"max": "113.0",
"missing": "0"
}
}, {
"feature": "All_weekly_transform",
"transformation": "",
"transformationsData": {},
"type": "real",
"selected": "True",
"stats": {
"count": "333",
"mean": "46.25",
"stddev": "56.53",
"min": "0.0",
"max": "174.0",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionRegression(IncomePredictionApp_AutoFE, [
"Occupation", "M_workers", "M_weekly", "F_workers", "F_weekly",
"All_workers"
], "All_weekly")
except Exception as ex:
print(ex)
import json
import Connectors
import Transformations
import AutoML
try:
Adnan_DBFS = Connectors.DBFSConnector.fetch([], {}, "5e96c95f672fce2edcf86fa2", spark,
"{'url': '/Demo/137_instance_demo/Bike_Rental_UCI_dataset.csv', 'file_type': 'Delimeted', 'dbfs_token': 'dapi9900607f95a71a87c0660d2860ef59ea', 'dbfs_domain': 'eastus.azuredatabricks.net', 'delimiter': ',', 'is_header': 'Use Header Line'}")
except Exception as ex:
print(ex)
try:
Adnan_AutoFE = Transformations.TransformationMain.run(["5e96c95f672fce2edcf86fa2"], {"5e96c95f672fce2edcf86fa2": Adnan_DBFS}, "5e96c95f672fce2edcf86fa3", spark, json.dumps({"FE": [{"transformationsData": {}, "feature": "instant", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "8718.71", "stddev": "5056.48", "min": "7", "max": "17366", "missing": "0"}}, {"transformationsData": {"feature_label": "dteday"}, "feature": "dteday", "type": "date", "selected": "True", "replaceby": "random", "stats": {"count": "", "mean": "", "stddev": "", "min": "", "max": "", "missing": "0"}, "transformation": "Extract Date"}, {"transformationsData": {}, "feature": "season", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "2.51", "stddev": "1.11", "min": "1", "max": "4", "missing": "0"}}, {"transformationsData": {}, "feature": "yr", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "0.5", "stddev": "0.5", "min": "0", "max": "1", "missing": "0"}}, {"transformationsData": {}, "feature": "mnth", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "6.59", "stddev": "3.47", "min": "1", "max": "12", "missing": "0"}}, {"transformationsData": {}, "feature": "hr", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "11.62", "stddev": "6.91", "min": "0", "max": "23", "missing": "0"}}, {"transformationsData": {}, "feature": "holiday", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "0.03", "stddev": "0.17", "min": "0", "max": "1", "missing": "0"}}, {"transformationsData": {}, "feature": "weekday", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "2.96", "stddev": "1.99", "min": "0", "max": "6", "missing": "0"}}, {"transformationsData": {}, "feature": "workingday", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "0.69", "stddev": "0.46", "min": "0", "max": "1", "missing": "0"}}, {"transformationsData": {}, "feature": "weathersit", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {
"count": "1816", "mean": "1.41", "stddev": "0.63", "min": "1", "max": "4", "missing": "0"}}, {"transformationsData": {}, "feature": "temp", "type": "real", "selected": "True", "replaceby": "mean", "stats": {"count": "1816", "mean": "0.49", "stddev": "0.19", "min": "0.02", "max": "0.94", "missing": "0"}, "transformation": ""}, {"transformationsData": {}, "feature": "atemp", "type": "real", "selected": "True", "replaceby": "mean", "stats": {"count": "1816", "mean": "0.47", "stddev": "0.17", "min": "0.0455", "max": "0.8939", "missing": "0"}, "transformation": ""}, {"transformationsData": {}, "feature": "hum", "type": "real", "selected": "True", "replaceby": "mean", "stats": {"count": "1816", "mean": "0.62", "stddev": "0.19", "min": "0.0", "max": "1.0", "missing": "0"}, "transformation": ""}, {"transformationsData": {}, "feature": "windspeed", "type": "real", "selected": "True", "replaceby": "mean", "stats": {"count": "1816", "mean": "0.19", "stddev": "0.12", "min": "0.0", "max": "0.7463", "missing": "0"}, "transformation": ""}, {"transformationsData": {}, "feature": "casual", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "34.63", "stddev": "49.19", "min": "0", "max": "352", "missing": "0"}}, {"transformationsData": {}, "feature": "registered", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "151.09", "stddev": "153.46", "min": "0", "max": "876", "missing": "0"}}, {"transformationsData": {}, "feature": "cnt", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "1816", "mean": "185.72", "stddev": "183.35", "min": "1", "max": "953", "missing": "0"}}, {"feature": "dteday_dayofmonth", "transformation": "", "transformationsData": {}, "type": "numeric", "generated": "True", "selected": "True", "stats": {"count": "1816", "mean": "15.62", "stddev": "8.86", "min": "1", "max": "31", "missing": "0"}}, {"feature": "dteday_month", "transformation": "", "transformationsData": {}, "type": "numeric", "generated": "True", "selected": "True", "stats": {"count": "1816", "mean": "6.59", "stddev": "3.47", "min": "1", "max": "12", "missing": "0"}}, {"feature": "dteday_year", "transformation": "", "transformationsData": {}, "type": "numeric", "generated": "True", "selected": "True", "stats": {"count": "1816", "mean": "2011.5", "stddev": "0.5", "min": "2011", "max": "2012", "missing": "0"}}]}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(Adnan_AutoFE, ["instant", "dteday", "season", "yr", "mnth", "hr", "holiday",
"weekday", "workingday", "weathersit", "temp", "atemp", "hum", "windspeed", "casual", "registered"], "cnt")
except Exception as ex:
print(ex)
},
"transformation": ""
}, {
"transformationsData": {},
"feature": "MEDV",
"type": "real",
"selected": "True",
"replaceby": "mean",
"stats": {
"count": "420",
"mean": "22.65",
"stddev": "9.31",
"min": "5.0",
"max": "50.0",
"missing": "0"
},
"transformation": ""
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionRegression(BostonHousingPricesRegression_AutoFE, [
"CRIM", "ZN", "INDUS", "CHAS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX",
"PTRATIO", "B", "LSTAT"
], "MEDV")
except Exception as ex:
print(ex)
"max": "158.0",
"missing": "0"
}
}, {
"feature": "All_weekly_transform",
"transformation": "",
"transformationsData": {},
"type": "real",
"selected": "True",
"stats": {
"count": "470",
"mean": "63.19",
"stddev": "77.68",
"min": "0.0",
"max": "241.0",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionRegression(PredictHighestIncome_AutoFE, [
"Occupation", "M_workers", "M_weekly", "F_workers", "F_weekly",
"All_workers"
], "All_weekly")
except Exception as ex:
print(ex)
"feature": "Channel_transform",
"transformation": "",
"transformationsData": {},
"type": "numeric",
"selected": "True",
"stats": {
"count": "987",
"mean": "0.5",
"stddev": "0.68",
"min": "0",
"max": "2",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(CustomerAcquisitionsClassifier_AutoFE, [
"City", "Product_Category", "Product_Sub-Category", "Count",
"Customer ID", "Store Number", "Customer Segment", "First Name",
"Last Name", "Address", "State", "Zip", "DriveTime",
"Length of Residense", "MOR BANK: UPSCALE MERCHANDISE BUYER",
"MOSAIC HOUSEHOLD", "MOSAIC DESCRIPTION", "Customer_Lon",
"Customer_Lat", "Store ID", "Store_Lon", "Store_Lat"
], "Channel")
except Exception as ex:
print(ex)
"missing": "0"
},
"transformation": ""
}, {
"feature": "\ufffdState_transform",
"transformation": "",
"transformationsData": {},
"type": "real",
"selected": "True",
"stats": {
"count": "33",
"mean": "16.0",
"stddev": "9.67",
"min": "0.0",
"max": "32.0",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionRegression(
ProductionExperiment_AutoFE,
["\ufffdState", "Area", "Fertilizer", "Price", "Employment", "Cost"],
"production")
except Exception as ex:
print(ex)
import json
import Connectors
import Transformations
import AutoML
try:
Adnan_DBFS = Connectors.DBFSConnector.fetch(
[], {}, "5e96ed40b9444966c25fe7be", spark,
"{'url': '', 'file_type': 'Delimeted', 'dbfs_token': '', 'dbfs_domain': '', 'delimiter': ',', 'is_header': 'Use Header Line'}"
)
except Exception as ex:
print(ex)
try:
Adnan_AutoFE = Transformations.TransformationMain.run(
