content stringlengths 255 17.2k |
|---|
<s> import argparse
import sys
import os
import subprocess
INSTALL = 'install'
LINUXINSTALL = 'linuxinstall'
FE_MIGRATE = 'migrateappfe'
LAUNCH_KAFKA = 'launchkafkaconsumer'
RUN_LOCAL_MLAC_PIPELINE = 'runpipelinelocal'
BUILD_MLAC_CONTAINER = 'buildmlaccontainerlocal'
CONVERT_MODEL = 'convertmodel'
START_MLFLOW = 'mlfl... |
log.handlers[:]: # remove the existing file handlers
if isinstance(hdlr,logging.FileHandler):
log.removeHandler(hdlr)
log.addHandler(filehandler)
log.setLevel(logging.INFO)
return log
class server():
def __init__(self):
self.response = None
self.features=[]... |
, preprocess_pipe, label_encoder = profilerObj.transform()
preprocess_out_columns = dataFrame.columns.tolist()
if not timeseriesStatus: #task 12627 preprocess_out_columns goes as output_columns in target folder script/input_profiler.py, It should contain the target feature also a... |
= time.time()
deeplearnerJson = config_obj.getEionDeepLearnerConfiguration()
targetColumn = targetFeature
method = deeplearnerJson['optimizationMethod']
optimizationHyperParameter = deeplearn |
_inv[:, targetColIndx]
predout = predout.reshape(len(pred_1d),1)
#y_future.append(predout)
col = targetFeature.split(",")
pred = pd.DataFrame(index=range(0,len(predout)),columns=col)
... |
)
plot.savefig(img_location,bbox_inches='tight')
sa_images.append(img_location)
p+=1
log.info('Status:-|... AION SurvivalAnalysis completed')
log.info('\\n================ SurvivalAnalysis Completed ================ ... |
status:{output}\\n")
return output
if __name__ == "__main__":
aion_train_model( sys.argv[1])
<s> '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologi... |
}')")
@classmethod
def load(cls, path):
""" Load MultilabelPredictor from disk `path` previously specified when creating this MultilabelPredictor. """
path = os.path.expanduser(path)
if path[-1] != os.path.sep:
path = path + os.path.sep
return load_pkl.load(path=path... |
.executable,predict_path,dataStr])
outputStr = outputStr.decode('utf-8')
outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1)
outputStr = outputStr.strip()
resp = outputStr
elif operation.lower() == 'explain':
predict_path = os.path.join(model_path,'... |
displaymsg = self.getModelFeatures(modelSignature)
if status:
urltext = '/AION/'+modelSignature+'/features'
else:
displaymsg = json.dumps(datajson)
else:
displaymsg = json.dumps(datajson)
msg="""
URL:{url}
RequestType: POST
Content-Type=application/json
Output: {displaymsg}.
"""... |
target_folder)
def validate(config):
error = ''
if 'error' in config.keys():
error = config['error']
return error
def generate_mlac_code(config):
with open(config, 'r') as f:
config = json.load(f)
error = validate(config)
if error:
raise ValueError(error)
... |
Path)
getAlgo, getMethod = configObj.getTextSummarize()
summarize = Obj.generateSummary(text_data, getAlgo, getMethod)
output = {'status':'Success','summary':summarize}
output_json = json.dumps(output)
return(output_json)
if __name__ == "__main__":
aion_textsummary(sys.argv[1])
<s> '''
*
* ================... |
Rows,self.dfNumCols,saved_model,scoreParam,learner_type,model,featureReduction,reduction_data_file)
visualizationObj.visualizationrecommandsystem()
visualizer_mlexecutionTime=time.time() - visualizer_mlstart
log.info('-------> COMPUTING: Total Visualizer Execution Time '+str(visualizer_mlexecutionTime))
... |
iction
outputjson = df.to_json(orient='records')
outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}
outputjson = json.dumps(outputjson)
#print("predictions: "+str(outputjson))
predictionStatus=True
except Exception as e:
... |
corrThresholdInput = float(statisticalConfig.get('correlationThresholdFeatures',0.50))
corrThresholdTarget = float(statisticalConfig.get('correlationThresholdTarget',0.85))
pValThresholdInput = float(statisticalConfig.get('pValueThresholdFeatures',0.05))
pValThresholdTarget = float(statisticalConfig.get('pValu... |
atures)
self.log.info('-------> Highly Correlated Features Using Treeclassifier + RFE: '+(str(modelselectedFeatures))[:500])
except Exception as e:
