In [1]:
import pandas as pd
import numpy as np
import re
%matplotlib inline
In [2]:
def cleanup_hive_message(mess):
if('TExecuteStatementResp' in mess):
mess1 = re.sub('^(.*)infoMessages=\[' , '' , mess)
mess2 = mess1.split(':')
# print("HIVE_BEFORE" , mess)
# print("HIVE_AFTER" , mess2)
res = mess2[1] + " " +mess2[2] + " " +mess2[3]
# print("HIVE_AFTER2" , res)
return res
return mess
def normalize_model_class_name(model_class_name):
line1 = model_class_name
line1 = line1.replace("sklearn2sql.Helpers.Caret_Model.cCaretClassifier_" , "caret.classifier.caret_class_")
line1 = line1.replace("sklearn2sql.Helpers.Caret_Model.cCaretRegressor_" , "caret.regressor.caret_reg_")
line1 = line1.replace("sklearn2sql.Helpers.Caret_Model.cCaretPreprocessor_" , "caret.preprocessor.caret_prep_")
line1 = line1.replace("sklearn2sql.Helpers.Keras_Model.cKerasClassifier_" , "keras.classifier.keras_class_")
line1 = line1.replace("sklearn2sql.Helpers.Keras_Model.cKerasRegressor_" , "keras.regressor.keras_reg_")
return line1
def normalize_model_name(model_name):
line1 = model_name
line1 = line1.replace("cCaretClassifier_" , "caret_class_")
line1 = line1.replace("cCaretRegressor_" , "caret_reg_")
line1 = line1.replace("cCaretPreprocessor_" , "caret_prep_")
line1 = line1.replace("cKerasClassifier_" , "keras_class_")
line1 = line1.replace("cKerasRegressor_" , "keras_reg_")
return line1
database_name_from_dsn = {"pgsql" : "PostgreSQL",
"oracle" : "Oracle",
"db2" : "IBM DB2",
"sqltm" : "SQLite",
"mssql" : "MS SQL Server",
"mysql" : "MariaDB",
"hive" : "Apache Hive",
"impala" : "Impala",
"firebird" : "Firebird",
"monetdb" : "MonetDB",
"teradata" : "Teradata"}
def strip_punc(x):
x = x .replace("'" , "")
x = x .replace("(" , "")
x = x .replace("," , "")
return x
def truncate_error_message(x):
lSpecialErrors = ['UnboundLocalError',
'Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS',
'Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL',
'cTrainingError:Exception:TRAIN_FAILED',
'Exception:CHECK_MATERIALIZED_DATA_ERROR',
'Exception:PREDICT_FAILED',
'NAN_VALUE_ENCOUNTERED_IN_MODEL',
'Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED',
'Exception:CONNECTION_FAILED_WITH_ERROR',
'FileNotFoundError', 'IndexError', 'KeyError',
'DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name:',
'cTrainingError']
for lError in lSpecialErrors:
if(lError in x):
return lError + " ..."
lLongMessages = ["concurrent transaction number is",
"DOUBLE value is out of range in",
"Unexpected end of command" ,
"Token unknown"]
for mess in lLongMessages:
x = re.sub(mess + ".*$", mess + " ...", x)
x = re.sub("\(Background on this error at:.*\)", "", x)
x = re.sub("\[SQL.*$", "\[SQL ...", x)
return x[0:400]
def read_bad_csv_file(name):
rows = [];
with open(name) as f:
content = f.readlines()
for line in content:
line1 = re.sub('^.*log:BENCH_STATUS ' , '', line)
# print(line1)
fields = line1.split(" ")
(lModel, lDatasetName, lDSN, lDialect, lDSNType) = fields[0:5]
lStatus = " ".join(fields[5:-1])
lTime = fields[-1][:-2]
fields2 = lStatus.split(" ")
lErrorMessage = " ".join(fields2[5:])
status = fields2[4]
sql_error = lErrorMessage
row = [lModel, lDatasetName, lDialect, lDSN, status, lErrorMessage, lTime]
if(False):
print("[lModel]" , row[0])
print("[lDatasetName]" , row[1])
print("[lDialect]" , row[2])
print("[lDSN]" , row[3])
print("[status]" , row[4])
print("[lErrorMessage]" , row[5])
print("[lTime]" , row[6])
rows = rows + [row]
df = pd.DataFrame(rows);
df.columns = ['Model' , 'dataset', 'dialect' , 'DSN' , 'status' , 'error_message' , 'elapsed_time']
df['dialect'] = df['dialect'].apply(lambda x : strip_punc(x))
df['Model'] = df['Model'].apply(lambda x : strip_punc(x))
df['dataset'] = df['dataset'].apply(lambda x : strip_punc(x))
df['status'] = df['status'].apply(lambda x : strip_punc(x))
print(list( df['dialect'].unique()))
df['dialect'] = df['dialect'].apply(database_name_from_dsn.get)
df['Model'] = df['Model'].str.replace("\(\'" , "")
df['Model'] = df['Model'].str.replace("\'," , "")
df['Model'] = df['Model'].apply(normalize_model_name)
df['full_error_message'] = df['error_message']
df['error_message'] = df['error_message'].str.replace("None\)\)" , "SUCCESS")
df['error_message'] = df['error_message'].str.replace("None\)," , "SUCCESS")
df['error_message'] = df['error_message'].apply(lambda x : cleanup_hive_message(x))
df['error_message'] = df['error_message'].apply(truncate_error_message)
return df
In [3]:
#df = pd.read_csv('result.txt' , engine='python', sep='\s+', index_col=False,
# quotechar="'", header=None)
In [4]:
df = read_bad_csv_file('result.txt')
['db2', 'firebird', 'impala', 'monetdb', 'mssql', 'mysql', 'oracle', 'pgsql', 'sqltm', 'teradata']
In [5]:
df.head()
Out[5]:
Model
dataset
dialect
DSN
status
error_message
elapsed_time
full_error_message
0
LGBMClassifier
DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
1
LGBMClassifier
DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
2
LGBMClassifier_pipe
p_DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
3
LGBMClassifier
DS_BENCH_C_200_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
4
LGBMClassifier_pipe
p_DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
In [6]:
df.Model.value_counts()
Out[6]:
LabelBinarizer 180
keras_class_LSTM 180
LabelEncoder 180
keras_class_SimpleRNN_pipe 180
PassiveAggressiveClassifier_pipe 180
DummyClassifier_pipe 180
ExtraTreeClassifier 180
BaggingClassifier_pipe 180
ExtraTreesClassifier_pipe 180
DummyClassifier 180
DecisionTreeClassifier 180
keras_class_LSTM_pipe 180
RandomForestClassifier 180
LinearSVC_pipe 180
RidgeClassifier_pipe 180
ExtraTreeClassifier_pipe 180
DecisionTreeClassifier_pipe 180
BaggingClassifier 180
LinearSVC 180
PassiveAggressiveClassifier 180
SGDClassifier_pipe 180
keras_class_GRU 180
ExtraTreesClassifier 180
RandomForestClassifier_pipe 180
keras_class_SimpleRNN 180
RidgeClassifierCV_pipe 180
keras_class_GRU_pipe 180
Pipeline 179
RidgeClassifierCV 179
OneVsOneClassifier 179
...
caret_reg_glmnet 60
OrthogonalMatchingPursuitCV 60
BayesianRidge_pipe 60
BaggingRegressor_pipe 60
caret_class_glm_pipe 60
keras_reg_Dense 60
DecisionTreeRegressor 60
caret_reg_rpart 60
GradientBoostingRegressor_pipe 60
LGBMRegressor 60
AdaBoostRegressor 60
LGBMRegressor_pipe 60
DummyRegressor_pipe 60
ExtraTreeRegressor 60
LassoLarsIC_pipe 60
RandomForestRegressor_pipe 60
LinearSVR 60
LarsCV 60
keras_reg_SimpleRNN 60
LassoCV_pipe 60
Lasso_pipe 60
IsolationForest_pipe 60
caret_reg_ctree 60
KernelRidge 60
Lars 60
caret_reg_nnet 60
LinearRegression_pipe 60
caret_reg_svmPoly_pipe 59
EllipticEnvelope_pipe 58
EllipticEnvelope 56
Name: Model, Length: 251, dtype: int64
In [7]:
def read_classes(name):
lClasses = {}
lCategories = {}
rows = [];
with open(name) as f:
content = f.readlines()
for line in content:
line1 = line.replace("cGenerationWrapperFactory::createScikitObject() <class '" , "")
line1 = line1.replace("'>\n" , "")
line1 = normalize_model_class_name(line1)
fq_class_name = line1
short_class_name = fq_class_name.split(".")[-1]
categeory = ".".join(fq_class_name.split(".")[0:2])
lClasses[short_class_name] = fq_class_name
lCategories[short_class_name] = categeory
return (lClasses , lCategories)
In [8]:
(Classes , Categories) = read_classes("classes.txt")
In [9]:
Classes
Out[9]:
{'LGBMClassifier': 'lightgbm.sklearn.LGBMClassifier',
'LGBMRegressor': 'lightgbm.sklearn.LGBMRegressor',
'caret_class_ctree': 'caret.classifier.caret_class_ctree',
'caret_class_ctree2': 'caret.classifier.caret_class_ctree2',
'caret_class_earth': 'caret.classifier.caret_class_earth',
'caret_class_glm': 'caret.classifier.caret_class_glm',
'caret_class_glmnet': 'caret.classifier.caret_class_glmnet',
'caret_class_nnet': 'caret.classifier.caret_class_nnet',
'caret_class_rf': 'caret.classifier.caret_class_rf',
'caret_class_rpart': 'caret.classifier.caret_class_rpart',
'caret_class_svmLinear': 'caret.classifier.caret_class_svmLinear',
'caret_class_svmPoly': 'caret.classifier.caret_class_svmPoly',
'caret_class_svmRadial': 'caret.classifier.caret_class_svmRadial',
'caret_class_svmRadialCost': 'caret.classifier.caret_class_svmRadialCost',
'caret_class_svmRadialSigma': 'caret.classifier.caret_class_svmRadialSigma',
'caret_class_svmRadialWeights': 'caret.classifier.caret_class_svmRadialWeights',
'caret_class_xgbTree': 'caret.classifier.caret_class_xgbTree',
'caret_prep_center_scale': 'caret.preprocessor.caret_prep_center_scale',
'caret_prep_ica': 'caret.preprocessor.caret_prep_ica',
'caret_prep_pca': 'caret.preprocessor.caret_prep_pca',
'caret_reg_ctree': 'caret.regressor.caret_reg_ctree',
'caret_reg_ctree2': 'caret.regressor.caret_reg_ctree2',
'caret_reg_earth': 'caret.regressor.caret_reg_earth',
'caret_reg_glm': 'caret.regressor.caret_reg_glm',
'caret_reg_glmnet': 'caret.regressor.caret_reg_glmnet',
'caret_reg_nnet': 'caret.regressor.caret_reg_nnet',
'caret_reg_rf': 'caret.regressor.caret_reg_rf',
'caret_reg_rpart': 'caret.regressor.caret_reg_rpart',
'caret_reg_svmLinear': 'caret.regressor.caret_reg_svmLinear',
'caret_reg_svmPoly': 'caret.regressor.caret_reg_svmPoly',
'caret_reg_svmRadial': 'caret.regressor.caret_reg_svmRadial',
'caret_reg_svmRadialCost': 'caret.regressor.caret_reg_svmRadialCost',
'caret_reg_svmRadialSigma': 'caret.regressor.caret_reg_svmRadialSigma',
'caret_reg_xgbTree': 'caret.regressor.caret_reg_xgbTree',
'keras_class_Dense': 'keras.classifier.keras_class_Dense',
'keras_class_GRU': 'keras.classifier.keras_class_GRU',
'keras_class_LSTM': 'keras.classifier.keras_class_LSTM',
'keras_class_SimpleRNN': 'keras.classifier.keras_class_SimpleRNN',
'keras_reg_Dense': 'keras.regressor.keras_reg_Dense',
'keras_reg_GRU': 'keras.regressor.keras_reg_GRU',
'keras_reg_LSTM': 'keras.regressor.keras_reg_LSTM',
'keras_reg_SimpleRNN': 'keras.regressor.keras_reg_SimpleRNN',
'CalibratedClassifierCV': 'sklearn.calibration.CalibratedClassifierCV',
'EllipticEnvelope': 'sklearn.covariance.elliptic_envelope.EllipticEnvelope',
'FactorAnalysis': 'sklearn.decomposition.factor_analysis.FactorAnalysis',
'FastICA': 'sklearn.decomposition.fastica_.FastICA',
'IncrementalPCA': 'sklearn.decomposition.incremental_pca.IncrementalPCA',
'KernelPCA': 'sklearn.decomposition.kernel_pca.KernelPCA',
'NMF': 'sklearn.decomposition.nmf.NMF',
'LatentDirichletAllocation': 'sklearn.decomposition.online_lda.LatentDirichletAllocation',
'PCA': 'sklearn.decomposition.pca.PCA',
'MiniBatchSparsePCA': 'sklearn.decomposition.sparse_pca.MiniBatchSparsePCA',
'SparsePCA': 'sklearn.decomposition.sparse_pca.SparsePCA',
'TruncatedSVD': 'sklearn.decomposition.truncated_svd.TruncatedSVD',
'LinearDiscriminantAnalysis': 'sklearn.discriminant_analysis.LinearDiscriminantAnalysis',
'DummyClassifier': 'sklearn.dummy.DummyClassifier',
'DummyRegressor': 'sklearn.dummy.DummyRegressor',
'BaggingClassifier': 'sklearn.ensemble.bagging.BaggingClassifier',
'BaggingRegressor': 'sklearn.ensemble.bagging.BaggingRegressor',
'ExtraTreesClassifier': 'sklearn.ensemble.forest.ExtraTreesClassifier',
'ExtraTreesRegressor': 'sklearn.ensemble.forest.ExtraTreesRegressor',
'RandomForestClassifier': 'sklearn.ensemble.forest.RandomForestClassifier',
'RandomForestRegressor': 'sklearn.ensemble.forest.RandomForestRegressor',
'GradientBoostingClassifier': 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier',
'GradientBoostingRegressor': 'sklearn.ensemble.gradient_boosting.GradientBoostingRegressor',
'IsolationForest': 'sklearn.ensemble.iforest.IsolationForest',
'AdaBoostClassifier': 'sklearn.ensemble.weight_boosting.AdaBoostClassifier',
'AdaBoostRegressor': 'sklearn.ensemble.weight_boosting.AdaBoostRegressor',
'SelectFromModel': 'sklearn.feature_selection.from_model.SelectFromModel',
'RFE': 'sklearn.feature_selection.rfe.RFE',
'RFECV': 'sklearn.feature_selection.rfe.RFECV',
'GenericUnivariateSelect': 'sklearn.feature_selection.univariate_selection.GenericUnivariateSelect',
'SelectFdr': 'sklearn.feature_selection.univariate_selection.SelectFdr',
'SelectFpr': 'sklearn.feature_selection.univariate_selection.SelectFpr',
'SelectFwe': 'sklearn.feature_selection.univariate_selection.SelectFwe',
'SelectKBest': 'sklearn.feature_selection.univariate_selection.SelectKBest',
'SelectPercentile': 'sklearn.feature_selection.univariate_selection.SelectPercentile',
'MissingIndicator': 'sklearn.impute.MissingIndicator',
'SimpleImputer': 'sklearn.impute.SimpleImputer',
'KernelRidge': 'sklearn.kernel_ridge.KernelRidge',
'LinearRegression': 'sklearn.linear_model.base.LinearRegression',
'ARDRegression': 'sklearn.linear_model.bayes.ARDRegression',
'BayesianRidge': 'sklearn.linear_model.bayes.BayesianRidge',
'ElasticNet': 'sklearn.linear_model.coordinate_descent.ElasticNet',
'ElasticNetCV': 'sklearn.linear_model.coordinate_descent.ElasticNetCV',
'Lasso': 'sklearn.linear_model.coordinate_descent.Lasso',
