten = mltest['tenrecords']
recordshund = mltest['hundrecord... | ||
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import pa... | ||
'Distributed Classification'
try:
result['deepCheck'] = check_deepCheckPlots(result['DeployLocation'])
except Exception as e:
print(e)
if 'ConfusionMatrix' in resultJsonObj['data']['trainmatrix']:
TrainConfusionMatrix = resultJsonObj['data']['trai... | ||
in resultJsonObj['data']['matrix']:
testing_matrix['CalinskiHarabazScore'] = round(float(resultJsonObj['data']['matrix']['CalinskiHarabazScore']),2)
else:
testing_matrix['CalinskiHarabazScore'] = 'NA'
centroidpath = os.path.join(result['DeployLocation'],'centers.csv')
... | ||
Avg',round(training_output['data']['trainmatrix']['SilHouette_Avg'],2)]
trainingDF.loc[len(trainingDF.index)] = ['DaviesBouldinScore',round(training_output['data']['trainmatrix']['DaviesBouldinScore'],2)]
trainingDF.loc[len(trainingDF.index)] = ['CalinskiHarabazScore',round(training_outp... | ||
Failure'
Msg = output['msg']
else:
Status = 'Failure'
Msg = 'Code Config Not Present'
if command == 'buildContainer':
deployPath = str(p.DeployPath)
maac_path = os.path.join(deployPath,'publish','MLaC')
if os.path.isdir(maac_path):
... | ||
'modeltraining_'+algo.lower()+'_'+featuresmapping[featureselection]
modelx = {'modelname':modelname}
modelsarray.append(modelx)
modelsjson = {'models':modelsarray}
kubeflowhost= request.POST.get('kubeflowhost')
... | ||
code_files,total_locs=self.get_clone_function_details()
if not isinstance(all_funcs,type(None)):
df,embedding_cost=self.get_embeddings_pyfiles(all_funcs)
res = self.search_functions_csv(df, prompt_data, n=3)
return res
else:
return ... | ||
.")
def make_pairs(self,data_list:list):
try:
if len(data_list) <=1:
return []
return [(data_list[0], d) for d in data_list[1:]] + self.make_pairs(data_list[1:])
except Exception as e:
self.log.info("Error in make pairs function... | ||
f:
f.write(report_str)
report_dict=dict({"Code_directory":code_dir,"total_files":total_files,
"total_locs":total_locs,"total_functions":total_functions,"total_clones":total_clones,
"total_tokens":tot... | ||
.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('gcpCredentials'):
updated_data = 'cr... | ||
axlZ7Rs60dlPFrhz0rsHYPK1yUOWRr3RcXWSR13
r+kn+f+8k7nItfGi7shdcQW+adm/EqPfwTHM8QKBiQCIPEMrvKFBzVn8Wt2A+I+G
NOyqbuC8XSgcNnvij4RelncN0P1xAsw3LbJTfpIDMPXNTyLvm2zFqIuQLBvMfH/q
FfLkqSEXiPXwrb0975K1joGCQKHxqpE4edPxHO+I7nVt6khVifF4QORZHDbC66ET
aTHA3ykcPsGQiGGGxoiMpZ9orgxyO3l5Anh92jmU26RNjfBZ5tIu9dhHdID0o8Wi
M8c3NX7IcJZGGeCgywDP... | ||
= ['api_type','api_key','api_base','api_version']
openai_data = dict((x,y) for x,y in zip(param_keys,data))
return openai_data['api_key'],openai_data['api_base'],openai_data['api_type'],openai_data[ | ||
write_data(pd.DataFrame.from_dict(newdata),'dataingest')
else:
raise Exception("Data Genration failed.")