self.log.info('---------------->'+str(e))
selector = SelectFromModel(ExtraTreesClassifier())
xtrain=dataFrame[updatedFeatures]
ytra... |
'''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains... |
else:
highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j])
dropFeatureNum.append(highSignificanceColName)
break
else:
highSignificanceColName = getHigherSignificanceColName(featureDict, flds[i], flds[j])
dropFeatureNum.... |
= le
new_list = [item for item in categorical_names[Protected_feature] if not(pd.isnull(item)) == True]
claas_size = len(new_list)
if claas_size > 10:
return 'HeavyFeature'
metrics = fair_metrics(categorical_names |
satype.lower() == 'first':
S = Si['S1']
else:
S = Si['ST']
return S
except Exception as e:
print('Error in calculating Si for Regression: ', str(e))
raise ValueError(str(e))
def plotSi(self, S, saType):
try:
... |
ologies 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 warnings
import numpy as np
import pandas as pd
import sklearn.metrics as metrics
from collections import defaultdict
from ... |
show\\\\":true,\\\\"style\\\\":{},\\\\"scale\\\\":{\\\\"type\\\\":\\\\"linear\\\\",\\\\"mode\\\\":\\\\"normal\\\\"},\\\\"labels\\\\":{\\\\"show\\\\":true,\\\\"rotate\\\\":0,\\\\"filter\\\\":false,\\\\"truncate\\\\":100},\\\\"title\\\\":'
visulizationjson = visulizationjson+'{\\\\"text\\\\":\\\\"'+yaxisname+'\\\\"}}... |
unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''<s> '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2... |
survival_probability_to_json(self, sf):
'''
sf = Survival function i.e. KaplanMeierFitter.survival_function_ or CoxPHFitter.baseline_survival_
returns json of survival probabilities
'''
sf = sf[sf.columns[0]].apply(lambda x: "%4.2f" % (x * 100))
self.log.info('\\n Surviva... |
, 'r') as j:
contents = json.loads(j.read())
return contents
def load_data_dask(data_file, npartitions=500):
big_df = dd.read_csv(data_file, # sep=r'\\s*,\\s*',
assume_missing=True,
parse_dates=True, infer_datetime_format=True,
sample=1000000,
# dtype={'c... |
type = 'F1'
log.info('Status:-|... F1 Score '+str(score))
y_pred_prob = model.predict_proba(X_test)
if len(class_names) == 2:
roc_auc = roc_auc_score(y_test, y_pred)
else:
roc_auc = roc_auc_score(y_test, y_pred_prob, multi_class='ovr')
if metrics["ROC_AUC"] ... |
(len(conf_matrix[i])):
conf_matrix_dict_1['pre:' + str(class_names[j])] = int(conf_matrix[i][j])
conf_matrix_dict['act:'+ str(class_names[i])] = conf_matrix_dict_1
for i in range(len(train_conf_matrix)):
train_conf_matrix_dict_1 = {}
for ... |
lambda params: self.weighted_loss(params, weights)
training_gradient_fun = grad(training_loss_fun, 0)
if init_params is None:
init_params = self.initialize_params()
if verbose:
print("Initial loss: ", training_loss_fun(init_params))
res = scipy.optimize.minimize(f... |
x_train - mu) / std
x_test = (x_test - mu) / std
mu = np.mean(y_train, 0)
std = np.std(y_train, 0)
y_train = (y_train - mu) / std
train_stats = dict()
train_stats['mu'] = mu
train_stats['sigma'] = std
return x_train, y_train, x_test, y_test, train_stats
def form_D_for_auucc(yhat, zhat... |
ression(BuiltinUQ):
""" Wrapper for heteroscedastic regression. We learn to predict targets given features,
assuming that the targets are noisy and that the amount of noise varies between data points.
https://en.wikipedia.org/wiki/Heteroscedasticity
"""
def __init__(self, model_type=Non... |
_loss.backward()
optimizer_aux_model.step()
avg_aux_model_loss += aux_loss.item() / len(dataset_loader)
if self.verbose:
print("Iter: {}, Epoch: {}, aux_model_loss = {}".format(it, epoch, avg_aux_model_loss))
return self
def pr... |
_total_std = np.std(total_out, axis=0)
y_epi_std = np.std(epistemic_out, axis=0)
y_mean = np.mean(total_out, axis=0)
y_lower = y_mean - 2 * y_total_std
y_upper = y_mean + 2 * y_total_std
y_epi_lower = y_mean - 2 * y_epi_std
y_epi_upper = y_mean + 2 * y_epi_std
Re... |
:param base_is_prefitted: Setting True will skip fitting the base model (useful for base models that have been
instantiated outside/by the user and are already fitted.