'LassoCV': 'sklearn.linear_model.coordinate_descent.LassoCV',
'MultiTaskElasticNet': 'sklearn.linear_model.coordinate_descent.MultiTaskElasticNet',
'MultiTaskElasticNetCV': 'sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV',
'MultiTaskLasso': 'sklearn.linear_model.coordinate_descent.MultiTaskLasso',
'MultiTaskLassoCV': 'sklearn.linear_model.coordinate_descent.MultiTaskLassoCV',
'Lars': 'sklearn.linear_model.least_angle.Lars',
'LarsCV': 'sklearn.linear_model.least_angle.LarsCV',
'LassoLars': 'sklearn.linear_model.least_angle.LassoLars',
'LassoLarsCV': 'sklearn.linear_model.least_angle.LassoLarsCV',
'LassoLarsIC': 'sklearn.linear_model.least_angle.LassoLarsIC',
'LogisticRegression': 'sklearn.linear_model.logistic.LogisticRegression',
'LogisticRegressionCV': 'sklearn.linear_model.logistic.LogisticRegressionCV',
'OrthogonalMatchingPursuit': 'sklearn.linear_model.omp.OrthogonalMatchingPursuit',
'OrthogonalMatchingPursuitCV': 'sklearn.linear_model.omp.OrthogonalMatchingPursuitCV',
'PassiveAggressiveClassifier': 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier',
'PassiveAggressiveRegressor': 'sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor',
'Perceptron': 'sklearn.linear_model.perceptron.Perceptron',
'RandomizedLogisticRegression': 'sklearn.linear_model.randomized_l1.RandomizedLogisticRegression',
'RANSACRegressor': 'sklearn.linear_model.ransac.RANSACRegressor',
'Ridge': 'sklearn.linear_model.ridge.Ridge',
'RidgeClassifier': 'sklearn.linear_model.ridge.RidgeClassifier',
'RidgeClassifierCV': 'sklearn.linear_model.ridge.RidgeClassifierCV',
'RidgeCV': 'sklearn.linear_model.ridge.RidgeCV',
'SGDClassifier': 'sklearn.linear_model.stochastic_gradient.SGDClassifier',
'SGDRegressor': 'sklearn.linear_model.stochastic_gradient.SGDRegressor',
'TheilSenRegressor': 'sklearn.linear_model.theil_sen.TheilSenRegressor',
'OneVsOneClassifier': 'sklearn.multiclass.OneVsOneClassifier',
'OneVsRestClassifier': 'sklearn.multiclass.OneVsRestClassifier',
'BernoulliNB': 'sklearn.naive_bayes.BernoulliNB',
'ComplementNB': 'sklearn.naive_bayes.ComplementNB',
'GaussianNB': 'sklearn.naive_bayes.GaussianNB',
'MultinomialNB': 'sklearn.naive_bayes.MultinomialNB',
'MLPClassifier': 'sklearn.neural_network.multilayer_perceptron.MLPClassifier',
'MLPRegressor': 'sklearn.neural_network.multilayer_perceptron.MLPRegressor',
'FeatureUnion': 'sklearn.pipeline.FeatureUnion',
'Pipeline': 'sklearn.pipeline.Pipeline',
'Binarizer': 'sklearn.preprocessing.data.Binarizer',
'MaxAbsScaler': 'sklearn.preprocessing.data.MaxAbsScaler',
'MinMaxScaler': 'sklearn.preprocessing.data.MinMaxScaler',
'Normalizer': 'sklearn.preprocessing.data.Normalizer',
'PolynomialFeatures': 'sklearn.preprocessing.data.PolynomialFeatures',
'PowerTransformer': 'sklearn.preprocessing.data.PowerTransformer',
'QuantileTransformer': 'sklearn.preprocessing.data.QuantileTransformer',
'RobustScaler': 'sklearn.preprocessing.data.RobustScaler',
'StandardScaler': 'sklearn.preprocessing.data.StandardScaler',
'KBinsDiscretizer': 'sklearn.preprocessing._discretization.KBinsDiscretizer',
'OneHotEncoder': 'sklearn.preprocessing._encoders.OneHotEncoder',
'OrdinalEncoder': 'sklearn.preprocessing._encoders.OrdinalEncoder',
'Imputer': 'sklearn.preprocessing.imputation.Imputer',
'LabelBinarizer': 'sklearn.preprocessing.label.LabelBinarizer',
'LabelEncoder': 'sklearn.preprocessing.label.LabelEncoder',
'GaussianRandomProjection': 'sklearn.random_projection.GaussianRandomProjection',
'SparseRandomProjection': 'sklearn.random_projection.SparseRandomProjection',
'LinearSVC': 'sklearn.svm.classes.LinearSVC',
'LinearSVR': 'sklearn.svm.classes.LinearSVR',
'NuSVC': 'sklearn.svm.classes.NuSVC',
'NuSVR': 'sklearn.svm.classes.NuSVR',
'OneClassSVM': 'sklearn.svm.classes.OneClassSVM',
'SVC': 'sklearn.svm.classes.SVC',
'SVR': 'sklearn.svm.classes.SVR',
'DecisionTreeClassifier': 'sklearn.tree.tree.DecisionTreeClassifier',
'DecisionTreeRegressor': 'sklearn.tree.tree.DecisionTreeRegressor',
'ExtraTreeClassifier': 'sklearn.tree.tree.ExtraTreeClassifier',
'ExtraTreeRegressor': 'sklearn.tree.tree.ExtraTreeRegressor',
'XGBClassifier': 'xgboost.sklearn.XGBClassifier',
'XGBRegressor': 'xgboost.sklearn.XGBRegressor'}
In [10]:
def get_category(model_name):
real_name = model_name
if(model_name.endswith('_pipe')):
real_name = model_name.replace("_pipe" , "")
return Categories.get(real_name , "bad_category")
df['model_category'] = df['Model'].apply(get_category)
df.head()
Out[10]:
Model
dataset
dialect
DSN
status
error_message
elapsed_time
full_error_message
model_category
0
LGBMClassifier
DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
1
LGBMClassifier
DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
2
LGBMClassifier_pipe
p_DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
3
LGBMClassifier
DS_BENCH_C_200_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
4
LGBMClassifier_pipe
p_DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
In [11]:
df_model_datasets = pd.DataFrame(df[['Model' , 'dataset']].groupby(['Model'])['dataset'].value_counts())
datatsets_by_model = df_model_datasets.to_dict()['dataset'].keys()
In [12]:
# datatsets_by_model
In [13]:
datatsets_by_model_dict = {}
for k in datatsets_by_model:
(m , ds) = k
if(m not in datatsets_by_model_dict.keys()):
datatsets_by_model_dict[m] = [ds]
else:
datatsets_by_model_dict[m] = datatsets_by_model_dict[m] + [ds]
# print(datatsets_by_model_dict)
In [14]:
missing_rows = []
dialects = df.dialect.unique()
print(dialects)
print("df.shape", df.shape , df.columns)
for dialect in dialects:
df1 = df[df['dialect'] == dialect]
# print("df1.shape", dialect, df1.shape , df1.columns)
for (m , datasets) in datatsets_by_model_dict.items():
lMissing = 0
cond2 = (df1['Model'] == m)
df2 = df1[cond2]
# print("df2.shape", dialect, m , df2.shape, df2.columns)
for ds in datasets:
cond3 = (df2['dataset'] == ds)
df3 = df2[cond3]
# print("df3.shape", dialect, m, ds, df3.shape, df3.columns)
if(df3.shape[0] == 0):
# print("MISSING_DATA" , [dialect , m , ds])
missing_rows = missing_rows + [[m , ds, dialect , 'no_dsn' , 'failure' , 'TIMEOUT' , None, 'TIMEOUT', get_category(m)]]
lMissing = lMissing + 1
if(lMissing >= (len(datasets) // 2)):
print("MODEL_DATASETS_MISSING" , dialect, m, len(datasets) , lMissing)
missing_data = pd.DataFrame(missing_rows, columns=df.columns)
# missing_data
['IBM DB2' 'Firebird' 'Impala' 'MonetDB' 'MS SQL Server' 'MariaDB'
'Oracle' 'PostgreSQL' 'SQLite' 'Teradata']
df.shape (27230, 9) Index(['Model', 'dataset', 'dialect', 'DSN', 'status', 'error_message',
'elapsed_time', 'full_error_message', 'model_category'],
dtype='object')
MODEL_DATASETS_MISSING IBM DB2 caret_class_ctree2 18 9
MODEL_DATASETS_MISSING IBM DB2 caret_class_ctree2_pipe 18 9
MODEL_DATASETS_MISSING IBM DB2 caret_class_svmPoly_pipe 12 6
MODEL_DATASETS_MISSING Firebird caret_class_ctree_pipe 18 10
MODEL_DATASETS_MISSING Firebird caret_class_earth 18 9
MODEL_DATASETS_MISSING Firebird caret_class_earth_pipe 18 10
MODEL_DATASETS_MISSING Firebird caret_class_nnet_pipe 18 9
MODEL_DATASETS_MISSING Firebird caret_class_rf_pipe 18 9
MODEL_DATASETS_MISSING Firebird caret_class_rpart_pipe 18 10
MODEL_DATASETS_MISSING Firebird caret_class_svmPoly 12 6
MODEL_DATASETS_MISSING Firebird caret_class_svmPoly_pipe 12 8
MODEL_DATASETS_MISSING Firebird caret_class_svmRadial_pipe 18 9
MODEL_DATASETS_MISSING Impala BernoulliNB 18 10
MODEL_DATASETS_MISSING Impala BernoulliNB_pipe 18 10
MODEL_DATASETS_MISSING Impala ComplementNB 18 10
MODEL_DATASETS_MISSING Impala ComplementNB_pipe 18 10
MODEL_DATASETS_MISSING Impala GaussianNB 18 10
MODEL_DATASETS_MISSING Impala GaussianNB_pipe 18 10
MODEL_DATASETS_MISSING Impala GradientBoostingClassifier 18 10
MODEL_DATASETS_MISSING Impala GradientBoostingClassifier_pipe 18 11
MODEL_DATASETS_MISSING Impala LGBMClassifier 18 11
MODEL_DATASETS_MISSING Impala LGBMClassifier_pipe 18 11
MODEL_DATASETS_MISSING Impala LinearDiscriminantAnalysis 18 13
MODEL_DATASETS_MISSING Impala LinearDiscriminantAnalysis_pipe 18 13
MODEL_DATASETS_MISSING Impala LogisticRegression 18 13
MODEL_DATASETS_MISSING Impala LogisticRegressionCV 18 13
MODEL_DATASETS_MISSING Impala LogisticRegressionCV_pipe 18 13
MODEL_DATASETS_MISSING Impala LogisticRegression_pipe 18 13
MODEL_DATASETS_MISSING Impala MLPClassifier 18 11
MODEL_DATASETS_MISSING Impala MLPClassifier_pipe 18 12
MODEL_DATASETS_MISSING Impala MultinomialNB 18 11
MODEL_DATASETS_MISSING Impala MultinomialNB_pipe 18 11
MODEL_DATASETS_MISSING Impala NuSVC 18 11
MODEL_DATASETS_MISSING Impala NuSVC_pipe 18 11
MODEL_DATASETS_MISSING Impala OneVsRestClassifier 18 10
MODEL_DATASETS_MISSING Impala OneVsRestClassifier_pipe 18 10
MODEL_DATASETS_MISSING Impala SVC 18 11
MODEL_DATASETS_MISSING Impala SVC_pipe 18 11
MODEL_DATASETS_MISSING Impala XGBClassifier 18 11
MODEL_DATASETS_MISSING Impala XGBClassifier_pipe 18 12
MODEL_DATASETS_MISSING Impala caret_class_earth 18 12
MODEL_DATASETS_MISSING Impala caret_class_earth_pipe 18 13
MODEL_DATASETS_MISSING Impala caret_class_glmnet 18 12
MODEL_DATASETS_MISSING Impala caret_class_glmnet_pipe 18 13
MODEL_DATASETS_MISSING Impala caret_class_nnet 18 10
MODEL_DATASETS_MISSING Impala caret_class_nnet_pipe 18 12
MODEL_DATASETS_MISSING Impala caret_class_svmLinear 18 12
MODEL_DATASETS_MISSING Impala caret_class_svmLinear_pipe 18 13
MODEL_DATASETS_MISSING Impala caret_class_svmPoly_pipe 12 7
MODEL_DATASETS_MISSING Impala caret_class_svmRadial 17 11
MODEL_DATASETS_MISSING Impala caret_class_svmRadialCost 18 12
MODEL_DATASETS_MISSING Impala caret_class_svmRadialCost_pipe 18 13
MODEL_DATASETS_MISSING Impala caret_class_svmRadialSigma 18 12
MODEL_DATASETS_MISSING Impala caret_class_svmRadialSigma_pipe 18 13
MODEL_DATASETS_MISSING Impala caret_class_svmRadialWeights 18 10
MODEL_DATASETS_MISSING Impala caret_class_svmRadialWeights_pipe 18 12
MODEL_DATASETS_MISSING Impala caret_class_svmRadial_pipe 18 13
MODEL_DATASETS_MISSING Impala caret_class_xgbTree 18 10
MODEL_DATASETS_MISSING Impala caret_class_xgbTree_pipe 18 11
MODEL_DATASETS_MISSING MonetDB caret_class_ctree_pipe 18 9
MODEL_DATASETS_MISSING MonetDB caret_class_earth_pipe 18 10
MODEL_DATASETS_MISSING MonetDB caret_class_glmnet 18 14
MODEL_DATASETS_MISSING MonetDB caret_class_glmnet_pipe 18 14
MODEL_DATASETS_MISSING MonetDB caret_class_nnet 18 14
MODEL_DATASETS_MISSING MonetDB caret_class_nnet_pipe 18 14
MODEL_DATASETS_MISSING MonetDB caret_class_rpart_pipe 18 9
MODEL_DATASETS_MISSING MonetDB caret_class_svmPoly 12 6
MODEL_DATASETS_MISSING MonetDB caret_class_svmPoly_pipe 12 7
MODEL_DATASETS_MISSING MonetDB caret_class_svmRadial 17 8
MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialCost_pipe 18 10
MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialSigma 18 9
MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialSigma_pipe 18 10
MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialWeights 18 9
MODEL_DATASETS_MISSING MonetDB caret_class_svmRadialWeights_pipe 18 10
MODEL_DATASETS_MISSING MonetDB caret_class_svmRadial_pipe 18 10
MODEL_DATASETS_MISSING MonetDB caret_class_xgbTree 18 14
MODEL_DATASETS_MISSING MonetDB caret_class_xgbTree_pipe 18 14
MODEL_DATASETS_MISSING MS SQL Server caret_class_ctree2 18 9
MODEL_DATASETS_MISSING MS SQL Server caret_class_ctree2_pipe 18 18
MODEL_DATASETS_MISSING MS SQL Server caret_class_svmPoly_pipe 12 6
MODEL_DATASETS_MISSING Oracle NuSVC 18 10
MODEL_DATASETS_MISSING Oracle SVC 18 10
MODEL_DATASETS_MISSING Oracle caret_class_svmPoly_pipe 12 6
MODEL_DATASETS_MISSING SQLite caret_class_nnet_pipe 18 9
MODEL_DATASETS_MISSING SQLite caret_class_svmLinear_pipe 18 9
MODEL_DATASETS_MISSING SQLite caret_class_svmPoly 12 6
MODEL_DATASETS_MISSING SQLite caret_class_svmPoly_pipe 12 6
MODEL_DATASETS_MISSING SQLite caret_class_svmRadialCost_pipe 18 9
MODEL_DATASETS_MISSING SQLite caret_class_svmRadialSigma 18 9
MODEL_DATASETS_MISSING SQLite caret_class_svmRadialSigma_pipe 18 9
MODEL_DATASETS_MISSING SQLite caret_class_svmRadialWeights_pipe 18 9
MODEL_DATASETS_MISSING SQLite caret_class_svmRadial_pipe 18 10
MODEL_DATASETS_MISSING SQLite caret_class_xgbTree_pipe 18 9
MODEL_DATASETS_MISSING Teradata caret_class_glmnet 18 9
MODEL_DATASETS_MISSING Teradata caret_class_glmnet_pipe 18 10
In [15]:
missing_data.head()
Out[15]:
Model
dataset
dialect
DSN
status
error_message
elapsed_time
full_error_message
model_category
0
SVC
DS_BENCH_C_200_82_10_EA8E6ACF
IBM DB2
no_dsn
failure
TIMEOUT
None
TIMEOUT
sklearn.svm
1
caret_class_ctree2
DS_BENCH_C_200_22_2_2E64DEB6
IBM DB2
no_dsn
failure
TIMEOUT
None
TIMEOUT
caret.classifier
2
caret_class_ctree2
DS_BENCH_C_200_22_4_9802FBBD
IBM DB2
no_dsn
failure
TIMEOUT
None
TIMEOUT
caret.classifier
3
caret_class_ctree2
DS_BENCH_C_200_22_10_D28EF166
IBM DB2
no_dsn
failure
TIMEOUT
None
TIMEOUT
caret.classifier
4
caret_class_ctree2
DS_BENCH_C_200_82_10_EA8E6ACF
IBM DB2