["5e96ed40b9444966c25fe7be"], {"5e96ed40b9444966c25fe7be": Adnan_DBFS},
"5e96ed40b9444966c25fe7bf", spark, json.dumps({"FE": []}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(Adnan_AutoFE, [], "")
except Exception as ex:
print(ex)
import json
import Connectors
import Transformations
import AutoML
try:
DepositAnalysis_DBFS = Connectors.DBFSConnector.fetch(
[], {}, "5e96ceecd8c39c31239e3416", spark, "{'url': '/Demo/BankDepositTrain.csv', 'file_type': 'Delimeted', 'dbfs_token': 'dapi0ef076722999cf4cd8859e9aafdb7b76', 'dbfs_domain': 'westus.azuredatabricks.net', 'delimiter': ',', 'is_header': 'Use Header Line'}")
except Exception as ex:
print(ex)
try:
DepositAnalysis_AutoFE = Transformations.TransformationMain.run(["5e96ceecd8c39c31239e3416"], {"5e96ceecd8c39c31239e3416": DepositAnalysis_DBFS}, "5e96ceecd8c39c31239e3417", spark, json.dumps({"FE": [{"transformationsData": {}, "feature": "age", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "5647", "mean": "40.94", "stddev": "11.79", "min": "18", "max": "95", "missing": "0"}}, {"transformationsData": {"feature_label": "job"}, "feature": "job", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "admin.", "max": "unknown", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {"feature_label": "marital"}, "feature": "marital", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "divorced", "max": "single", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {"feature_label": "education"}, "feature": "education", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "primary", "max": "unknown", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {"feature_label": "default"}, "feature": "default", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "no", "max": "yes", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {}, "feature": "balance", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "5647", "mean": "1537.64", "stddev": "3157.01", "min": "-2712", "max": "66653", "missing": "0"}}, {"transformationsData": {"feature_label": "housing"}, "feature": "housing", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "no", "max": "yes", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {"feature_label": "loan"}, "feature": "loan", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "no", "max": "yes", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {"feature_label": "contact"}, "feature": "contact", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "cellular", "max": "unknown", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {}, "feature": "day", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "5647", "mean": "15.48", "stddev": "8.43", "min": "1", "max": "31", "missing": "0"}}, {"transformationsData": {"feature_label": "month"}, "feature": "month", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "apr", "max": "sep", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {}, "feature": "duration", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "5647", "mean": "372.74", "stddev": "342.15", "min": "3", "max": "3253", "missing": "0"}}, {"transformationsData": {}, "feature": "campaign", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {
"count": "5647", "mean": "2.51", "stddev": "2.6", "min": "1", "max": "43", "missing": "0"}}, {"transformationsData": {}, "feature": "pdays", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "5647", "mean": "51.92", "stddev": "110.33", "min": "-1", "max": "854", "missing": "0"}}, {"transformationsData": {}, "feature": "previous", "transformation": "", "type": "numeric", "replaceby": "mean", "selected": "True", "stats": {"count": "5647", "mean": "0.81", "stddev": "2.17", "min": "0", "max": "41", "missing": "0"}}, {"transformationsData": {"feature_label": "poutcome"}, "feature": "poutcome", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "failure", "max": "unknown", "missing": "0"}, "transformation": "String