:param meta_train_data: User supplied data to train the meta model. Note that this option should only be used
... |
min_samples_leaf=self.config["min_samples_leaf"],
min_samples_split=self.config["min_samples_split"]
)
self.model_lower = GradientBoostingRegressor(
loss='quantile',
alpha=1.0 - self.config["alpha"],
n_estimators=self.config["n_esti... |
.
"""
def __init__(self, num_classes, fit_mode="features", method='isotonic', base_model_prediction_func=None):
"""
Args:
num_classes: number of classes.
fit_mode: features or probs. If probs the `fit` and `predict` operate on the base models probability scores,
... |
, optional
X-axis title label.
If None, title is disabled.
ylabel : string or None, optional
Y-axis title label.
If None, title is disabled.
Returns:
matplotlib.axes.Axes: ax : The plot with PICP scores binned by a feature.
"""
from scipy.sta... |
_prob: array-like of shape (n_samples, n_classes).
Probability scores from the base model.
y_pred: array-like of shape (n_samples,)
predicted labels.
num_bins: number of bins.
return_counts: set to True to return counts also.
Returns:
float or tuple:
... |
normalize = normalize
self.d = None
self.gt = None
self.lb = None
self.ub = None
self.precompute_bias_data = precompute_bias_data
self.set_coordinates(x_axis_name=DEFAULT_X_AXIS_NAME, y_axis_name=DEFAULT_Y_AXIS_NAME, normalize=normalize)
def set_coordinates(self, x_a... |
"""
excess = np.zeros(d.shape)
posidx = np.where(d >= 0)[0]
excess[posidx] = np.where(ub[posidx] - d[posidx] < 0., 0., ub[posidx] - d[posidx])
negidx = np.where(d < 0)[0]
excess[negidx] = np.where(lb[negidx] + d[negidx] < 0., 0., lb[negidx] + d[negidx])
return np.m... |
60.models.noise_models.heteroscedastic_noise_models import GaussianNoise
class GaussianNoiseMLPNet(torch.nn.Module):
def __init__(self, num_features, num_outputs, num_hidden):
super(GaussianNoiseMLPNet, self).__init__()
self.fc = torch.nn.Linear(num_features, num_hidden)
self.fc_mu = torch... |
return scale_sample * activ_sample
def kl(self):
return super(HorseshoeLayer, self).kl() + self.nodescales.kl() + self.layerscale.kl()
def fixed_point_updates(self):
self.nodescales.fixed_point_updates()
self.layerscale.fixed_point_updates()
class RegularizedHorseshoeLayer(HorseshoeL... |
fixed_point_updates'):
self.fc_out.fixed_point_updates()
for layer in self.fc_hidden:
if hasattr(layer, 'fixed_point_updates'):
layer.fixed_point_updates()
def prior_predictive_samples(self, n_sample=100):
n_eval = 1000
x = torch.linspace(-2, 2, n_eva... |
params_eval = {param_key: param_value[0] for param_key, param_value in params_eval.items()}
ensClass_algs_params[key]=params_eval
else:
pass
return ensClass_algs_params
''' To make array of voting algorithm based on user config list. Not used now, in future if needed similar line with bagging e... |
params.items():
if (key == 'Linear Regression'):
lir=LinearRegression()
lir=lir.set_params(**val)
ensembleBaggingRegList.append(lir)
elif (key == 'Decision Tree'):
dtr=DecisionTreeRegress... |
self.ensemble_params.items():
try:
if (k == "max_features_percentage"):
max_features_percentage=float(v)
elif (k == "max_samples"):
max_samples=float(v)
elif (k == "seed"):
seed=int(v)
... |
params['losses'], optimizer=params['optimizer'], metrics=['mae','mse',rmse_m,r_square])
out = model.fit(x=x_train,
y=y_train,
validation_data=(x_val, y_val),
epochs=params['epochs'],
batch_size=params['batch_size'],
verbose=0)
return out, model
def CNNRegression(self,x_trai... |
r_square])
elif self.scoreParam == 'mae':
best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mae'])