no_dsn
failure
TIMEOUT
None
TIMEOUT
caret.classifier
In [16]:
df = df.append(missing_data , ignore_index=True)
In [17]:
df[df.error_message == "'"].head(200)
Out[17]:
Model
dataset
dialect
DSN
status
error_message
elapsed_time
full_error_message
model_category
In [18]:
df.shape
Out[18]:
(28910, 9)
In [19]:
df.dialect.value_counts()
Out[19]:
MonetDB 2891
Impala 2891
IBM DB2 2891
Firebird 2891
MS SQL Server 2891
Teradata 2891
SQLite 2891
PostgreSQL 2891
MariaDB 2891
Oracle 2891
Name: dialect, dtype: int64
In [20]:
#df0[df0.ds == numpy.na]
df_errors = df[(df.error_message != 'SUCCESS')]
indices = df_errors.error_message.apply(lambda x : 'dialect' not in x)
df_errors = df_errors[indices]
In [21]:
msg_by_estim_and_dsn = pd.DataFrame(df_errors.groupby(['dialect'])['error_message'].value_counts())
In [22]:
df4 = msg_by_estim_and_dsn.sort_values(by='error_message' , ascending=False)
df4.head(df4.shape[0])
/home/antoine/.local/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'error_message' is both an index level and a column label.
Defaulting to column, but this will raise an ambiguity error in a future version
"""Entry point for launching an IPython kernel.
Out[22]:
error_message
dialect
error_message
Impala
TIMEOUT
587
MonetDB
TIMEOUT
391
PostgreSQL
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
254
IBM DB2
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
254
MS SQL Server
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
244
Firebird
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
244
MariaDB
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
240
Oracle
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
239
Teradata
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
232
Firebird
TIMEOUT
217
SQLite
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
168
TIMEOUT
162
Teradata
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
160
MonetDB
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
146
Impala
Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...
142
MonetDB
Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...
141
Firebird
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
131
Impala
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
118
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
118
Teradata
TIMEOUT
112
Firebird
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
103
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
92
Oracle
TIMEOUT
92
Firebird
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while executing SQL statement:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range\', -802, 335544321) \[SQL ...
88
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
69
MariaDB
TIMEOUT
42
MS SQL Server
TIMEOUT
41
Impala
Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...
36
MariaDB
Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...
36
SQLite
Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...
36
...
...
FileNotFoundError ...
1
cTrainingError ...
1
IBM DB2
'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N Arithmetic overflow or other arithmetic exception occurred. SQLSTATE=22003 SQLCODE=-802 ,
1
cTrainingError ...
1
Exception:PREDICT_FAILED ...
1
'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N The name of the object to be created is identical to the existing name "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" of type "CREATE TEMPORARY TABLE". SQLSTATE=42710 SQLCODE=-601 \[SQL ...
1
'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" is an undefined name. SQLSTATE=42704 SQLCODE=-204 \[SQL ...
1
Firebird
cTrainingError ...
1
Teradata
'DatabaseError:(teradata.api.DatabaseError) (5639, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\') \[SQL ...
1
Exception:PREDICT_FAILED ...
1
SQLite
'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),
1
Impala
'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \'createTable\' RPC to Hive Metastore: \nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\n \[SQL ...
1
"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\n \[SQL ...
1
'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid
1
Oracle
IndexError ...
1
cTrainingError ...
1
MonetDB
cTrainingError ...
1
MariaDB
cTrainingError ...
1
FileNotFoundError ...
1
MS SQL Server
cTrainingError ...
1
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206010532_OEPNFRI_OVR_Score_1\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
PostgreSQL
FileNotFoundError ...
1
KeyError ...
1
cTrainingError ...
1
MS SQL Server
"OperationalError:(pymssql.OperationalError) (20004, b'DB-Lib error message 20004, severity 9:\\nRead from the server failed (db:1433)\\nNet-Lib error during Connection reset by peer (104)\\nDB-Lib error message 20002, severity 9:\\nAdaptive Server connection failed (db:1433)\\n') ,
1
Impala
cTrainingError ...
1
Oracle
FileNotFoundError ...
1
Teradata
cTrainingError ...
1
127 rows × 1 columns
In [23]:
sorted(df.error_message.unique().tolist())
Out[23]:
['"DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Exceeded the maximum depth of an expression tree (1000).\\n \\[SQL ...',
'"DatabaseError:(\'Error while commiting transaction:\\\\n- SQLCODE: -913\\\\n- deadlock\\\\n- update conflicts with concurrent update\\\\n- concurrent transaction number is ...',
'"DatabaseError:(fdb.fbcore.DatabaseError) (\'Cursor.fetchone:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\', -802, 335544321) ,',
'"DatabaseError:(fdb.fbcore.DatabaseError) (\'Cursor.fetchone:\\\\n- SQLCODE: -901\\\\n- Integer overflow. The result of an integer operation caused the most significant bit of the result to carry.\', -901, 335544779) ,',
'"DatabaseError:(teradata.api.DatabaseError) (10670, \'[07009] [Teradata][ODBC] (10670) Invalid descriptor index, descriptor record does not exist, or descriptor record was not properly initialized.\') ,',
'"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\\n \\[SQL ...',
'"OperationalError:(pymssql.OperationalError) (125, b\'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\') \\[SQL ...',
'"OperationalError:(pymssql.OperationalError) (125, b\'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\nDB-Lib error message 20018, severity 15:\\\\nGeneral SQL Server error: Check messages fro',
'"OperationalError:(pymssql.OperationalError) (20004, b\'DB-Lib error message 20004, severity 9:\\\\nRead from the server failed (db:1433)\\\\nNet-Lib error during Connection reset by peer (104)\\\\nDB-Lib error message 20002, severity 9:\\\\nAdaptive Server connection failed (db:1433)\\\\n\') ,',
'"OperationalError:(pymssql.OperationalError) (8115, b\'Arithmetic overflow error converting expression to data type float.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\') \\[SQL ...',
'"OperationalError:(pymssql.OperationalError) (8631, b\'Internal error: Server stack limit has been reached. Please look for potentially deep nesting in your query, and try to simplify it.DB-Lib error message 20018, severity 17:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n\') \\[SQL ...',
"'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,",
"'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N Arithmetic overflow or other arithmetic exception occurred. SQLSTATE=22003 SQLCODE=-802 ,",
"'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid ",
"'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \\'createTable\\' RPC to Hive Metastore: \\nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\\n \\[SQL ...",
"'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0840N Too many items were returned in a SELECT list. SQLSTATE=54004 SQLCODE=-840 \\[SQL ...",
"'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL1585N A temporary table could not be created because there is no available system temporary table space that has a compatible page size. SQLSTATE=54048 SQLCODE=-1585 \\[SQL ...",
"'DataError:(psycopg2.DataError) value out of range: overflow\\n \\[SQL ...",
"'DataError:(psycopg2.DataError) value out of range: underflow\\n \\[SQL ...",
"'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while executing SQL statement:\\\\n- SQLCODE: -802\\\\n- arithmetic exception, numeric overflow, or string truncation\\\\n- numeric value is out of range\\', -802, 335544321) \\[SQL ...",
"'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Token unknown ...",
"'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -104\\\\n- Dynamic SQL Error\\\\n- SQL error code = -104\\\\n- Unexpected end of command ...",
"'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -901\\\\n- request size limit exceeded\\', -901, 335544382) \\[SQL ...",
"'DatabaseError:(fdb.fbcore.DatabaseError) (\\'Error while preparing SQL statement:\\\\n- SQLCODE: -902\\\\n- Dynamic SQL Error\\\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\\', -902, 335544569) \\[SQL ...",
"'DatabaseError:(teradata.api.DatabaseError) (2616, \\'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\\') \\[SQL ...",
"'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\\') \\[SQL ...",
"'DatabaseError:(teradata.api.DatabaseError) (3710, \\'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\\') \\[SQL ...",
"'DatabaseError:(teradata.api.DatabaseError) (3754, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\\') \\[SQL ...",
"'DatabaseError:(teradata.api.DatabaseError) (5639, \\'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\\') \\[SQL ...",
'\'InternalError:(ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0973N Not enough storage is available in the "AGENT_STACK_SZ" heap or stack to process the statement. SQLSTATE=57011 SQLCODE=-973 \\[SQL ...',
'\'OperationalError:(_mysql_exceptions.OperationalError) (1436, "Thread stack overrun: 1238176 bytes used of a 1269760 byte stack, and 32000 bytes needed. Use \\\'mysqld --thread_stack=#\\\' to specify a bigger stack") \\[SQL ...',
'\'OperationalError:(_mysql_exceptions.OperationalError) (1690, "DOUBLE value is out of range in ...',
'\'OperationalError:(psycopg2.OperationalError) stack depth limit exceeded\\nHINT: Increase the configuration parameter "max_stack_depth" (currently 2048kB), after ensuring the platform\\\'s stack depth limit is adequate.\\n \\[SQL ...',
"'OperationalError:(psycopg2.OperationalError) target lists can have at most 1664 entries\\n \\[SQL ...",
"'OperationalError:(pymonetdb.exceptions.OperationalError) Math exception: Numerical argument out of domain\\n \\[SQL ...",
'\'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \\\'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n") \\[SQL ...',
'\'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \\\'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n") \\[SQL ...',
'\'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \\\'##TMP_20181206010532_OEPNFRI_OVR_Score_1\\\' in the database.DB-Lib error message 20018, severity 16:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n") \\[SQL ...',
'\'OperationalError:(pymssql.OperationalError) (701, b"There is insufficient system memory in resource pool \\\'default\\\' to run this query.DB-Lib error message 20018, severity 17:\\\\nGeneral SQL Server error: Check messages from the SQL Server\\\\n") \\[SQL ...',
"'OperationalError:(sqlite3.OperationalError) Expression tree is too large (maximum depth 1000) \\[SQL ...",
"'OperationalError:(sqlite3.OperationalError) parser stack overflow \\[SQL ...",
"'OperationalError:(sqlite3.OperationalError) too many columns in result set \\[SQL ...",
"'OperationalError:(sqlite3.OperationalError) too many terms in compound SELECT \\[SQL ...",
"'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0419N A decimal divide operation is not valid because the result would have a negative scale. SQLSTATE=42911 SQLCODE=-419 \\[SQL ...",
'\'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" is an undefined name. SQLSTATE=42704 SQLCODE=-204 \\[SQL ...',
'\'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N The name of the object to be created is identical to the existing name "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" of type "CREATE TEMPORARY TABLE". SQLSTATE=42710 SQLCODE=-601 \\[SQL ...',
"'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),",
"'ValueError:Length mismatch: Expected axis has 5 elements, new values have 20 elements'),",
'DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...',
'Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...',
'Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...',
'Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...',
'Exception:CONNECTION_FAILED_WITH_ERROR ...',
'Exception:PREDICT_FAILED ...',
'FileNotFoundError ...',
'IndexError ...',
'KeyError ...',
'NAN_VALUE_ENCOUNTERED_IN_MODEL ...',
'SUCCESS',
'TIMEOUT',
'cTrainingError ...',
'cTrainingError:Exception:TRAIN_FAILED ...']