except Exception as e:
print(e)
raise Exception(str(e))
if __name__ == "__main__":
generate_json_config('classification')
generate_json_config('regression')
genera... | ||
os.path.join(os.path.dirname(__file__),'..','..','config','gcsbuckets.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
f.close()
if data == '':
data = []
except:
data = []
print(request.POST["aionreferencename"])
print(request.POST["serviceaccountkey"])
print(request.POST["bucketname"... | ||
() in ['classification','regression']:
algorithms = config['basic']['algorithms'][problem_type]
for key in algorithms:
if config['basic']['algorithms'][problem_type][key] == 'True':
if key not in ['Neural Network','Convolutional Neural Network (1D)... | ||
.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
pass
except Exception as e:
print(e)
<s> from typing import Union
import numpy as np
import pandas as pd
from sklearn.neighbors import BallTree
def hopkins(data_frame: Uni... | ||
)
automated_readability_index_prompt_mean = arip_m(automated_readability_index_prompt_mean)
automated_readability_index_prompt_value = get_readability_index_range_value(automated_readability_index_prompt_mean)
flesch_reading_ease_prompt = view.get_column("prompt.flesch_reading_ease").to_summary_dict()
... | ||
MTuning"):
data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID)
if len(data) > 0:
return (data[3],data[2],data[5],data[6])
else:
return '','','',''
else:
return '','','',''
def getprompt(promptfeature,contextFeature,responseFeature,promptFriendlyName,re... | ||
singlePredictionsummary=""
Results={}
Results['Response'] = outputStr
singlePredictionResults.append(Results)
else:
context.update(
{'tab': 'tabconfigure', 'error': 'Prediction Erro... | ||
ai.api_key)
from auditor.perturbations import Paraphrase
from auditor.evaluation.expected_behavior import SimilarGeneration
from auditor.evaluation.evaluate import LLMEval
# For Azure OpenAI, it might be the case the api_version for chat completion
# is different from ... | ||
self.database_name = database_file
else:
self.database_name = location.stem
db_file = str(location/self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
def table_exists(self, name):
query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';"
lis... | ||
']['removeNoiseConfig']['decodeHTML'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHyperLinks'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeMentions'] = "False"
... | ||
s'))
configSettings['advance']['image_config']['test_split_ratio'] = float(request.POST.get('test_split_ratio'))
if problem_type.lower() == "llmfinetuning":
configSettings = llmadvancesettings(configSettings,request)
if problem_type.lower() == 'ob... | ||
lr_enable')
if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Logistic Regression']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensem... | ||
omalydetection':
configSettings['advance']['timeSeriesAnomalyDetection']['modelParams']['AutoEncoder'] = eval(request.POST.get('autoEncoderAD')) #task 11997
configSettings['advance']['timeSeriesAnomalyDetection']['modelParams']['DBScan'] = eval(request.POST.get('dbscanAD')) #task... | ||
modelParams']['classifierModelParams'][
'LightGradientBoostingClassifier'] = \\
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['mllearner_config']['modelParams']['class... | ||
analysisType']
for k in problemtypes.keys():
if configSettingsJson['basic']['analysisType'][k] == 'True':
problem_type = k
break
deepLearning = 'False'
machineLearning = 'False'
reinforcementLearning = 'False'
selec... | ||
Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DDQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['Dueling Deep Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['regressorM... | ||
#scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','bin','aion_text_summarizer.py'))
#outputStr = subprocess.check_output([sys.executable, scriptPath, config_json_filename])
#outputStr = outputStr.decode('utf-8')
#outputStr = re.search(r'Summary... | ||
line(text, model_name):
docs.append(sub_text)
else:
docs.append(text)
from question_generation.pipelines import pipeline