else:
best_modelRNN.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mse'])
scoreRNN = best_modelRNN.evaluate(X1,Y, batch_size=batchsize)
self.log.info("----------... |
_matrix,optimizer=optimizer, metrics=[r_square])
elif self.scoreParam == 'mae':
best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mae'])
else:
best_modelRNNLSTM.compile(loss=loss_matrix,optimizer=optimizer, metrics=['mse'])
scoreRNNLSTM = best_modelRNNLSTM.evaluate(X1,... |
ologies 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 logging
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import cla... |
--->\\n')
self.log.info('\\n<-------- Train Data Shape '+str(xtrain.shape)+' ---------->\\n')
self.log.info('\\n<-------- Test Data Shape '+str(xtest.shape)+' ---------->\\n')
'''
else:
xtrain=featureData
ytrain=targetData
xtest=featureDa |
Conv1D(filters=params['first_neuron'], kernel_size=(3), activation=params['activation'], input_shape=(x_train.shape[1],1),padding='same') )
if params['numConvLayers'] > 1:
for x in range(1,params['numConvLayers']):
if params['MaxPool'] == "True":
model.add(MaxPooling1D(pool_size=2,padding='same'))
mo... |
Param.lower() == 'precision'):
matrix_type = 'val_precision_m'
elif(self.scoreParam.lower() == 'f1_score'):
matrix_type = 'val_f1_m'
analyze_objectRNN = talos.Analyze(scan_object)
highValAccRNN = analyze_objectRNN.high(matrix_type)
dfRNN = analyze_objectRNN.data
newdfRNN = d... |
np.expand_dims(self.testX, axis=2)
#predictedData = best_modelRNNGRU.predict_classes(XSNN)
predictedData=np.argmax(best_modelRNNGRU.predict(XSNN),axis=1)
#predictedData = best_modelSNN.predict(self.testX)
if 'accuracy' in str(self.scoreParam):
score = accuracy_score(self.testY,predictedData)
el... |
.scoreParam.lower() == 'f1_score'):
matrix_type = 'val_f1_m'
analyze_objectCNN = talos.Analyze(scan_object)
highValAccCNN = analyze_objectCNN.high(matrix_type)
dfCNN = analyze_objectCNN.data
newdfCNN = dfCNN.loc[dfCNN[matrix_type] == highValAccCNN]
if(len(newdfCNN) > 1):
lowLo... |
-> " + str(row['consequents']))
self.log.info("---------->Support: "+ str(row['support']))
self.log.info("---------->Confidence: "+ str(row['confidence']))
self.log.info("---------->Lift: "+ str(row['lift']))
#rules['antecedents'] = lis |
try:
start_time = time.time()
objConvUtility=model_converter(model_path,output_path,input_format,output_format,input_shape)
objConvUtility.convert()
end_time = time.time()
log.info(f"Time required for conversion: {end_time - start_time} sec")
log.info(f'\\nConverting {... |
import urllib.request
import zipfile
import os
from os.path import expanduser
import platform
from text import TextCleaning as text_cleaner
from text.Embedding import extractFeatureUsingPreTrainedModel
logEnabled = False
spacy_nlp = None
def ExtractFeatureCountVectors(ngram_range=(1, 1),
... |
stopwordslist = 'extend',
removeNumeric_fIncludeSpecialCharacters = True,
fRemovePuncWithinTokens = False,
data_path = None
):
global logEnabled
#logEnabled = EnableLogging
self.functionSequence = functionSequence
self.fRemoveNoise = fRemoveNoise
... |
else:
if stopwordsList:
stopwordRemovalList = stopwordRemovalList.union(set(stopwordsList))
resultTokensList = [word for word in inputTokensList if word not in stopwordRemovalList]
return resultTokensList
def RemoveNumericTokens(inp... |
, '100d':100, '200d': 200, '300d':300,'500d':500,'700d':700,'1000d':1000}
size_enabled = get_one_true_option(config, 'default')
return size_map[size_enabled]
elif model in ['tf_idf', 'countvectors']:
return int(config.get('maxFeatures', 2000))
else: # for word2vec