Indexer"}, {"transformationsData": {"feature_label": "deposit"}, "feature": "deposit", "type": "string", "selected": "True", "replaceby": "max", "stats": {"count": "5647", "mean": "", "stddev": "", "min": "no", "max": "yes", "missing": "0"}, "transformation": "String Indexer"}, {"feature": "job_transform", "transformation": "", "transformationsData": {}, "type": "real", "selected": "True", "stats": {"count": "5647", "mean": "2.81", "stddev": "2.72", "min": "0.0", "max": "11.0", "missing": "0"}}, {"feature": "marital_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.54", "stddev": "0.69", "min": "0", "max": "2", "missing": "0"}}, {"feature": "education_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.72", "stddev": "0.85", "min": "0", "max": "3", "missing": "0"}}, {"feature": "default_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.02", "stddev": "0.13", "min": "0", "max": "1", "missing": "0"}}, {"feature": "housing_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.48", "stddev": "0.5", "min": "0", "max": "1", "missing": "0"}}, {"feature": "loan_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.13", "stddev": "0.33", "min": "0", "max": "1", "missing": "0"}}, {"feature": "contact_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.35", "stddev": "0.61", "min": "0", "max": "2", "missing": "0"}}, {"feature": "month_transform", "transformation": "", "transformationsData": {}, "type": "real", "selected": "True", "stats": {"count": "5647", "mean": "3.0", "stddev": "2.85", "min": "0.0", "max": "11.0", "missing": "0"}}, {"feature": "poutcome_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.45", "stddev": "0.86", "min": "0", "max": "3", "missing": "0"}}, {"feature": "deposit_transform", "transformation": "", "transformationsData": {}, "type": "numeric", "selected": "True", "stats": {"count": "5647", "mean": "0.47", "stddev": "0.5", "min": "0", "max": "1", "missing": "0"}}]}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(DepositAnalysis_AutoFE, ["age", "job", "marital", "education", "default", "balance",
"housing", "loan", "contact", "day", "month", "duration", "campaign", "pdays", "previous", "poutcome"], "deposit")
except Exception as ex:
print(ex)
"max": "18.0",
"missing": "0"
}
}, {
"feature": "Mosaic_likelihood_transform",
"transformation": "",
"transformationsData": {},
"type": "numeric",
"selected": "True",
"stats": {
"count": "4078",
"mean": "0.82",
"stddev": "0.76",
"min": "0",
"max": "2",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(CustomerResponse_AutoFE, [
"Distance_from_nearest_store", "Mosaic_group", "Amount_purchased_6m",
"Mosaic_likelihood"
], "Purchased_sale_soda")
except Exception as ex:
print(ex)
"transformationsData": {},
"type": "numeric",
"selected": "True",
"stats": {
"count": "653",
"mean": "0.04",
"stddev": "0.2",
"min": "0",
"max": "1",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(CreditRisk_AutoFE, [
"Status_of_checking_account", "Duration_in_months", "Credit_history",
"Purpose", "Credit_amount", "Savings_account_bond",
"Present_employment_since",
"Installment_rate_in_percentage_of_disposable_income",
"Personal_status_and_sex", "Other_debtors", "Present_residence_since",
"Property", "Age_in_years", "Other_installment_plans", "Housing",
"Number_of_existing_credits", "Job", "Number_of_people", "Telephone",
"Foreign_worker"
], "Credit_Risk")
except Exception as ex:
print(ex)
import json
import Connectors
import Transformations
import AutoML
try:
testprotestapp_DBFS = Connectors.DBFSConnector.fetch(
[], {}, "5e96c514672fce2edcf86f9e", spark, "{'url': '', 'file_type': 'Delimeted', 'dbfs_token': '', 'dbfs_domain': '', 'delimiter': ',', 'is_header': 'Use Header Line'}")
except Exception as ex:
print(ex)
try:
testprotestapp_AutoFE = Transformations.TransformationMain.run(["5e96c514672fce2edcf86f9e"], {
"5e96c514672fce2edcf86f9e": testprotestapp_DBFS}, "5e96c514672fce2edcf86f9f", spark, json.dumps({"FE": []}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(testprotestapp_AutoFE, [], "")
except Exception as ex:
print(ex)
"max": "9.0",
