def gpu_load_names(col_names_path, **kwargs):
""" Loads names used for renaming the banks
Returns
-------
GPU DataFrame
"""
chronometer = Chronometer.makeStarted()
cols = ["seller_name", "new_seller_name"]
dtypes = OrderedDict([
("seller_name", "category"),
("new_seller_name", "category"),
])
new = col_names_path + "names.load"
print(new)
names_table = pyblazing.create_table(
table_name="names",
type=get_type_schema(new),
path=new,
delimiter="|",
names=cols,
dtypes=dtypes, # TODO: dtypes=get_dtype_values(dtypes)
skip_rows=1,
)
Chronometer.show(chronometer, "Read Names CSV")
return names_table
def gpu_load_names(**kwargs):
""" Loads names used for renaming the banks
Returns
-------
GPU DataFrame
"""
chronometer = Chronometer.makeStarted()
cols = ['seller_name', 'new_seller_name']
dtypes = OrderedDict([
("seller_name", "category"),
("new_seller_name", "category"),
])
names_table = pyblazing.create_table(table_name='names',
type=get_type_schema(col_names_path),
path=col_names_path,
delimiter='|',
names=cols,
dtypes=get_dtype_values(dtypes),
skip_rows=1)
Chronometer.show(chronometer, 'Read Names CSV')
return names_table
def gpu_load_acquisition_csv(acquisition_path, **kwargs):
""" Loads acquisition data
Returns
-------
GPU DataFrame
"""
chronometer = Chronometer.makeStarted()
cols = [
'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate',
'orig_upb', 'orig_loan_term', 'orig_date', 'first_pay_date',
'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti',
'borrower_credit_score', 'first_home_buyer', 'loan_purpose',
'property_type', 'num_units', 'occupancy_status', 'property_state',
'zip', 'mortgage_insurance_percent', 'product_type',
'coborrow_credit_score', 'mortgage_insurance_type',
'relocation_mortgage_indicator'
]
dtypes = OrderedDict([("loan_id", "int64"), ("orig_channel", "category"),
("seller_name", "category"),
("orig_interest_rate", "float64"),
("orig_upb", "int64"), ("orig_loan_term", "int64"),
("orig_date", "date"), ("first_pay_date", "date"),
("orig_ltv", "float64"), ("orig_cltv", "float64"),
("num_borrowers", "float64"), ("dti", "float64"),
("borrower_credit_score", "float64"),
("first_home_buyer", "category"),
("loan_purpose", "category"),
("property_type", "category"),
("num_units", "int64"),
("occupancy_status", "category"),
("property_state", "category"), ("zip", "int64"),
("mortgage_insurance_percent", "float64"),
("product_type", "category"),
("coborrow_credit_score", "float64"),
("mortgage_insurance_type", "float64"),
("relocation_mortgage_indicator", "category")])
print(acquisition_path)
acquisition_table = pyblazing.create_table(
table_name='acq',
type=get_type_schema(acquisition_path),
path=acquisition_path,
delimiter='|',
names=cols,
dtypes=get_dtype_values(dtypes),
skip_rows=1)
Chronometer.show(chronometer, 'Read Acquisition CSV')
return acquisition_table
def gpu_load_acquisition_csv(acquisition_path, **kwargs):
""" Loads acquisition data
Returns
-------
GPU DataFrame
"""
chronometer = Chronometer.makeStarted()
cols = [
"loan_id",
"orig_channel",
"seller_name",
"orig_interest_rate",
"orig_upb",
"orig_loan_term",
"orig_date",
"first_pay_date",
"orig_ltv",
"orig_cltv",
"num_borrowers",
"dti",
"borrower_credit_score",
"first_home_buyer",
"loan_purpose",
"property_type",
"num_units",
"occupancy_status",
"property_state",
"zip",
"mortgage_insurance_percent",
"product_type",
"coborrow_credit_score",
"mortgage_insurance_type",
"relocation_mortgage_indicator",
]
dtypes = OrderedDict([
("loan_id", "int64"),
("orig_channel", "category"),