return 300
def cleaner(sel... |
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains th... |
from .cat_type_str import cat_to_str
__version__ = "1.0"<s> '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023
* Proprietary an... |
'
output['Status']='Fail'
output["ProblemName"] = ProblemName
output["Msg"] = 'Detected Model : {} \\\\n Problem Type : Regression \\\\n Error : {}'.format(ProblemName, str(e).replace('"','//"').replace('\\n', '\\\\n'))
print(json.dumps(output))
else:
... |
round(modeloutput,2)'
self.output_formatfile += '\\n'
if(learner_type == 'ImageClassification'):
if(str(output_label) != '{}'):
inv_mapping_dict = {v: k for k, v in output_label.items()}
self.output_formatfile += ' le_dict = '+ str... |
if model == 'COX':
self.output_formatfile += '\\n'
self.output_formatfile += ' modeloutput[0] = modeloutput[0].round(2)'
self.output_formatfile += '\\n'
#self.output_formatfile += ' modeloutput = modeloutput[0... |
params['output_features']}
if isinstance(df, scipy.sparse.spmatrix):
df = pd.DataFrame(df.toarray(), columns=columns)
else:
df = pd.DataFrame(df, columns=columns)
return df
"""
return code.replace('\\n', '\\n'+(indent * TAB_CHAR))
def feature_selector_code( params, indent=0):
modules = [
{'... |
File += '\\n'
self.predictionFile += 'if __name__ == "__main__":'
self.predictionFile += '\\n'
self.predictionFile += ' predictobj = prediction()'
self.predictionFile += '\\n'
self.predictionFile += ' predictobj.predict_from_file(sys.argv[1])'
self.predictionFi... |
self.predictionFile += 'from script.selector import selector'
self.predictionFile += '\\n'
self.predictionFile += 'from script.inputprofiler import inputprofiler'
self.predictionFile += '\\n'
#self.predictionFile += 'from '+classname+' import '+classname
... |
import drift
from aion_opdrift import odrift"""
filedata += """
import json
import os
import pandas as pd
import io
import argparse
from pathlib import Path
from flask_cors import CORS, cross_origin
app = Flask(__name__)
#cross origin resource from system arguments
parser = argparse.ArgumentParser()
parser.add... |
table_name,condition=''):
if condition == '':
return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn)
else:
return pd.read_sql_query(f"SELECT * FROM {table_name} WHERE {condition}", self.conn)
def create_table(self,name, columns, dtypes):
query = f'CREATE ... |
file).encode('utf8'))
f.close()
featurefile = 'import json'
featurefile +='\\n'
featurefile += 'def getfeatures():'
featurefile +='\\n'
featurefile +=' try:'
featurefile +='\\n'
featurelist = []
if 'profiler' in config:
if 'input_features_t... |
'
self.modelfile += ' prediction_df["min_threshold"] = min_threshold\\n'
self.modelfile += ' prediction_df["anomaly"] = np.where((prediction_df["loss"] > prediction_df["max_threshold"]) | (prediction_df["loss"] <= prediction_df["min_threshold"]), True, False)\\n'
self.modelfile += ' ... |
+str(additional_regressors)
self.modelfile += '\\n'
self.modelfile += ' ts_prophet_future[additional_regressors] = dataFrame[additional_regressors]'
self.modelfile += '\\n'
|
cing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*/
"""
from importlib.metadata import version
import sys
class importModule():
def __init__(self):
self.importModule = {}
self.stdlibModule = [... |
self.modelfile += ' return 2*((precision*recall)/(precision+recall+K.epsilon()))'
self.modelfile += '\\n';
if(scoreParam.lower() == 'rmse'):
self.modelfile += 'def rmse_m(y_true, y_pred):'
self.modelfile += '\\n';
self.model... |
orecasts')
else:
code = self.profiler_code(model_type,model,config['profiler']['output_features'],features, text_features,config['profiler']['word2num_features'],config,datetimeFeature)
if code:
with open(filename,'w',encoding="utf-8") as f:
f.... |
=None):
cs.create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config)
def create_init_function_for_regress |
.crate_readme_file(deploy_path,saved_model,features,deployJson['method'])
from prediction_package.requirements import requirementfile
requirementfile(deploy_path,model,textFeatures,learner_type)
os.chdir(deploy_path)
textdata = False
if(learner_type == 'Text Similarity' or len(te... |
']['text_feat'],self.params['features']['target_feat'])
else:
obj.create_regression_performance_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'])
def training_code( self):
self.importer.addModule(module='pandas',mod_as='p... |
ct', 'birla_vect', 'birth_vect', 'birthdate_vect', 'birthday_vect', 'bishan_vect', 'bit_vect', 'bitch_vect', 'bite_vect', 'black_vect', 'blackberry_vect', 'blah_vect', 'blake_vect', 'blank_vect', 'bleh_vect', 'bless_vect', 'blessing_vect', 'bloo_vect', 'blood_vect', 'bloody_vect', 'blue_vect', 'bluetooth_vect', 'bluff_... |
fancies_vect', 'fancy_vect', 'fantasies_vect', 'fantastic_vect', 'fantasy_vect', 'far_vect', 'farm_vect', 'fast_vect', 'faster_vect', 'fat_vect', 'father_vect', 'fathima_vect', 'fault_vect', 'fave_vect', 'favorite_vect', 'favour_vect', 'favourite_vect', 'fb_vect', 'feb_vect', 'february_vect', 'feel_vect', 'feelin_vect'... |
'loss_vect', 'lost_vect', 'lot_vect', 'lotr_vect', 'lots_vect', 'lou_vect', 'loud_vect', 'lounge_vect', 'lousy_vect', 'lovable_vect', 'love_vect', 'loved_vect', 'lovely_vect', 'loveme_vect', 'lover_vect', 'loverboy_vect', 'lovers_vect', 'loves_vect', 'loving_vect', 'low_vect', 'lower_vect', 'loyal_vect', 'loyalty_vect'... |
ember_vect', 'remembered_vect', 'remembr_vect', 'remind_vect', 'reminder_vect', 'remove_vect', 'rent_vect', 'rental_vect', 'rentl_vect', 'repair_vect', 'repeat_vect', 'replied_vect', 'reply_vect', 'replying_vect', 'report_vect', 'representative_vect', 'request_vect', 'requests_vect', 'research_vect', 'resend_vect', 're... |
vect', 'txtstop_vect', 'tyler_vect', 'type_vect', 'tyrone_vect', 'u4_vect', 'ubi_vect', 'ufind_vect', 'ugh_vect', 'uh_vect', 'uk_vect', 'uks_vect', 'ultimatum_vect', 'umma_vect', 'unable_vect', 'uncle_vect', 'understand_vect', 'understanding_vect', 'understood_vect', 'underwear_vect', 'unemployed_vect', 'uni_vect', 'un... |
'avoid_vect', 'await_vect', 'awaiting_vect', 'awake_vect', 'award_vect', 'awarded_vect', 'away_vect', 'awesome_vect', 'aww_vect', 'b4_vect', 'ba_vect', 'babe_vect', 'babes_vect', 'babies_vect', 'baby_vect', 'back_vect', 'bad_vect', 'bag_vect', 'bags_vect', 'bahamas_vect', 'bak_vect', 'balance_vect', 'bank_vect', 'banks... |
'eng_vect', 'engin_vect', 'england_vect', 'english_vect', 'enjoy_vect', 'enjoyed_vect', 'enough_vect', 'enter_vect', 'entered_vect', 'entitled_vect', 'entry_vect', 'enuff_vect', 'envelope_vect', 'er_vect', 'erm_vect', 'escape_vect', 'especially_vect', 'esplanade_vect', 'eta_vect', 'etc_vect', 'euro_vect', 'euro2004_vec... |
'leaves_vect', 'leaving_vect', 'lect_vect', 'lecture_vect', 'left_vect', 'legal_vect', 'legs_vect', 'leh_vect', 'lei_vect', 'lem_vect', 'less_vect', 'lesson_vect', 'lessons_vect', 'let_vect', 'lets_vect', 'letter_vect', 'letters_vect', 'liao_vect', 'library_vect', 'lick_vect', 'licks_vect', 'lido_vect', 'lie_vect', 'li... |