In [24]:
pd.DataFrame(df.error_message.value_counts())
Out[24]:
error_message
SUCCESS
23280
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1987
TIMEOUT
1680
Exception:CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL ...
358
Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...
283
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
244
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
160
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
131
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
118
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
103
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while executing SQL statement:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range\', -802, 335544321) \[SQL ...
88
cTrainingError:Exception:TRAIN_FAILED ...
78
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
69
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
35
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
31
Exception:CONNECTION_FAILED_WITH_ERROR ...
30
"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\nDB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\nDB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages fro
23
'DatabaseError:(teradata.api.DatabaseError) (2616, \'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\') \[SQL ...
21
'OperationalError:(_mysql_exceptions.OperationalError) (1690, "DOUBLE value is out of range in ...
20
"OperationalError:(pymssql.OperationalError) (8115, b'Arithmetic overflow error converting expression to data type float.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n') \[SQL ...
18
'DataError:(psycopg2.DataError) value out of range: overflow\n \[SQL ...
13
'OperationalError:(pymonetdb.exceptions.OperationalError) Math exception: Numerical argument out of domain\n \[SQL ...
12
cTrainingError ...
10
'DataError:(psycopg2.DataError) value out of range: underflow\n \[SQL ...
8
FileNotFoundError ...
8
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Token unknown ...
6
'OperationalError:(sqlite3.OperationalError) parser stack overflow \[SQL ...
6
"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\n') \[SQL ...
6
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -901\\n- request size limit exceeded\', -901, 335544382) \[SQL ...
6
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\') \[SQL ...
6
...
...
"DatabaseError:(teradata.api.DatabaseError) (10670, '[07009] [Teradata][ODBC] (10670) Invalid descriptor index, descriptor record does not exist, or descriptor record was not properly initialized.') ,
4
'OperationalError:(psycopg2.OperationalError) target lists can have at most 1664 entries\n \[SQL ...
4
'OperationalError:(sqlite3.OperationalError) too many terms in compound SELECT \[SQL ...
4
'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0840N Too many items were returned in a SELECT list. SQLSTATE=54004 SQLCODE=-840 \[SQL ...
4
'OperationalError:(sqlite3.OperationalError) too many columns in result set \[SQL ...
4
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\') \[SQL ...
3
IndexError ...
3
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -901\\n- Integer overflow. The result of an integer operation caused the most significant bit of the result to carry.', -901, 335544779) ,
3
'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),
3
"OperationalError:(pymssql.OperationalError) (8631, b'Internal error: Server stack limit has been reached. Please look for potentially deep nesting in your query, and try to simplify it.DB-Lib error message 20018, severity 17:\\nGeneral SQL Server error: Check messages from the SQL Server\\n') \[SQL ...
2
'OperationalError:(_mysql_exceptions.OperationalError) (1436, "Thread stack overrun: 1238176 bytes used of a 1269760 byte stack, and 32000 bytes needed. Use \'mysqld --thread_stack=#\' to specify a bigger stack") \[SQL ...
2
'OperationalError:(sqlite3.OperationalError) Expression tree is too large (maximum depth 1000) \[SQL ...
2
'InternalError:(ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0973N Not enough storage is available in the "AGENT_STACK_SZ" heap or stack to process the statement. SQLSTATE=57011 SQLCODE=-973 \[SQL ...
2
'OperationalError:(psycopg2.OperationalError) stack depth limit exceeded\nHINT: Increase the configuration parameter "max_stack_depth" (currently 2048kB), after ensuring the platform\'s stack depth limit is adequate.\n \[SQL ...
2
'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL1585N A temporary table could not be created because there is no available system temporary table space that has a compatible page size. SQLSTATE=54048 SQLCODE=-1585 \[SQL ...
2
"DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Exceeded the maximum depth of an expression tree (1000).\n \[SQL ...
2
'OperationalError:(pymssql.OperationalError) (701, b"There is insufficient system memory in resource pool \'default\' to run this query.DB-Lib error message 20018, severity 17:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
2
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206010532_OEPNFRI_OVR_Score_1\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
'DatabaseError:(teradata.api.DatabaseError) (5639, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\') \[SQL ...
1
'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N Arithmetic overflow or other arithmetic exception occurred. SQLSTATE=22003 SQLCODE=-802 ,
1
'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N The name of the object to be created is identical to the existing name "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" of type "CREATE TEMPORARY TABLE". SQLSTATE=42710 SQLCODE=-601 \[SQL ...
1
"OperationalError:(pymssql.OperationalError) (20004, b'DB-Lib error message 20004, severity 9:\\nRead from the server failed (db:1433)\\nNet-Lib error during Connection reset by peer (104)\\nDB-Lib error message 20002, severity 9:\\nAdaptive Server connection failed (db:1433)\\n') ,
1
'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid
1
'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" is an undefined name. SQLSTATE=42704 SQLCODE=-204 \[SQL ...
1
'ValueError:Length mismatch: Expected axis has 5 elements, new values have 20 elements'),
1
"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\n \[SQL ...
1
KeyError ...
1
'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \'createTable\' RPC to Hive Metastore: \nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\n \[SQL ...
1
62 rows × 1 columns
In [25]:
lGroupBy = df[df.status == 'failure'].groupby(['error_message'])
In [26]:
#lGroupBy['rows'].describe()
In [27]:
lComparisonErrorMessage = '"Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS'
real_errors = df[df.error_message.str.contains(lComparisonErrorMessage)]
real_errors.Model.value_counts()
Out[27]:
Series([], Name: Model, dtype: int64)
In [28]:
lNotImplementedMessage = 'CODE_GENERATION_NOT_IMPLEMENTED_FOR_MODEL'
not_impl_errors = df[df.error_message.str.contains(lNotImplementedMessage)]
not_impl_errors.Model.value_counts()
Out[28]:
MiniBatchSparsePCA 120
SparsePCA 118
LatentDirichletAllocation 60
NMF 60
Name: Model, dtype: int64
In [29]:
# database related errors
def is_other_error(x):
lKnownError = 'SUCCESS' in [x] or lComparisonErrorMessage in [x] or lNotImplementedMessage in [x]
return not lKnownError
# other_errors = df.error_message.apply(lambda x : 1 if is_other_error(x) else 0)
other_errors = df[df.error_message.apply(is_other_error)]
other_errors.Model.value_counts()
Out[29]:
caret_class_svmRadialSigma 180
caret_class_svmRadialWeights_pipe 180
caret_class_svmRadialWeights 180
caret_class_svmLinear_pipe 180
caret_class_svmRadialCost 180
caret_class_svmLinear 180
caret_class_svmRadialSigma_pipe 180
caret_class_svmRadialCost_pipe 180
caret_class_svmRadial_pipe 159
caret_class_svmRadial 128
caret_class_svmPoly_pipe 120
caret_class_svmPoly 120
MiniBatchSparsePCA 120
SparsePCA 120
caret_class_nnet_pipe 98
caret_class_xgbTree_pipe 97
caret_class_nnet 96
keras_class_SimpleRNN_pipe 95
caret_class_xgbTree 92
keras_class_SimpleRNN 85
caret_class_glmnet_pipe 78
caret_class_rf_pipe 75
keras_class_LSTM_pipe 75
keras_class_LSTM 73
caret_class_earth_pipe 71
caret_class_earth 67
DummyClassifier_pipe 65
DummyClassifier 61
NMF 60
LatentDirichletAllocation 60
...
EllipticEnvelope_pipe 2
RandomForestRegressor_pipe 2
Perceptron_pipe 2
caret_reg_earth_pipe 2
caret_prep_ica 2
caret_reg_xgbTree 2
DecisionTreeClassifier_pipe 2
RandomForestRegressor 2
LinearRegression_pipe 2
caret_reg_nnet_pipe 2
PassiveAggressiveClassifier_pipe 2
Lasso_pipe 1
OneClassSVM_pipe 1
SGDClassifier 1
MLPRegressor 1
RidgeClassifier_pipe 1
caret_reg_ctree 1
LassoLars_pipe 1
MLPRegressor_pipe 1
RANSACRegressor_pipe 1
RidgeClassifierCV_pipe 1
LassoLarsIC_pipe 1
RidgeClassifierCV 1
LinearSVC_pipe 1
Pipeline 1
GradientBoostingRegressor 1
PassiveAggressiveRegressor_pipe 1
caret_reg_rpart 1
keras_reg_Dense 1
ExtraTreeRegressor_pipe 1
Name: Model, Length: 179, dtype: int64
In [30]:
# df.pivot(index = 'Model', values='status' , columns='dialect')
In [31]:
df.columns
Out[31]:
Index(['Model', 'dataset', 'dialect', 'DSN', 'status', 'error_message',
'elapsed_time', 'full_error_message', 'model_category'],
dtype='object')
In [32]:
df.head()
Out[32]:
Model
dataset
dialect
DSN
status
error_message
elapsed_time
full_error_message
model_category
0
LGBMClassifier
DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
1
LGBMClassifier
DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
2
LGBMClassifier_pipe
p_DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
3
LGBMClassifier
DS_BENCH_C_200_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
4
LGBMClassifier_pipe
p_DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
success
SUCCESS
3
None),
lightgbm.sklearn
In [33]:
df['status'] = df['status'].apply(lambda x : 1 if('failure' in x) else 0)
df['status_2'] = df['status'].apply(lambda x : 1)
In [34]:
df = df[df['model_category'] != 'sklearn.dummy']
df = df[~df['error_message'].str.contains(lNotImplementedMessage)]
In [35]:
df.head()
Out[35]:
Model
dataset
dialect
DSN
status
error_message
elapsed_time
full_error_message
model_category
status_2
0
LGBMClassifier
DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
0
SUCCESS
3
None),
lightgbm.sklearn
1
1
LGBMClassifier
DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
0
SUCCESS
3
None),
lightgbm.sklearn
1
2
LGBMClassifier_pipe
p_DS_BENCH_C_50_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
0
SUCCESS
3
None),
lightgbm.sklearn
1
3
LGBMClassifier
DS_BENCH_C_200_7_2_F2617B35
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
0
SUCCESS
3
None),
lightgbm.sklearn
1
4
LGBMClassifier_pipe
p_DS_BENCH_C_50_22_2_2E64DEB6
IBM DB2
'db2+ibm_db://db:db@localhost:50000/db',
0
SUCCESS
3
None),
lightgbm.sklearn
1
In [36]:
# How many tests were used for each model category
pvt_count = pd.pivot_table(df, index='model_category', values='status_2' , columns=['dialect'], aggfunc=[np.sum], margins=True)
pvt_count.head(pvt_count.shape[0])
Out[36]:
sum
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
model_category
caret.classifier
503
503
503
503
503
503
503
503
503
503
5030
caret.preprocessor
36
36
36
36
36
36
36
36
36
36
360
caret.regressor
168
168
168
168
168
168
168
168
168
168
1680
keras.classifier
144
144
144
144
144
144
144
144
144
144
1440
keras.regressor
48
48
48
48
48
48
48
48
48
48
480
lightgbm.sklearn
48
48
48
48
48
48
48
48
48
48
480
sklearn.calibration
36
36
36
36
36
36
36
36
36
36
360
sklearn.covariance
12
12
12
12
12
12
12
12
12
12
120
sklearn.decomposition
72
72
72
72
72
72
72
72
72
74
722
sklearn.discriminant_analysis
36
36
36
36
36
36
36
36
36
36
360
sklearn.ensemble
252
252
252
252
252
252
252
252
252
252
2520
sklearn.feature_selection
108
108
108
108
108
108
108
108
108
108
1080
sklearn.impute
24
24
24
24
24
24
24
24
24
24
240
sklearn.kernel_ridge
12
12
12
12
12
12
12
12
12
12
120
sklearn.linear_model
492
492
492
492
492
492
492
492
492
492
4920
sklearn.multiclass
72
72
72
72
72
72
72
72
72
72
720
sklearn.naive_bayes
144
144
144
144
144
144
144
144
144
144
1440
sklearn.neural_network
48
48
48
48
48
48
48
48
48
48
480
sklearn.pipeline
48
48
48
48
48
48
48
48
48
48
480
sklearn.preprocessing
180
180
180
180
180
180
180
180
180
180
1800
sklearn.random_projection
24
24
24
24
24
24
24
24
24
24
240
sklearn.svm
156
156
156
156
156
156
156
156
156
156
1560
sklearn.tree
96
96
96
96
96
96
96
96
96
96
960
xgboost.sklearn
48
48
48
48
48
48
48
48
48
48
480
All
2807
2807
2807
2807
2807
2807
2807
2807
2807
2809
28072
In [37]:
pvt = pd.pivot_table(df, index='model_category', values='status' , columns=['dialect'], aggfunc=[np.mean], margins=True)