"missing": "0"
}
}, {
"feature": "W_L_transform",
"transformation": "",
"transformationsData": {},
"type": "numeric",
"selected": "True",
"stats": {
"count": "10",
"mean": "0.4",
"stddev": "0.52",
"min": "0",
"max": "1",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(Winnerprediction_AutoFE, [
"Team", "Goals_Scored", "Goals_Conceded", "Wins", "Attempts",
"Attemps_On_Target", "Pass_Accuracy", "Distance_Covered"
], "W_L")
except Exception as ex:
print(ex)
"missing": "0"
}
}, {
"feature": "y_transform",
"transformation": "",
"transformationsData": {},
"type": "numeric",
"selected": "True",
"stats": {
"count": "1785",
"mean": "0.04",
"stddev": "0.19",
"min": "0",
"max": "1",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(Effectivenewproducts_AutoFE, [
"age", "job", "marital", "education", "default", "balance", "housing",
"loan", "contact", "day", "month", "duration", "campaign", "pdays",
"previous", "poutcome"
], "y")
except Exception as ex:
print(ex)
import json
import Connectors
import Transformations
import AutoML
try:
Namechanged_DBFS = Connectors.DBFSConnector.fetch(
[], {}, "5e96e981cc56f84185d0503c", spark,
"{'url': '', 'file_type': 'Delimeted', 'dbfs_token': '', 'dbfs_domain': '', 'delimiter': ',', 'is_header': 'Use Header Line'}"
)
except Exception as ex:
print(ex)
try:
Namechanged_AutoFE = Transformations.TransformationMain.run(
["5e96e981cc56f84185d0503c"],
{"5e96e981cc56f84185d0503c": Namechanged_DBFS},
"5e96e981cc56f84185d0503d", spark, json.dumps({"FE": []}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(Namechanged_AutoFE, [], "")
except Exception as ex:
print(ex)
}, {
"feature": "cluster_labels_transform",
"transformation": "",
"transformationsData": {},
"type": "real",
"selected": "True",
"stats": {
"count": "1876",
"mean": "2.35",
"stddev": "1.7",
"min": "0.0",
"max": "5.0",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(PredictivechurnApp_AutoFE, [
"State", "Account_Length", "Area_Code", "Phone", "Intl_Plan",
"VMail_Plan", "VMail_Message", "Day_Mins", "Day_Calls", "Day_Charge",
"Eve_Mins", "Eve_Calls", "Eve_Charge", "Night_Mins", "Night_Calls",
"Night_Charge", "Intl_Mins", "total_Mins", "Intl_Calls", "Intl_Charge",
"Total_Charge", "CustServ_Calls", "cluster_labels"
], "Churn")
except Exception as ex:
print(ex)
"missing": "0"
},
"transformation": ""
}, {
"feature": "Date_transform",
"transformation": "",
"transformationsData": {},
"type": "real",
"selected": "True",
"stats": {
"count": "1512",
"mean": "107.98",
"stddev": "69.88",
"min": "0.0",
"max": "249.0",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionRegression(Stockprices_AutoFE, [
"Date", "Month", "Company", "Open", "High", "Low", "Prev Close",
"Adj Close", "Volume"
], "Close")
except Exception as ex:
print(ex)
import json
import Connectors
import Transformations
import AutoML
try:
Adnan_DBFS = Connectors.DBFSConnector.fetch(
[], {}, "5e9703ae2f478682d5f01d90", spark,
"{'url': '', 'file_type': 'Delimeted', 'dbfs_token': '', 'dbfs_domain': '', 'delimiter': ',', 'is_header': 'Use Header Line'}"
)
except Exception as ex:
print(ex)
try:
Adnan_AutoFE = Transformations.TransformationMain.run(
["5e9703ae2f478682d5f01d90"], {"5e9703ae2f478682d5f01d90": Adnan_DBFS},
"5e9703ae2f478682d5f01d91", spark, json.dumps({"FE": []}))
except Exception as ex:
print(ex)
try:
AutoML.functionRegression(Adnan_AutoFE, [], "")
except Exception as ex:
print(ex)
"missing": "0"
}
}, {
"feature": "SP_TYPE_CD_transform",
"transformation": "",
"transformationsData": {},
"type": "numeric",
"selected": "True",
"stats": {
"count": "88656",
"mean": "0.01",
"stddev": "0.09",
"min": "0",
"max": "3",
"missing": "0"
}
}]
}))
except Exception as ex:
print(ex)
try:
AutoML.functionClassification(MeterAnomaly_AutoFE, [
"BADGE_NBR", "INT_SUM_DAY_USG", "MONTH_AVG_USG", "HIGHEST_INT_DAY_USG",
"METER_TYPE", "WEEK_DAY_IND", "MONTH_NM", "DAILY_AVG_TEMP", "CITY",
"MFG_CD", "SP_TYPE_CD", "USAGE_VAR_PER"
], "ISSUE_IND")
except Exception as ex:
print(ex)