'queen_vect', 'ques_vect', 'question_vect', 'questions_vect', 'quick_vect', 'quickly_vect', 'quiet_vect', 'quit_vect', 'quite_vect', 'quiz_vect', 'quote_vect', 'quoting_vect', 'racing_vect', 'radio_vect', 'railway_vect', 'rain_vect', 'raining_vect', 'raise_vect', 'rakhesh_vect', 'rally_vect', 'ran_vect', 'random_vect',... |
vect', 'toclaim_vect', 'today_vect', 'todays_vect', 'tog_vect', 'together_vect', 'tok_vect', 'told_vect', 'tomarrow_vect', 'tomo_vect', 'tomorrow_vect', 'tone_vect', 'tones_vect', 'tones2youcouk_vect', 'tonight_vect', 'tonite_vect', 'took_vect', 'tool_vect', 'tooo_vect', 'toot_vect', 'top_vect', 'topic_vect', 'torch_ve... |
modelservice.sh start_modelservice.sh'
dockerdata+='\\n'
if textdata:
dockerdata+='''RUN apt-get update \\
&& apt-get install -y build-essential manpages-dev \\
&& python -m pip install --no-cache-dir --upgrade pip \\
&& python -m pip install --no-cache-dir pandas==1.2.4 \\
&& python -m pip inst... |
subdir in subdirs:
if(subdir != 'pytransform'):
alldirs.append(os.path.join(project_path, subdir))
encrypt(alldirs)
replace_by_compressed(alldirs)
if __name__=="__main__":
project_path = sys.argv[1]
print("project_path", project_path)
subdirs = [dI for dI in os.listdir(project_path) if os.path.isdir(os.path... |
+ code
def feature_engg_code(self):
self.importer.addModule(module='pandas',mod_as='pd')
return f"""
class selector():
def __init__(self):
pass
def run(self, df):
return df
"""
def training_code( |
]==2:
prediction[col] = (datasetdf[col].iloc[-1]-datasetdf[col].iloc[-2]) + prediction[col].cumsum()
prediction[col] = datasetdf[col].iloc[-1] + prediction[col].cumsum()
prediction = pred
return(prediction)
def run(self,raw_df,df):
df = self.invertTransfo... |
chdir(str(self.sagemakerLogLocation))
mlflow_root_dir = os.getcwd()
self.log.info('mlflow root dir: '+str(mlflow_root_dir))
except:
self.log.info("path issue.")
try:
c_status=self.check_sm_deploy_status(app_name)
#if ((c_status ==... |
FileNotFoundError:
self.log.info('model_path does not exist. '+str(mlflow_root_dir))
except NotADirectoryError:
self.log.info('model_path is not a directory. '+str(mlflow_root_dir))
except PermissionError:
... |
with open(file_path,'r') as f:
data = json.load(f)
return data
def run_pipeline(inputconfig):
inputconfig = json.loads(inputconfig)
logfilepath = inputconfig['logfilepath']
logging.basicConfig(level=logging.INFO,filename =logfilepath)
usecasename = inputconfig['usecas... |
sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve
from math import sqrt
from sklearn.metrics import mean_squared_error, explained_variance_score,mean_absolute_error
from sklearn import metrics
class aionNAS:
def __init__(self,nas_class,nas_params,xtrain1,xtest1,ytrain1,ytest1,deployLoc... |
IL","message":str(inst).strip('"')}
output = json.dumps(output)
<s> import itertools
import logging
from typing import Optional, Dict, Union
from nltk import sent_tokenize
import torch
from transformers import(
AutoModelForSeq2SeqLM,
AutoTokenizer,
PreTrainedModel,
PreTrainedToken... |
(model=model, tokenizer=tokenizer, ans_model=ans_model, ans_tokenizer=ans_tokenizer, qg_format=qg_format, use_cuda=use_cuda)
else:
return task_class(model=model, tokenizer=tokenizer, ans_model=model, ans_tokenizer=tokenizer, qg_format=qg_format, use_cuda=use_cuda)
<s> '''
*
* ===============================... |
2Uv/h8oARWnbrvicMRTwYL0w2GrP0f+aG0RqQ
msLMzS3kp6szhM7C99reFxdlxJoWBKkp94psOksCgYkApB01zGRudkK17EcdvjPc
sMHzfoFryNpPaI23VChuR4UW2mZ797NAypSqRXE7OALxaOuOVuWqP8jW0C9i/Cps
hI+SnZHFAw2tU3+hd3Wz9NouNUd6c2MwCSDQ5LikGttHSTa49/JuGdmGLTxCzRVu
V0NiMPMfW4I2Sk8o4U3gbzWgwiYohLrhrwJ5ANun/7IB2lIykvk7B3g1nZzRYDIk
EFpuI3ppWA8NwOUUoj/zks... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.