In [38]:
pvt.head(pvt.shape[0])
Out[38]:
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
model_category
caret.classifier
0.759443
0.495030
0.691849
0.560636
0.481113
0.755467
0.485089
0.461233
0.586481
0.606362
0.588270
caret.preprocessor
0.111111
0.027778
0.000000
0.000000
0.000000
0.000000
0.000000
0.083333
0.027778
0.027778
0.027778
caret.regressor
0.369048
0.011905
0.023810
0.047619
0.011905
0.000000
0.011905
0.029762
0.023810
0.267857
0.079762
keras.classifier
0.291667
0.097222
0.791667
0.222222
0.236111
0.750000
0.097222
0.243056
0.097222
0.118056
0.294444
keras.regressor
0.166667
0.062500
0.750000
0.062500
0.020833
0.750000
0.062500
0.062500
0.062500
0.083333
0.208333
lightgbm.sklearn
0.666667
0.020833
0.625000
0.000000
0.020833
0.416667
0.020833
0.000000
0.000000
0.145833
0.191667
sklearn.calibration
0.138889
0.000000
0.611111
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.083333
0.083333
sklearn.covariance
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.333333
0.183333
sklearn.decomposition
0.222222
0.041667
0.194444
0.027778
0.027778
0.194444
0.027778
0.027778
0.027778
0.202703
0.099723
sklearn.discriminant_analysis
0.111111
0.055556
0.777778
0.027778
0.055556
0.055556
0.055556
0.027778
0.027778
0.138889
0.133333
sklearn.ensemble
0.500000
0.011905
0.361111
0.007937
0.007937
0.091270
0.007937
0.007937
0.007937
0.261905
0.126587
sklearn.feature_selection
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
sklearn.impute
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
sklearn.kernel_ridge
0.750000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.083333
0.083333
sklearn.linear_model
0.032520
0.002033
0.111789
0.004065
0.002033
0.002033
0.002033
0.002033
0.002033
0.028455
0.018902
sklearn.multiclass
0.458333
0.000000
0.500000
0.013889
0.000000
0.000000
0.000000
0.000000
0.000000
0.250000
0.122222
sklearn.naive_bayes
0.395833
0.041667
0.652778
0.027778
0.027778
0.472222
0.027778
0.027778
0.027778
0.138889
0.184028
sklearn.neural_network
0.354167
0.000000
0.479167
0.000000
0.000000
0.333333
0.000000
0.000000
0.000000
0.083333
0.125000
sklearn.pipeline
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.125000
0.012500
sklearn.preprocessing
0.111111
0.088889
0.033333
0.033333
0.033333
0.094444
0.033333
0.022222
0.077778
0.100000
0.062778
sklearn.random_projection
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.016667
sklearn.svm
0.538462
0.019231
0.282051
0.006410
0.000000
0.076923
0.217949
0.000000
0.000000
0.096154
0.123718
sklearn.tree
0.020833
0.000000
0.000000
0.000000
0.000000
0.000000
0.135417
0.000000
0.000000
0.020833
0.017708
xgboost.sklearn
0.645833
0.020833
0.645833
0.020833
0.020833
0.354167
0.020833
0.020833
0.020833
0.041667
0.181250
All
0.340577
0.109369
0.348415
0.123620
0.106876
0.255077
0.117919
0.105094
0.122551
0.203631
0.183314
In [39]:
df.to_csv('report_extensive_tests.csv')
In [40]:
%matplotlib inline
import matplotlib.pyplot as plt
plt.pcolor(1-pvt, cmap='RdYlGn' , vmin=0 , vmax=1)
plt.yticks(np.arange(0.5, len(pvt.index), 1), pvt.index)
plt.xticks(np.arange(0.5, len(pvt.columns), 1), [col[1] for col in pvt.columns])
fig = plt.gcf()
fig.set_size_inches(16, 8)
plt.show()
In [41]:
Category_Labels = df.model_category.unique()
In [42]:
Category_Labels
Out[42]:
array(['lightgbm.sklearn', 'caret.classifier', 'caret.preprocessor',
'caret.regressor', 'keras.classifier', 'keras.regressor',
'sklearn.calibration', 'sklearn.covariance',
'sklearn.decomposition', 'sklearn.discriminant_analysis',
'sklearn.ensemble', 'sklearn.feature_selection', 'sklearn.impute',
'sklearn.kernel_ridge', 'sklearn.linear_model',
'sklearn.multiclass', 'sklearn.naive_bayes',
'sklearn.neural_network', 'sklearn.pipeline',
'sklearn.preprocessing', 'sklearn.random_projection',
'sklearn.svm', 'sklearn.tree', 'xgboost.sklearn'], dtype=object)
In [43]:
for cat in Category_Labels:
print("ERROR_REPORT_FOR_CATEGORY" , cat)
df1 = df[df.model_category == cat]
real_errors = df1 # df1[df1.error_message != "SUCCESS"]
if(real_errors.shape[0] > 0):
msg_by_estim_and_dsn = pd.DataFrame(real_errors.groupby(['dialect'])['error_message'].value_counts())
from IPython.core.display import display, HTML
display(msg_by_estim_and_dsn)
pvt1 = pd.pivot_table(df1, index='Model', values='status' , columns=['dialect'], aggfunc=[np.mean], margins=True)
display(pvt1)
else:
print("NO_ERROR_FOR_CATEGORY" , cat)
ERROR_REPORT_FOR_CATEGORY lightgbm.sklearn
error_message
dialect
error_message
Firebird
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
27
SUCCESS
16
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while executing SQL statement:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range\', -802, 335544321) \[SQL ...
5
IBM DB2
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
Impala
TIMEOUT
22
SUCCESS
18
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
8
MS SQL Server
SUCCESS
48
MariaDB
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
MonetDB
SUCCESS
28
TIMEOUT
16
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
4
Oracle
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
PostgreSQL
SUCCESS
48
SQLite
SUCCESS
48
Teradata
SUCCESS
41
'DatabaseError:(teradata.api.DatabaseError) (2616, \'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\') \[SQL ...
5
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
1
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
LGBMClassifier
0.833333
0.000000
0.833333
0.0
0.000000
0.555556
0.000000
0.0
0.0
0.166667
0.238889
LGBMClassifier_pipe
0.611111
0.055556
0.833333
0.0
0.055556
0.555556
0.055556
0.0
0.0
0.166667
0.233333
LGBMRegressor
0.500000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.0
0.0
0.000000
0.050000
LGBMRegressor_pipe
0.500000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.0
0.0
0.166667
0.066667
All
0.666667
0.020833
0.625000
0.0
0.020833
0.416667
0.020833
0.0
0.0
0.145833
0.191667
ERROR_REPORT_FOR_CATEGORY caret.classifier
error_message
dialect
error_message
Firebird
TIMEOUT
216
SUCCESS
121
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
59
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
35
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while executing SQL statement:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range\', -802, 335544321) \[SQL ...
32
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
20
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
13
cTrainingError:Exception:TRAIN_FAILED ...
4
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
3
IBM DB2
SUCCESS
254
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
207
TIMEOUT
32
cTrainingError:Exception:TRAIN_FAILED ...
6
FileNotFoundError ...
2
IndexError ...
2
Impala
TIMEOUT
241
SUCCESS
155
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
78
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
16
cTrainingError:Exception:TRAIN_FAILED ...
13
MS SQL Server
SUCCESS
221
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
210
TIMEOUT
40
"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\nDB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\nDB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages fro
15
cTrainingError:Exception:TRAIN_FAILED ...
12
'OperationalError:(pymssql.OperationalError) (701, b"There is insufficient system memory in resource pool \'default\' to run this query.DB-Lib error message 20018, severity 17:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
2
FileNotFoundError ...
2
"OperationalError:(pymssql.OperationalError) (20004, b'DB-Lib error message 20004, severity 9:\\nRead from the server failed (db:1433)\\nNet-Lib error during Connection reset by peer (104)\\nDB-Lib error message 20002, severity 9:\\nAdaptive Server connection failed (db:1433)\\n') ,
1
MariaDB
SUCCESS
261
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
197
...
...
FileNotFoundError ...
1
MonetDB
TIMEOUT
266
SUCCESS
123
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
111
cTrainingError:Exception:TRAIN_FAILED ...
2
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
1
Oracle
SUCCESS
259
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
183
TIMEOUT
56
'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),
2
'ValueError:Length mismatch: Expected axis has 5 elements, new values have 20 elements'),
1
FileNotFoundError ...
1
IndexError ...
1
PostgreSQL
SUCCESS
271
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
219
cTrainingError:Exception:TRAIN_FAILED ...
9
TIMEOUT
3
FileNotFoundError ...
1
SQLite
SUCCESS
208
TIMEOUT
161
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
133
FileNotFoundError ...
1
Teradata
SUCCESS
198
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
129
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
101
TIMEOUT
60
cTrainingError:Exception:TRAIN_FAILED ...
10
'DatabaseError:(teradata.api.DatabaseError) (2616, \'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\') \[SQL ...
2
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\') \[SQL ...
2
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\') \[SQL ...
1
62 rows × 1 columns
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
caret_class_ctree
0.444444
0.000000
0.000000
0.000000
0.000000
0.388889
0.166667
0.111111
0.222222
0.055556
0.138889
caret_class_ctree2
0.388889
0.500000
0.000000
0.500000
0.000000
0.388889
0.166667
0.000000
0.000000
0.000000
0.194444
caret_class_ctree2_pipe
0.444444
0.500000
0.333333
1.000000
0.000000
0.444444
0.111111
0.000000
0.000000
0.000000
0.283333
caret_class_ctree_pipe
0.555556
0.000000
0.055556
0.000000
0.055556
0.500000
0.111111
0.000000
0.388889
0.055556
0.172222
caret_class_earth
0.611111
0.166667
0.777778
0.333333
0.222222
0.555556
0.166667
0.166667
0.388889
0.333333
0.372222
caret_class_earth_pipe
0.611111
0.166667
0.777778
0.277778
0.277778
0.611111
0.166667
0.166667
0.555556
0.333333
0.394444
caret_class_glm
0.000000
0.000000
0.500000
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.066667
caret_class_glm_pipe
0.500000
0.333333
0.500000
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
0.366667
caret_class_glmnet
0.555556
0.000000
0.722222
0.166667
0.055556
0.777778
0.000000
0.000000
0.111111
0.611111
0.300000
caret_class_glmnet_pipe
0.722222
0.166667
0.833333
0.166667
0.166667
0.888889
0.111111
0.111111
0.444444
0.722222
0.433333
caret_class_nnet
0.833333
0.333333
0.777778
0.333333
0.444444
0.888889
0.333333
0.333333
0.555556
0.500000
0.533333
caret_class_nnet_pipe
0.833333
0.333333
0.833333
0.333333
0.388889
0.888889
0.333333
0.333333
0.666667
0.500000
0.544444
caret_class_rf
0.555556
0.000000
0.555556
0.444444
0.000000
0.388889
0.000000
0.000000
0.055556
0.833333
0.283333
caret_class_rf_pipe
0.611111
0.111111
0.611111
0.500000
0.166667
0.555556
0.111111
0.111111
0.444444
0.944444
0.416667
caret_class_rpart
0.444444
0.055556
0.000000
0.000000
0.000000
0.388889
0.055556
0.000000
0.000000
0.000000
0.094444
caret_class_rpart_pipe
0.555556
0.000000
0.000000
0.000000
0.055556
0.500000
0.111111
0.000000
0.277778
0.055556
0.155556
caret_class_svmLinear
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmLinear_pipe
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmPoly
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmPoly_pipe
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmRadial
0.941176
0.647059
0.823529
0.705882
0.705882
0.823529
0.705882
0.705882
0.764706
0.705882
0.752941
caret_class_svmRadialCost
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmRadialCost_pipe
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmRadialSigma
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmRadialSigma_pipe
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmRadialWeights
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmRadialWeights_pipe
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
caret_class_svmRadial_pipe
1.000000
0.777778
0.944444
0.777778
0.833333
0.944444
0.833333
0.777778
0.944444
1.000000
0.883333
caret_class_xgbTree
0.833333
0.333333
0.833333
0.333333
0.333333
0.888889
0.333333
0.333333
0.500000
0.388889
0.511111
caret_class_xgbTree_pipe
0.833333
0.333333
0.833333
0.333333
0.333333
0.888889
0.333333
0.333333
0.666667
0.500000
0.538889
All
0.759443
0.495030
0.691849
0.560636
0.481113
0.755467
0.485089
0.461233
0.586481
0.606362
0.588270
ERROR_REPORT_FOR_CATEGORY caret.preprocessor
error_message
dialect
error_message
Firebird
SUCCESS
32
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
4
IBM DB2
SUCCESS
35
Exception:PREDICT_FAILED ...
1
Impala
SUCCESS
36
MS SQL Server
SUCCESS
36
MariaDB
SUCCESS
36
MonetDB
SUCCESS
36
Oracle
SUCCESS
36
PostgreSQL
SUCCESS
33
Exception:PREDICT_FAILED ...
3
SQLite
SUCCESS
35
Exception:PREDICT_FAILED ...