("seller_name", "category"),
("orig_interest_rate", "float64"),
("orig_upb", "int64"),
("orig_loan_term", "int64"),
("orig_date", "date"),
("first_pay_date", "date"),
("orig_ltv", "float64"),
("orig_cltv", "float64"),
("num_borrowers", "float64"),
("dti", "float64"),
("borrower_credit_score", "float64"),
("first_home_buyer", "category"),
("loan_purpose", "category"),
("property_type", "category"),
("num_units", "int64"),
("occupancy_status", "category"),
("property_state", "category"),
("zip", "int64"),
("mortgage_insurance_percent", "float64"),
("product_type", "category"),
("coborrow_credit_score", "float64"),
("mortgage_insurance_type", "float64"),
("relocation_mortgage_indicator", "category"),
])
print(acquisition_path)
acquisition_table = pyblazing.create_table(
table_name="acq",
type=get_type_schema(acquisition_path),
path=acquisition_path,
delimiter="|",
names=cols,
dtypes=dtypes, # TODO: dtypes=get_dtype_values(dtypes)
skip_rows=1,
)
Chronometer.show(chronometer, "Read Acquisition CSV")
return acquisition_table
def gpu_load_performance_csv(performance_path, **kwargs):
""" Loads performance data
Returns
-------
GPU DataFrame
"""
chronometer = Chronometer.makeStarted()
cols = [
"loan_id",
"monthly_reporting_period",
"servicer",
"interest_rate",
"current_actual_upb",
"loan_age",
"remaining_months_to_legal_maturity",
"adj_remaining_months_to_maturity",
"maturity_date",
"msa",
"current_loan_delinquency_status",
"mod_flag",
"zero_balance_code",
"zero_balance_effective_date",
"last_paid_installment_date",
"foreclosed_after",
"disposition_date",
"foreclosure_costs",
"prop_preservation_and_repair_costs",
"asset_recovery_costs",
"misc_holding_expenses",
"holding_taxes",
"net_sale_proceeds",
"credit_enhancement_proceeds",
"repurchase_make_whole_proceeds",
"other_foreclosure_proceeds",
"non_interest_bearing_upb",
"principal_forgiveness_upb",
"repurchase_make_whole_proceeds_flag",
"foreclosure_principal_write_off_amount",
"servicing_activity_indicator",
]
dtypes = OrderedDict([
("loan_id", "int64"),
("monthly_reporting_period", "date"),
("servicer", "category"),
("interest_rate", "float64"),
("current_actual_upb", "float64"),
("loan_age", "float64"),
("remaining_months_to_legal_maturity", "float64"),
("adj_remaining_months_to_maturity", "float64"),
("maturity_date", "date"),
("msa", "float64"),
("current_loan_delinquency_status", "int32"),
("mod_flag", "category"),
("zero_balance_code", "category"),
("zero_balance_effective_date", "date"),
("last_paid_installment_date", "date"),
("foreclosed_after", "date"),
("disposition_date", "date"),
("foreclosure_costs", "float64"),
("prop_preservation_and_repair_costs", "float64"),
("asset_recovery_costs", "float64"),
("misc_holding_expenses", "float64"),
("holding_taxes", "float64"),
("net_sale_proceeds", "float64"),
("credit_enhancement_proceeds", "float64"),
("repurchase_make_whole_proceeds", "float64"),
("other_foreclosure_proceeds", "float64"),
("non_interest_bearing_upb", "float64"),
("principal_forgiveness_upb", "float64"),
("repurchase_make_whole_proceeds_flag", "category"),
("foreclosure_principal_write_off_amount", "float64"),
("servicing_activity_indicator", "category"),
])
print(performance_path)
performance_table = pyblazing.create_table(
table_name="perf",
type=get_type_schema(performance_path),
path=performance_path,
delimiter="|",
names=cols,
dtypes=dtypes, # TODO: dtypes=get_dtype_values(dtypes)
skip_rows=1,
)
Chronometer.show(chronometer, "Read Performance CSV")
return performance_table