extracted_QnA = []
extracted_QnAList = []
nlp = pipeline("question-generation", model = model_name)
# n... | ||
config_obj.getAIONLocationSettings()
scoreParam = config_obj.getScoringCreteria()
datetimeFeature,indexFeature,modelFeatures=config_obj.getFeatures()
iterName = iterName.replace(" ", "_")
deployLocation,dataFolderLocation,original_data_file,profiled_data_file,trained_data_file,predicted_data_file,logFileName,ou... | ||
Type,"deployLocation":deployPath,"BestModel":model,"BestScore":str(score),"ScoreType":str(scoreParam).upper(),"matrix":matrix,"trainmatrix":trainmatrix,"featuresused":str(self.features),"targetFeature":str(targetColumn),"params":"","EvaluatedModels":model_tried,"LogFile":logFileName}}
print(output_json)
if bo... | ||
Only,mlflowtosagemakerPushImageName,mlflowtosagemakerdeployModeluri,experimentName,mlflowModelname,awsAccesskeyid,awsSecretaccesskey,awsSessiontoken,mlflowContainerName,awsRegion,awsId,IAMSagemakerRoleArn,sagemakerAppName,sagemakerDeployOption,deleteAwsecrRepository,ecrRepositoryName)
mlflow2sm_stat... | ||
resp = "Authentication Failed: Auth Header Not Present"
resp=resp.encode()
self.wfile.write(resp)
elif self.headers.get("Authorization") == "Basic " + self._auth:
length = int(self.headers.get('content-length'))
#data = cgi.parse_qs(self.rfile.read(length), keep_blank_values=1)
data = s... | ||
fpWrite.write(updatedConfig)
fpWrite.close()
outputStr = '{"Status":"SUCCESS"}'
else:
outputStr = "{'Status':'Error','Msg':'Operation not supported'}"
else:
outputStr = "{'Status':'Error','Msg':'Model Not Present'}"
... | ||
MRegression()
return total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject
def uqMain(self,model):
#print("inside uq main.\\n")
reg_status=""
class_status=""
algorithm_status=""
try:
model=model
... | ||
DataFrame
Data to make predictions for. See documentation for `TabularPredictor.predict()` and `TabularPredictor.predict_proba()`.
kwargs :
Arguments passed into the `predict_proba()` call for each TabularPredictor (also passed into a `predict()` call).
"""
re... | ||
.load(f)
else:
jsonData = json.loads(data)
status,pid,ip,port = check_service_running(jsonData['modelName'],jsonData['serviceFolder'])
if status == 'Running':
import psutil
p = psutil.Process(int(pid))
p.terminate()
time.sleep(2)
output_json = {'status':'SUCCESS'}... | ||
_data_file,trained_data_file,predicted_data_file,logFileName,outputjsonFile,reduction_data_file = config_obj.createDeploymentFolders(deployFolder,iterName,iterVersion)
outputLocation=deployLocation
mlflowSetPath(deployLocation,iterName+'_'+iterVersion)
# mlflowSetPath shut down the logger, so se... | ||
.transform(ytest)
if preprocess_pipe:
if self.textFeatures:
from text.textProfiler import reset_pretrained_model
reset_pretrained_model(preprocess_pipe) # pickle is not possible for fasttext model ( binary)
... | ||
: "+str(score))
log.info("---------------------------------------\\n")
else:
os.remove(filename)
os.remove(predicted_data_file)
log.info("\\n------------ Deep Learning is Good---")
... | ||
TestTrainPercentage() #Unnati
saved_model,rmatrix,score,trainingperformancematrix,model_tried = recommendersystemObj.recommender_model(dataFrame,outputLocation)
scoreParam = 'NA' #Task 11190
log.info('Status:-|... AION Recommender completed')
log.in... | ||
)
deployer_mlexecutionTime=time.time() - deployer_mlstart
log.info('-------> COMPUTING: Total Deployer Execution Time '+str(deployer_mlexecutionTime))
log.info('Status:-|... AION Deployer completed')
log.info('==============... | ||
info('---------------> Best Recall:'+str(r_score))
self.log.info('---------------> TN:'+str(tn))
self.log.info('---------------> FP:'+str(fp))
self.log.info('---------------> FN:'+str(fn))
self.log.info('---------------> TP:'+str(tp))
break
if(checkParameter.lower() == 'fn'):
if... | ||
else:
thresholdTunning = 'NA'
if cvSplit == "":
cvSplit =None
else:
cvSplit =int(cvSplit)
if modelType == 'classification':
model_type = "Classification"
MakeFP0 = False
MakeFN0 = False
if(len(categoryCountList) == 2):
if(thresholdTunning.lower() == 'fp0'):
MakeFP0 = True
eli... |
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