1
Teradata
SUCCESS
35
Exception:PREDICT_FAILED ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
caret_prep_center_scale
0.000000
0.000000
0.0
0.0
0.0
0.0
0.0
0.250000
0.000000
0.000000
0.025000
caret_prep_ica
0.000000
0.083333
0.0
0.0
0.0
0.0
0.0
0.000000
0.083333
0.000000
0.016667
caret_prep_pca
0.333333
0.000000
0.0
0.0
0.0
0.0
0.0
0.000000
0.000000
0.083333
0.041667
All
0.111111
0.027778
0.0
0.0
0.0
0.0
0.0
0.083333
0.027778
0.027778
0.027778
ERROR_REPORT_FOR_CATEGORY caret.regressor
error_message
dialect
error_message
Firebird
SUCCESS
106
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
46
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
15
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
1
IBM DB2
SUCCESS
166
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
Impala
SUCCESS
164
cTrainingError:Exception:TRAIN_FAILED ...
4
MS SQL Server
SUCCESS
160
"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\nDB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\nDB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages fro
8
MariaDB
SUCCESS
166
cTrainingError:Exception:TRAIN_FAILED ...
2
MonetDB
SUCCESS
168
Oracle
SUCCESS
166
cTrainingError:Exception:TRAIN_FAILED ...
2
PostgreSQL
SUCCESS
163
cTrainingError:Exception:TRAIN_FAILED ...
4
KeyError ...
1
SQLite
SUCCESS
164
cTrainingError:Exception:TRAIN_FAILED ...
2
'ValueError:Length mismatch: Expected axis has 20 elements, new values have 5 elements'),
1
TIMEOUT
1
Teradata
SUCCESS
123
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
30
cTrainingError:Exception:TRAIN_FAILED ...
6
'DatabaseError:(teradata.api.DatabaseError) (2616, \'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\') \[SQL ...
4
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\') \[SQL ...
3
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
caret_reg_ctree
0.000000
0.000000
0.166667
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.016667
caret_reg_ctree2
0.000000
0.000000
0.166667
0.000000
0.000000
0.0
0.000000
0.000000
0.166667
0.000000
0.033333
caret_reg_ctree2_pipe
0.333333
0.000000
0.166667
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.050000
caret_reg_ctree_pipe
0.333333
0.000000
0.166667
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.050000
caret_reg_earth
0.000000
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.000000
caret_reg_earth_pipe
0.333333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.033333
caret_reg_glm
0.000000
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.000000
caret_reg_glm_pipe
0.333333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.033333
caret_reg_glmnet
0.000000
0.000000
0.000000
0.000000
0.166667
0.0
0.166667
0.000000
0.000000
0.000000
0.033333
caret_reg_glmnet_pipe
0.333333
0.000000
0.000000
0.000000
0.166667
0.0
0.166667
0.000000
0.000000
0.000000
0.066667
caret_reg_nnet
0.000000
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.000000
caret_reg_nnet_pipe
0.333333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.033333
caret_reg_rf
0.000000
0.000000
0.000000
0.500000
0.000000
0.0
0.000000
0.000000
0.000000
0.666667
0.116667
caret_reg_rf_pipe
0.333333
0.000000
0.000000
0.833333
0.000000
0.0
0.000000
0.000000
0.000000
0.666667
0.183333
caret_reg_rpart
0.000000
0.166667
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.016667
caret_reg_rpart_pipe
0.333333
0.166667
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.000000
0.050000
caret_reg_svmLinear
0.500000
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.166667
0.000000
0.333333
0.100000
caret_reg_svmLinear_pipe
0.666667
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
1.000000
0.166667
caret_reg_svmPoly
0.500000
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.166667
0.066667
caret_reg_svmPoly_pipe
0.833333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.166667
1.000000
0.200000
caret_reg_svmRadial
0.833333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.166667
0.100000
caret_reg_svmRadialCost
0.666667
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.166667
0.333333
0.116667
caret_reg_svmRadialCost_pipe
0.666667
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.166667
1.000000
0.183333
caret_reg_svmRadialSigma
0.833333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
0.166667
0.100000
caret_reg_svmRadialSigma_pipe
0.833333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
1.000000
0.183333
caret_reg_svmRadial_pipe
0.833333
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.000000
1.000000
0.183333
caret_reg_xgbTree
0.000000
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.333333
0.000000
0.000000
0.033333
caret_reg_xgbTree_pipe
0.500000
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.333333
0.000000
0.000000
0.083333
All
0.369048
0.011905
0.023810
0.047619
0.011905
0.0
0.011905
0.029762
0.023810
0.267857
0.079762
ERROR_REPORT_FOR_CATEGORY keras.classifier
error_message
dialect
error_message
Firebird
SUCCESS
102
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
30
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
11
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
IBM DB2
SUCCESS
130
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
13
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
Impala
Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...
107
SUCCESS
30
TIMEOUT
6
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
MS SQL Server
SUCCESS
112
"OperationalError:(pymssql.OperationalError) (8115, b'Arithmetic overflow error converting expression to data type float.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n') \[SQL ...
18
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
13
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
MariaDB
SUCCESS
110
'OperationalError:(_mysql_exceptions.OperationalError) (1690, "DOUBLE value is out of range in ...
20
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
13
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
MonetDB
Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...
107
SUCCESS
36
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
Oracle
SUCCESS
130
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
13
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
PostgreSQL
SUCCESS
109
'DataError:(psycopg2.DataError) value out of range: overflow\n \[SQL ...
13
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
13
'DataError:(psycopg2.DataError) value out of range: underflow\n \[SQL ...
8
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
SQLite
SUCCESS
130
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
13
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
Teradata
SUCCESS
127
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
12
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
4
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
keras_class_Dense
0.277778
0.000000
0.111111
0.000000
0.000000
0.00
0.000000
0.000000
0.000000
0.000000
0.038889
keras_class_Dense_pipe
0.333333
0.000000
0.222222
0.000000
0.000000
0.00
0.000000
0.000000
0.000000
0.055556
0.061111
keras_class_GRU
0.111111
0.000000
1.000000
0.000000
0.000000
1.00
0.000000
0.000000
0.000000
0.000000
0.211111
keras_class_GRU_pipe
0.166667
0.000000
1.000000
0.000000
0.000000
1.00
0.000000
0.000000
0.000000
0.055556
0.222222
keras_class_LSTM
0.166667
0.055556
1.000000
0.555556
0.555556
1.00
0.055556
0.555556
0.055556
0.055556
0.405556
keras_class_LSTM_pipe
0.333333
0.000000
1.000000
0.500000
0.611111
1.00
0.000000
0.666667
0.000000
0.055556
0.416667
keras_class_SimpleRNN
0.388889
0.333333
1.000000
0.333333
0.333333
1.00
0.333333
0.333333
0.333333
0.333333
0.472222
keras_class_SimpleRNN_pipe
0.555556
0.388889
1.000000
0.388889
0.388889
1.00
0.388889
0.388889
0.388889
0.388889
0.527778
All
0.291667
0.097222
0.791667
0.222222
0.236111
0.75
0.097222
0.243056
0.097222
0.118056
0.294444
ERROR_REPORT_FOR_CATEGORY keras.regressor
error_message
dialect
error_message
Firebird
SUCCESS
40
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
5
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
3
IBM DB2
SUCCESS
45
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
3
Impala
Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...
35
SUCCESS
12
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
MS SQL Server
SUCCESS
45
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
3
MariaDB
SUCCESS
47
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
1
MonetDB
Exception:CODE_GENERATION_DATABASE_NOT_SUPPORTED ...
34
SUCCESS
12
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
2
Oracle
SUCCESS
45
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
3
PostgreSQL
SUCCESS
45
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
3
SQLite
SUCCESS
45
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
3
Teradata
SUCCESS
44
NAN_VALUE_ENCOUNTERED_IN_MODEL ...
3
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
keras_reg_Dense
0.166667
0.000000
0.00
0.000000
0.000000
0.00
0.000000
0.000000
0.000000
0.000000
0.016667
keras_reg_Dense_pipe
0.333333
0.000000
0.00
0.000000
0.000000
0.00
0.000000
0.000000
0.000000
0.000000
0.033333
keras_reg_GRU
0.000000
0.000000
1.00
0.000000
0.000000
1.00
0.000000
0.000000
0.000000
0.000000
0.200000
keras_reg_GRU_pipe
0.000000
0.000000
1.00
0.000000
0.000000
1.00
0.000000
0.000000
0.000000
0.000000
0.200000
keras_reg_LSTM
0.166667
0.000000
1.00
0.000000
0.000000
1.00
0.000000
0.000000
0.000000
0.166667
0.233333
keras_reg_LSTM_pipe
0.000000
0.000000
1.00
0.000000
0.000000
1.00
0.000000
0.000000
0.000000
0.000000
0.200000
keras_reg_SimpleRNN
0.500000
0.333333
1.00
0.166667
0.000000
1.00
0.333333
0.333333
0.333333
0.333333
0.433333
keras_reg_SimpleRNN_pipe
0.166667
0.166667
1.00
0.333333
0.166667
1.00
0.166667
0.166667
0.166667
0.166667
0.350000
All
0.166667
0.062500
0.75
0.062500
0.020833
0.75
0.062500
0.062500
0.062500
0.083333
0.208333
ERROR_REPORT_FOR_CATEGORY sklearn.calibration
error_message
dialect
error_message
Firebird
SUCCESS
31
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
5
IBM DB2
SUCCESS
36
Impala
SUCCESS
14
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
11
TIMEOUT
11
MS SQL Server
SUCCESS
36
MariaDB
SUCCESS
36
MonetDB
SUCCESS
36
Oracle
SUCCESS
36
PostgreSQL
SUCCESS
36
SQLite
SUCCESS
36
Teradata
SUCCESS
33
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
CalibratedClassifierCV
0.111111
0.0
0.555556
0.0
0.0
0.0
0.0
0.0
0.0
0.055556
0.072222
CalibratedClassifierCV_pipe
0.166667
0.0
0.666667
0.0
0.0
0.0
0.0
0.0
0.0
0.111111
0.094444
All
0.138889
0.0
0.611111
0.0
0.0
0.0
0.0
0.0
0.0
0.083333
0.083333
ERROR_REPORT_FOR_CATEGORY sklearn.covariance
error_message
dialect
error_message
Firebird
SUCCESS
10
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
2
IBM DB2
SUCCESS
10
'InternalError:(ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0973N Not enough storage is available in the "AGENT_STACK_SZ" heap or stack to process the statement. SQLSTATE=57011 SQLCODE=-973 \[SQL ...
2
Impala
SUCCESS
10
"DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Exceeded the maximum depth of an expression tree (1000).\n \[SQL ...
2
MS SQL Server
SUCCESS
10
"OperationalError:(pymssql.OperationalError) (8631, b'Internal error: Server stack limit has been reached. Please look for potentially deep nesting in your query, and try to simplify it.DB-Lib error message 20018, severity 17:\\nGeneral SQL Server error: Check messages from the SQL Server\\n') \[SQL ...
2
MariaDB
SUCCESS
10
'OperationalError:(_mysql_exceptions.OperationalError) (1436, "Thread stack overrun: 1238176 bytes used of a 1269760 byte stack, and 32000 bytes needed. Use \'mysqld --thread_stack=#\' to specify a bigger stack") \[SQL ...
2
MonetDB
SUCCESS
10
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
2
Oracle
SUCCESS
10
TIMEOUT
2
PostgreSQL
SUCCESS
10
'OperationalError:(psycopg2.OperationalError) stack depth limit exceeded\nHINT: Increase the configuration parameter "max_stack_depth" (currently 2048kB), after ensuring the platform\'s stack depth limit is adequate.\n \[SQL ...
2
SQLite
SUCCESS
10
'OperationalError:(sqlite3.OperationalError) Expression tree is too large (maximum depth 1000) \[SQL ...
2
Teradata
SUCCESS
8
TIMEOUT
4
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
EllipticEnvelope
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
0.333333
EllipticEnvelope_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.333333
0.033333
All
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.333333
0.183333
ERROR_REPORT_FOR_CATEGORY sklearn.decomposition
error_message
dialect
error_message
Firebird
SUCCESS
56
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
11
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
2
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -901\\n- Integer overflow. The result of an integer operation caused the most significant bit of the result to carry.', -901, 335544779) ,
2
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Token unknown ...
1
IBM DB2
SUCCESS
69
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
'DBAPIError:(ibm_db_dbi.Error) ibm_db_dbi::Error: [IBM][CLI Driver][DB2/LINUXX8664] SQL0802N Arithmetic overflow or other arithmetic exception occurred. SQLSTATE=22003 SQLCODE=-802 ,
1
Impala
SUCCESS
58
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
14
MS SQL Server
SUCCESS
70
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
MariaDB
SUCCESS
70
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
MonetDB
SUCCESS
58
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
14
Oracle
SUCCESS
70
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
PostgreSQL
SUCCESS
70
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
SQLite
SUCCESS
70
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
Teradata
SUCCESS
59
Exception:CONNECTION_FAILED_WITH_ERROR ...
7
'DatabaseError:(teradata.api.DatabaseError) (2616, \'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\') \[SQL ...
4
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
TIMEOUT
2
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
FactorAnalysis
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.100000
FastICA
0.333333
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.183333
IncrementalPCA
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.416667
0.041667
KernelPCA
1.000000
0.083333
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.500000
0.258333
PCA
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
SparsePCA
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
1.000000
1.000000
TruncatedSVD
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
All
0.222222
0.041667
0.194444
0.027778
0.027778
0.194444
0.027778
0.027778
0.027778
0.202703
0.099723
ERROR_REPORT_FOR_CATEGORY sklearn.discriminant_analysis
error_message
dialect
error_message
Firebird
SUCCESS
32
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -901\\n- request size limit exceeded\', -901, 335544382) \[SQL ...
2
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
1
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
IBM DB2
SUCCESS
34
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
Impala
TIMEOUT
26
SUCCESS
8
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
MS SQL Server
SUCCESS
35
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
MariaDB
SUCCESS
34
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
MonetDB
SUCCESS
34
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
Oracle
SUCCESS
34
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
PostgreSQL
SUCCESS
35
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
SQLite
SUCCESS
35
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
Teradata
SUCCESS
31
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
TIMEOUT
2
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
LinearDiscriminantAnalysis
0.111111
0.000000
0.722222
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.111111
0.094444
LinearDiscriminantAnalysis_pipe
0.111111
0.111111
0.833333
0.055556
0.111111
0.111111
0.111111
0.055556
0.055556
0.166667
0.172222
All
0.111111
0.055556
0.777778
0.027778
0.055556
0.055556
0.055556
0.027778
0.027778
0.138889
0.133333
ERROR_REPORT_FOR_CATEGORY sklearn.ensemble
error_message
dialect
error_message
Firebird
SUCCESS
126
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
90
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while executing SQL statement:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range\', -802, 335544321) \[SQL ...
22
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
9
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Token unknown ...
3
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
1
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
IBM DB2
SUCCESS
249
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
3
Impala
SUCCESS
161
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
55
TIMEOUT
32
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
"OperationalError:(impala.error.OperationalError) TableNotFoundException: Table not found: impala_db.tmp_20181207193148_aj3ecws_ads_imp_1_out\n \[SQL ...
1
'DBAPIError:(impala.error.HiveServer2Error) ImpalaRuntimeException: Error making \'createTable\' RPC to Hive Metastore: \nCAUSED BY: AlreadyExistsException: Table tmp_20181207193148_aj3ecws_ads_imp_1_out already exists\n \[SQL ...
1
MS SQL Server
SUCCESS
250
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
MariaDB
SUCCESS
250
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
MonetDB
SUCCESS
229
TIMEOUT
16
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
4
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
3
Oracle
SUCCESS
250
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
PostgreSQL
SUCCESS
250
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
SQLite
SUCCESS
250
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
2
Teradata
SUCCESS
186
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
55
'DatabaseError:(teradata.api.DatabaseError) (2616, \'[22003] [Teradata][ODBC Teradata Driver][Teradata Database](-2616)Numeric overflow occurred during computation.\') \[SQL ...
6
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
5
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
AdaBoostClassifier
0.944444
0.000000
0.555556
0.000000
0.000000
0.111111
0.000000
0.000000
0.000000
0.333333
0.194444
AdaBoostClassifier_pipe
0.888889
0.055556
0.666667
0.000000
0.000000
0.166667
0.000000
0.000000
0.000000
0.444444
0.222222
AdaBoostRegressor
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
AdaBoostRegressor_pipe
0.666667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.066667
BaggingClassifier
0.444444
0.000000
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.277778
0.105556
BaggingClassifier_pipe
0.222222
0.000000
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.277778
0.083333
BaggingRegressor
0.666667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.066667
BaggingRegressor_pipe
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.033333
ExtraTreesClassifier
0.388889
0.000000
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.555556
0.127778
ExtraTreesClassifier_pipe
0.444444
0.111111
0.444444
0.111111
0.111111
0.111111
0.111111
0.111111
0.111111
0.777778
0.244444
ExtraTreesRegressor
0.666667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.066667
ExtraTreesRegressor_pipe
0.500000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.166667
0.066667
GradientBoostingClassifier
0.722222
0.000000
0.777778
0.000000
0.000000
0.444444
0.000000
0.000000
0.000000
0.000000
0.194444
GradientBoostingClassifier_pipe
0.722222
0.000000
0.833333
0.000000
0.000000
0.444444
0.000000
0.000000
0.000000
0.055556
0.205556
GradientBoostingRegressor
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.016667
GradientBoostingRegressor_pipe
0.500000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.050000
IsolationForest
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.666667
0.066667
IsolationForest_pipe
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.666667
0.100000
RandomForestClassifier
0.388889
0.000000
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.222222
0.094444
RandomForestClassifier_pipe
0.333333
0.000000
0.444444
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.222222
0.100000
RandomForestRegressor
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.033333
RandomForestRegressor_pipe
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.033333
All
0.500000
0.011905
0.361111
0.007937
0.007937
0.091270
0.007937
0.007937
0.007937
0.261905
0.126587
ERROR_REPORT_FOR_CATEGORY sklearn.feature_selection
error_message
dialect
error_message
Firebird
SUCCESS
108
IBM DB2
SUCCESS
108
Impala
SUCCESS
108
MS SQL Server
SUCCESS
108
MariaDB
SUCCESS
108
MonetDB
SUCCESS
108
Oracle
SUCCESS
108
PostgreSQL
SUCCESS
108
SQLite
SUCCESS
108
Teradata
SUCCESS
108
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
GenericUnivariateSelect
0
0
0
0
0
0
0
0
0
0
0
RFE
0
0
0
0
0
0
0
0
0
0
0
RFECV
0
0
0
0
0
0
0
0
0
0
0
SelectFdr
0
0
0
0
0
0
0
0
0
0
0
SelectFpr
0
0
0
0
0
0
0
0
0
0
0
SelectFromModel
0
0
0
0
0
0
0
0
0
0
0
SelectFwe
0
0
0
0
0
0
0
0
0
0
0
SelectKBest
0
0
0
0
0
0
0
0
0
0
0
SelectPercentile
0
0
0
0
0
0
0
0
0
0
0
All
0
0
0
0
0
0
0
0
0
0
0
ERROR_REPORT_FOR_CATEGORY sklearn.impute
error_message
dialect
error_message
Firebird
SUCCESS
24
IBM DB2
SUCCESS
24
Impala
SUCCESS
24
MS SQL Server
SUCCESS
24
MariaDB
SUCCESS
24
MonetDB
SUCCESS
24
Oracle
SUCCESS
24
PostgreSQL
SUCCESS
24
SQLite
SUCCESS
24
Teradata
SUCCESS
24
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
MissingIndicator
0
0
0
0
0
0
0
0
0
0
0
SimpleImputer
0
0
0
0
0
0
0
0
0
0
0
All
0
0
0
0
0
0
0
0
0
0
0
ERROR_REPORT_FOR_CATEGORY sklearn.kernel_ridge
error_message
dialect
error_message
Firebird
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
6
SUCCESS
3
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
2
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Token unknown ...
1
IBM DB2
SUCCESS
12
Impala
SUCCESS
12
MS SQL Server
SUCCESS
12
MariaDB
SUCCESS
12
MonetDB
SUCCESS
12
Oracle
SUCCESS
12
PostgreSQL
SUCCESS
12
SQLite
SUCCESS
12
Teradata
SUCCESS
11
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\') \[SQL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
KernelRidge
1.00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.166667
0.116667
KernelRidge_pipe
0.50
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.000000
0.050000
All
0.75
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.083333
0.083333
ERROR_REPORT_FOR_CATEGORY sklearn.linear_model
error_message
dialect
error_message
Firebird
SUCCESS
476
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
10
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -901\\n- request size limit exceeded\', -901, 335544382) \[SQL ...
4
TIMEOUT
1
cTrainingError ...
1
IBM DB2
SUCCESS
491
cTrainingError ...
1
Impala
SUCCESS
437
TIMEOUT
54
cTrainingError ...
1
MS SQL Server
SUCCESS
490
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206005156_H92RC4Z_ADS_imp_1_OUT\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
cTrainingError ...
1
MariaDB
SUCCESS
491
cTrainingError ...
1
MonetDB
SUCCESS
491
cTrainingError ...
1
Oracle
SUCCESS
491
cTrainingError ...
1
PostgreSQL
SUCCESS
491
cTrainingError ...
1
SQLite
SUCCESS
491
cTrainingError ...
1
Teradata
SUCCESS
478
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
7
TIMEOUT
6
cTrainingError ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
ARDRegression
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
ARDRegression_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
BayesianRidge
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
BayesianRidge_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
ElasticNet
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
ElasticNetCV
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
ElasticNetCV_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
ElasticNet_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
Lars
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LarsCV
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LarsCV_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
Lars_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
Lasso
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LassoCV
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LassoCV_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LassoLars
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LassoLarsCV
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LassoLarsCV_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LassoLarsIC
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LassoLarsIC_pipe
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.016667
LassoLars_pipe
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.016667
Lasso_pipe
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.016667
LinearRegression
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LinearRegression_pipe
0.166667
0.000000
0.000000
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.033333
LogisticRegression
0.111111
0.000000
0.722222
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.083333
LogisticRegressionCV
0.166667
0.000000
0.722222
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.088889
LogisticRegressionCV_pipe
0.000000
0.000000
0.722222
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.077778
LogisticRegression_pipe
0.055556
0.000000
0.722222
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.083333
OrthogonalMatchingPursuit
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
OrthogonalMatchingPursuitCV
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
OrthogonalMatchingPursuitCV_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
OrthogonalMatchingPursuit_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
PassiveAggressiveClassifier
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
PassiveAggressiveClassifier_pipe
0.055556
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.011111
PassiveAggressiveRegressor
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
PassiveAggressiveRegressor_pipe
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.016667
Perceptron
0.000000
0.000000
0.055556
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.111111
0.016667
Perceptron_pipe
0.000000
0.000000
0.055556
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.011111
RANSACRegressor
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
RANSACRegressor_pipe
0.166667
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.016667
Ridge
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
RidgeCV
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
RidgeCV_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
RidgeClassifier
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.111111
0.011111
RidgeClassifierCV
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.005556
RidgeClassifierCV_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.005556
RidgeClassifier_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.005556
Ridge_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
SGDClassifier
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.005556
SGDClassifier_pipe
0.111111
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.055556
0.016667
SGDRegressor
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
SGDRegressor_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
TheilSenRegressor
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
TheilSenRegressor_pipe
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
All
0.032520
0.002033
0.111789
0.004065
0.002033
0.002033
0.002033
0.002033
0.002033
0.028455
0.018902
ERROR_REPORT_FOR_CATEGORY sklearn.multiclass
error_message
dialect
error_message
Firebird
SUCCESS
39
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
19
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
14
IBM DB2
SUCCESS
72
Impala
SUCCESS
36
TIMEOUT
23
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
12
'DBAPIError:(impala.error.HiveServer2Error) ExecQueryFInstances rpc query_id=16432345ede1bdbc:cf2b999500000000 failed: Failed to get minimum memory reservation of 1.73 GB on daemon cloudera:22000 for query 16432345ede1bdbc:cf2b999500000000 because it would exceed an applicable memory limit. Memory is likely oversubscribed. Reducing query concurrency or configuring admission control may help avoid
1
MS SQL Server
SUCCESS
71
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206010532_OEPNFRI_OVR_Score_1\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
MariaDB
SUCCESS
72
MonetDB
SUCCESS
72
Oracle
SUCCESS
72
PostgreSQL
SUCCESS
72
SQLite
SUCCESS
72
Teradata
SUCCESS
54
Exception:CONNECTION_FAILED_WITH_ERROR ...
16
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
2
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
OneVsOneClassifier
0.666667
0.0
0.388889
0.000000
0.0
0.0
0.0
0.0
0.0
0.222222
0.127778
OneVsOneClassifier_pipe
0.444444
0.0
0.444444
0.000000
0.0
0.0
0.0
0.0
0.0
0.277778
0.116667
OneVsRestClassifier
0.333333
0.0
0.555556
0.000000
0.0
0.0
0.0
0.0
0.0
0.222222
0.111111
OneVsRestClassifier_pipe
0.388889
0.0
0.611111
0.055556
0.0
0.0
0.0
0.0
0.0
0.277778
0.133333
All
0.458333
0.0
0.500000
0.013889
0.0
0.0
0.0
0.0
0.0
0.250000
0.122222
ERROR_REPORT_FOR_CATEGORY sklearn.naive_bayes
error_message
dialect
error_message
Firebird
SUCCESS
87
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
40
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
7
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
5
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Token unknown ...
1
IBM DB2
SUCCESS
138
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL1585N A temporary table could not be created because there is no available system temporary table space that has a compatible page size. SQLSTATE=54048 SQLCODE=-1585 \[SQL ...
2
Impala
TIMEOUT
82
SUCCESS
50
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
8
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
MS SQL Server
SUCCESS
140
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
3
'OperationalError:(pymssql.OperationalError) (2714, b"There is already an object named \'##TMP_20181206010237_ZWPX1VH_ADS_imp_1_OUT\' in the database.DB-Lib error message 20018, severity 16:\\nGeneral SQL Server error: Check messages from the SQL Server\\n") \[SQL ...
1
MariaDB
SUCCESS
140
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
MonetDB
SUCCESS
76
TIMEOUT
52
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
12
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
Oracle
SUCCESS
140
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
PostgreSQL
SUCCESS
140
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
SQLite
SUCCESS
140
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
Teradata
SUCCESS
124
TIMEOUT
14
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
4
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during queryrewrite phase.\') \[SQL ...
1
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
BernoulliNB
0.333333
0.000000
0.555556
0.000000
0.000000
0.444444
0.000000
0.000000
0.000000
0.222222
0.155556
BernoulliNB_pipe
0.388889
0.000000
0.555556
0.000000
0.000000
0.444444
0.000000
0.000000
0.000000
0.222222
0.161111
ComplementNB
0.444444
0.111111
0.666667
0.111111
0.111111
0.666667
0.111111
0.111111
0.111111
0.111111
0.255556
ComplementNB_pipe
0.444444
0.111111
0.666667
0.111111
0.111111
0.666667
0.111111
0.111111
0.111111
0.166667
0.261111
GaussianNB
0.500000
0.111111
0.777778
0.000000
0.000000
0.555556
0.000000
0.000000
0.000000
0.333333
0.227778
GaussianNB_pipe
0.388889
0.000000
0.777778
0.000000
0.000000
0.555556
0.000000
0.000000
0.000000
0.055556
0.177778
MultinomialNB
0.333333
0.000000
0.611111
0.000000
0.000000
0.222222
0.000000
0.000000
0.000000
0.000000
0.116667
MultinomialNB_pipe
0.333333
0.000000
0.611111
0.000000
0.000000
0.222222
0.000000
0.000000
0.000000
0.000000
0.116667
All
0.395833
0.041667
0.652778
0.027778
0.027778
0.472222
0.027778
0.027778
0.027778
0.138889
0.184028
ERROR_REPORT_FOR_CATEGORY sklearn.neural_network
error_message
dialect
error_message
Firebird
SUCCESS
31
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
12
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
5
IBM DB2
SUCCESS
48
Impala
SUCCESS
25
TIMEOUT
23
MS SQL Server
SUCCESS
48
MariaDB
SUCCESS
48
MonetDB
SUCCESS
32
TIMEOUT
16
Oracle
SUCCESS
48
PostgreSQL
SUCCESS
48
SQLite
SUCCESS
48
Teradata
SUCCESS
44
TIMEOUT
3
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
MLPClassifier
0.388889
0.0
0.611111
0.0
0.0
0.444444
0.0
0.0
0.0
0.111111
0.155556
MLPClassifier_pipe
0.444444
0.0
0.666667
0.0
0.0
0.444444
0.0
0.0
0.0
0.111111
0.166667
MLPRegressor
0.166667
0.0
0.000000
0.0
0.0
0.000000
0.0
0.0
0.0
0.000000
0.016667
MLPRegressor_pipe
0.166667
0.0
0.000000
0.0
0.0
0.000000
0.0
0.0
0.0
0.000000
0.016667
All
0.354167
0.0
0.479167
0.0
0.0
0.333333
0.0
0.0
0.0
0.083333
0.125000
ERROR_REPORT_FOR_CATEGORY sklearn.pipeline
error_message
dialect
error_message
Firebird
SUCCESS
48
IBM DB2
SUCCESS
48
Impala
SUCCESS
48
MS SQL Server
SUCCESS
48
MariaDB
SUCCESS
48
MonetDB
SUCCESS
48
Oracle
SUCCESS
48
PostgreSQL
SUCCESS
48
SQLite
SUCCESS
48
Teradata
SUCCESS
42
TIMEOUT
5
Exception:CONNECTION_FAILED_WITH_ERROR ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
FeatureUnion
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.000000
0.000000
Pipeline
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.055556
0.005556
Pipeline_pipe
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.277778
0.027778
All
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.125000
0.012500
ERROR_REPORT_FOR_CATEGORY sklearn.preprocessing
error_message
dialect
error_message
Firebird
SUCCESS
160
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
15
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
3
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
2
IBM DB2
SUCCESS
164
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
7
'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0419N A decimal divide operation is not valid because the result would have a negative scale. SQLSTATE=42911 SQLCODE=-419 \[SQL ...
5
'DataError:(ibm_db_dbi.DataError) ibm_db_dbi::DataError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0840N Too many items were returned in a SELECT list. SQLSTATE=54004 SQLCODE=-840 \[SQL ...
4
Impala
SUCCESS
174
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
6
MS SQL Server
SUCCESS
174
"OperationalError:(pymssql.OperationalError) (125, b'Case expressions may only be nested to level 10.DB-Lib error message 20018, severity 15:\\nGeneral SQL Server error: Check messages from the SQL Server\\n') \[SQL ...
6
MariaDB
SUCCESS
174
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
6
MonetDB
SUCCESS
163
'OperationalError:(pymonetdb.exceptions.OperationalError) Math exception: Numerical argument out of domain\n \[SQL ...
12
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
4
TIMEOUT
1
Oracle
SUCCESS
174
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
6
PostgreSQL
SUCCESS
176
'OperationalError:(psycopg2.OperationalError) target lists can have at most 1664 entries\n \[SQL ...
4
SQLite
SUCCESS
166
'OperationalError:(sqlite3.OperationalError) parser stack overflow \[SQL ...
6
'OperationalError:(sqlite3.OperationalError) too many columns in result set \[SQL ...
4
'OperationalError:(sqlite3.OperationalError) too many terms in compound SELECT \[SQL ...
4
Teradata
SUCCESS
162
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
9
"DatabaseError:(teradata.api.DatabaseError) (10670, '[07009] [Teradata][ODBC] (10670) Invalid descriptor index, descriptor record does not exist, or descriptor record was not properly initialized.') ,
4
TIMEOUT
4
'DatabaseError:(teradata.api.DatabaseError) (5639, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-5639)Request size exceeds Parser limit of 1048500 bytes.\') \[SQL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
Binarizer
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
KBinsDiscretizer
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LabelBinarizer
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
LabelEncoder
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
MaxAbsScaler
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
MinMaxScaler
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
Normalizer
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
OneHotEncoder
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
OrdinalEncoder
0.500000
0.500000
0.500000
0.500000
0.500000
0.083333
0.500000
0.000000
0.500000
0.500000
0.408333
PolynomialFeatures
0.333333
0.333333
0.000000
0.000000
0.000000
0.000000
0.000000
0.333333
0.333333
0.333333
0.166667
PowerTransformer
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.100000
QuantileTransformer
0.833333
0.500000
0.000000
0.000000
0.000000
0.333333
0.000000
0.000000
0.333333
0.666667
0.266667
RobustScaler
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
StandardScaler
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
All
0.111111
0.088889
0.033333
0.033333
0.033333
0.094444
0.033333
0.022222
0.077778
0.100000
0.062778
ERROR_REPORT_FOR_CATEGORY sklearn.random_projection
error_message
dialect
error_message
Firebird
SUCCESS
20
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
3
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -901\\n- Integer overflow. The result of an integer operation caused the most significant bit of the result to carry.', -901, 335544779) ,
1
IBM DB2
SUCCESS
24
Impala
SUCCESS
24
MS SQL Server
SUCCESS
24
MariaDB
SUCCESS
24
MonetDB
SUCCESS
24
Oracle
SUCCESS
24
PostgreSQL
SUCCESS
24
SQLite
SUCCESS
24
Teradata
SUCCESS
24
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
GaussianRandomProjection
0.333333
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.033333
SparseRandomProjection
0.000000
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.000000
All
0.166667
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.016667
ERROR_REPORT_FOR_CATEGORY sklearn.svm
error_message
dialect
error_message
Firebird
SUCCESS
72
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while executing SQL statement:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range\', -802, 335544321) \[SQL ...
29
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -104\\n- Dynamic SQL Error\\n- SQL error code = -104\\n- Unexpected end of command ...
22
"DatabaseError:(fdb.fbcore.DatabaseError) ('Cursor.fetchone:\\n- SQLCODE: -802\\n- arithmetic exception, numeric overflow, or string truncation\\n- numeric value is out of range', -802, 335544321) ,
21
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
12
IBM DB2
SUCCESS
153
'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0204N "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" is an undefined name. SQLSTATE=42704 SQLCODE=-204 \[SQL ...
1
'ProgrammingError:(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: Statement Execute Failed: [IBM][CLI Driver][DB2/LINUXX8664] SQL0601N The name of the object to be created is identical to the existing name "DB.TMP_20181205235842_F5UC68X_KERNAGG_B9" of type "CREATE TEMPORARY TABLE". SQLSTATE=42710 SQLCODE=-601 \[SQL ...
1
TIMEOUT
1
Impala
SUCCESS
112
TIMEOUT
44
MS SQL Server
SUCCESS
155
TIMEOUT
1
MariaDB
SUCCESS
156
MonetDB
SUCCESS
144
TIMEOUT
8
'DBAPIError:(builtins.BrokenPipeError) [Errno 32] Broken pipe ,
3
Exception:CONNECTION_FAILED_WITH_ERROR ...
1
Oracle
SUCCESS
122
TIMEOUT
34
PostgreSQL
SUCCESS
156
SQLite
SUCCESS
156
Teradata
SUCCESS
141
TIMEOUT
12
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
2
'DatabaseError:(teradata.api.DatabaseError) (3710, \'[HY001] [Teradata][ODBC Teradata Driver][Teradata Database](-3710)Insufficient memory to parse this request, during optimizer phase.\') \[SQL ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
LinearSVC
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.0
0.0
0.000000
0.000000
LinearSVC_pipe
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.0
0.0
0.055556
0.005556
LinearSVR
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.0
0.0
0.000000
0.000000
LinearSVR_pipe
0.000000
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.0
0.0
0.000000
0.000000
NuSVC
0.777778
0.055556
0.611111
0.000000
0.0
0.166667
0.555556
0.0
0.0
0.277778
0.244444
NuSVC_pipe
0.833333
0.055556
0.611111
0.000000
0.0
0.055556
0.388889
0.0
0.0
0.055556
0.200000
NuSVR
1.000000
0.000000
0.000000
0.000000
0.0
0.166667
0.000000
0.0
0.0
0.000000
0.116667
NuSVR_pipe
0.333333
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.0
0.0
0.000000
0.033333
OneClassSVM
0.833333
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.0
0.0
0.000000
0.083333
OneClassSVM_pipe
0.166667
0.000000
0.000000
0.000000
0.0
0.000000
0.000000
0.0
0.0
0.000000
0.016667
SVC
0.888889
0.055556
0.611111
0.055556
0.0
0.166667
0.555556
0.0
0.0
0.277778
0.261111
SVC_pipe
0.833333
0.000000
0.611111
0.000000
0.0
0.055556
0.388889
0.0
0.0
0.111111
0.200000
SVR
1.000000
0.000000
0.000000
0.000000
0.0
0.166667
0.000000
0.0
0.0
0.166667
0.133333
SVR_pipe
0.666667
0.000000
0.000000
0.000000
0.0
0.333333
0.000000
0.0
0.0
0.000000
0.100000
All
0.538462
0.019231
0.282051
0.006410
0.0
0.076923
0.217949
0.0
0.0
0.096154
0.123718
ERROR_REPORT_FOR_CATEGORY sklearn.tree
error_message
dialect
error_message
Firebird
SUCCESS
94
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
2
IBM DB2
SUCCESS
96
Impala
SUCCESS
96
MS SQL Server
SUCCESS
96
MariaDB
SUCCESS
96
MonetDB
SUCCESS
96
Oracle
SUCCESS
83
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
13
PostgreSQL
SUCCESS
96
SQLite
SUCCESS
96
Teradata
SUCCESS
94
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
2
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
DecisionTreeClassifier
0.000000
0.0
0.0
0.0
0.0
0.0
0.000000
0.0
0.0
0.000000
0.000000
DecisionTreeClassifier_pipe
0.055556
0.0
0.0
0.0
0.0
0.0
0.000000
0.0
0.0
0.055556
0.011111
DecisionTreeRegressor
0.000000
0.0
0.0
0.0
0.0
0.0
0.000000
0.0
0.0
0.000000
0.000000
DecisionTreeRegressor_pipe
0.000000
0.0
0.0
0.0
0.0
0.0
0.000000
0.0
0.0
0.000000
0.000000
ExtraTreeClassifier
0.000000
0.0
0.0
0.0
0.0
0.0
0.222222
0.0
0.0
0.000000
0.022222
ExtraTreeClassifier_pipe
0.000000
0.0
0.0
0.0
0.0
0.0
0.500000
0.0
0.0
0.055556
0.055556
ExtraTreeRegressor
0.000000
0.0
0.0
0.0
0.0
0.0
0.000000
0.0
0.0
0.000000
0.000000
ExtraTreeRegressor_pipe
0.166667
0.0
0.0
0.0
0.0
0.0
0.000000
0.0
0.0
0.000000
0.016667
All
0.020833
0.0
0.0
0.0
0.0
0.0
0.135417
0.0
0.0
0.020833
0.017708
ERROR_REPORT_FOR_CATEGORY xgboost.sklearn
error_message
dialect
error_message
Firebird
"DatabaseError:('Error while commiting transaction:\\n- SQLCODE: -913\\n- deadlock\\n- update conflicts with concurrent update\\n- concurrent transaction number is ...
29
SUCCESS
17
'DatabaseError:(fdb.fbcore.DatabaseError) (\'Error while preparing SQL statement:\\n- SQLCODE: -902\\n- Dynamic SQL Error\\n- Too many Contexts of Relation/Procedure/Views. Maximum allowed is 256\', -902, 335544569) \[SQL ...
1
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
IBM DB2
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
Impala
TIMEOUT
23
SUCCESS
17
DBAPIError:(impala.error.HiveServer2Error) AnalysisException: Invalid column/field name: ...
8
MS SQL Server
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
MariaDB
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
MonetDB
SUCCESS
31
TIMEOUT
16
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
Oracle
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
PostgreSQL
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
SQLite
SUCCESS
47
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
Teradata
SUCCESS
46
'DatabaseError:(teradata.api.DatabaseError) (3754, \'[HY000] [Teradata][ODBC Teradata Driver][Teradata Database](-3754)Precision error in FLOAT type constant or during implicit conversions.\') \[SQL ...
1
Exception:COMPARISON_FAILURE_TOO_MANY_DIFFS ...
1
mean
dialect
Firebird
IBM DB2
Impala
MS SQL Server
MariaDB
MonetDB
Oracle
PostgreSQL
SQLite
Teradata
All
Model
XGBClassifier
0.777778
0.000000
0.833333
0.000000
0.000000
0.444444
0.000000
0.000000
0.000000
0.000000
0.205556
XGBClassifier_pipe
0.611111
0.000000
0.888889
0.000000
0.000000
0.444444
0.000000
0.000000
0.000000
0.055556
0.200000
XGBRegressor
0.500000
0.166667
0.000000
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.166667
0.183333
XGBRegressor_pipe
0.500000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.050000
All
0.645833
0.020833
0.645833
0.020833
0.020833
0.354167
0.020833
0.020833
0.020833
0.041667
0.181250
In [44]:
df[df.model_category == "bad_category"]
Out[44]:
Model
dataset
dialect
DSN
status
error_message
elapsed_time
full_error_message
model_category
status_2
In [ ]:
In [45]:
def to_float(x):
try:
return float(x)
except:
# print("PROBLEM_CONVERTING" , x)
return None
import matplotlib.pyplot as plt
for dialect in dialects:
print(dialect)
df1 = df[df.dialect == dialect] # .sample(4000)
times = df1['elapsed_time'].apply(to_float)
# times= times[times > 100]
times.plot(kind='hist' , bins=20)
plt.show()
IBM DB2
Firebird
Impala
MonetDB
MS SQL Server
MariaDB
Oracle
PostgreSQL
SQLite
Teradata
In [ ]:
In [ ]:
Content source: antoinecarme/sklearn2sql_heroku
Similar notebooks: