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test_kaggle_ipynb.py
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test_kaggle_ipynb.py
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import logging
import os
import subprocess
import numpy as np
import pytest
import modin.pandas as pd
# import ray
# ray.init(address="localhost:6379")
logger = logging.getLogger(__name__)
# Size for synthetic datasets
DF_SIZE = 1 * 2**10 * 2**10 # * 2**10 # 1 GiB dataframes
# This file path
DIR_PATH = os.path.dirname(os.path.realpath(__file__))
KAGGLE_DIR_PATH = "{}/kaggle".format(DIR_PATH)
def create_dataframe(columns, dtypes, size):
def _num_to_str(x):
letters = ""
while x:
mod = (x - 1) % 26
letters += chr(mod + 65)
x = (x - 1) // 26
result = "".join(reversed(letters))
if "NA" in result:
return _num_to_str(x + 1)
else:
return result
result_dict = {}
for col, dtype in zip(columns, dtypes):
if dtype is str:
result_dict[col] = [_num_to_str(x + 1) for x in np.arange(size, dtype=int)]
elif dtype is bool:
result_dict[col] = [x % 2 == 0 for x in np.arange(size, dtype=int)]
else:
result_dict[col] = np.arange(size, dtype=dtype)
return pd.DataFrame(result_dict)
@pytest.fixture
def generate_dataset():
"""Generates a synthetic dataset using the given arguments.
Args:
columns (list): Column names of the result
dtypes (list): List of dtypes for the corresponding column
size (int): Number of rows for result
Returns:
Modin dataframe of synthetic data following arguments.
"""
# Record of files generated for a test
filenames = []
def _dataset_builder(filename, columns, dtypes, size=DF_SIZE, files_to_remove=[]):
# Add the files generated by the script to be removed
for file in files_to_remove:
filenames.append("{}/{}".format(KAGGLE_DIR_PATH, file))
# Update filename to include path
filename = "{}/{}".format(KAGGLE_DIR_PATH, filename)
# Check that the number of column names is the same as the nubmer of dtypes
if len(columns) != len(dtypes):
raise ValueError("len(columns) != len(dtypes)")
# Determine number of rows for synthetic dataset
row_size = (
create_dataframe(columns, dtypes, 1)
.memory_usage(index=False, deep=True)
.sum()
)
result = create_dataframe(columns, dtypes, np.ceil(size / row_size))
result.to_csv(filename)
filenames.append(filename)
return result
# Return dataset builder factory
yield _dataset_builder
# Delete files created
for filename in filenames:
if os.path.exists(filename):
os.remove(filename)
def test_kaggle3(generate_dataset):
pokemon_columns = [
"#",
"Name",
"Type 1",
"Type 2",
"HP",
"Attack",
"Defense",
"Sp. Atk",
"Sp. Def",
"Speed",
"Generation",
"Legendary",
]
pokemon_dtypes = [int, str, str, str, int, int, int, int, int, int, int, bool]
generate_dataset(
"pokemon.csv", pokemon_columns, pokemon_dtypes, files_to_remove=["graph.png"]
)
ipynb = subprocess.Popen(
["python", "kaggle3.py"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=KAGGLE_DIR_PATH,
)
outs, errs = ipynb.communicate()
if ipynb.returncode:
logging.debug("Error message\n-------------\n %s", errs.decode("utf-8"))
logging.info("Finished kaggle3")
assert ipynb.returncode == 0
def test_kaggle4(generate_dataset):
columns = [
"Id",
"MSSubClass",
"MSZoning",
"LotFrontage",
"LotArea",
"Street",
"Alley",
"LotShape",
"LandContour",
"Utilities",
"LotConfig",
"LandSlope",
"Neighborhood",
"Condition1",
"Condition2",
"BldgType",
"HouseStyle",
"OverallQual",
"OverallCond",
"YearBuilt",
"YearRemodAdd",
"RoofStyle",
"RoofMatl",
"Exterior1st",
"Exterior2nd",
"MasVnrType",
"MasVnrArea",
"ExterQual",
"ExterCond",
"Foundation",
"BsmtQual",
"BsmtCond",
"BsmtExposure",
"BsmtFinType1",
"BsmtFinSF1",
"BsmtFinType2",
"BsmtFinSF2",
"BsmtUnfSF",
"TotalBsmtSF",
"Heating",
"HeatingQC",
"CentralAir",
"Electrical",
"1stFlrSF",
"2ndFlrSF",
"LowQualFinSF",
"GrLivArea",
"BsmtFullBath",
"BsmtHalfBath",
"FullBath",
"HalfBath",
"BedroomAbvGr",
"KitchenAbvGr",
"KitchenQual",
"TotRmsAbvGrd",
"Functional",
"Fireplaces",
"FireplaceQu",
"GarageType",
"GarageYrBlt",
"GarageFinish",
"GarageCars",
"GarageArea",
"GarageQual",
"GarageCond",
"PavedDrive",
"WoodDeckSF",
"OpenPorchSF",
"EnclosedPorch",
"3SsnPorch",
"ScreenPorch",
"PoolArea",
"PoolQC",
"Fence",
"MiscFeature",
"MiscVal",
"MoSold",
"YrSold",
"SaleType",
"SaleCondition",
"SalePrice",
]
dtypes = [
int,
int,
str,
float,
int,
str,
float,
str,
str,
str,
str,
str,
str,
str,
str,
str,
str,
int,
int,
int,
int,
str,
str,
str,
str,
str,
float,
str,
str,
str,
str,
str,
str,
str,
int,
str,
int,
int,
int,
str,
str,
str,
str,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
str,
int,
str,
int,
float,
str,
float,
str,
int,
int,
str,
str,
str,
int,
int,
int,
int,
int,
int,
float,
float,
float,
int,
int,
int,
str,
str,
int,
]
generate_dataset("train.csv", columns, dtypes)
generate_dataset("test.csv", columns, dtypes)
ipynb = subprocess.Popen(
["python", "kaggle4.py"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=KAGGLE_DIR_PATH,
)
outs, errs = ipynb.communicate()
if ipynb.returncode:
logging.debug("Error message\n-------------\n %s", errs.decode("utf-8"))
logging.info("Finished kaggle4")
assert ipynb.returncode == 0
def test_kaggle5(generate_dataset):
columns = [
"PassengerId",
"Survived",
"Pclass",
"Name",
"Sex",
"Age",
"SibSp",
"Parch",
"Ticket",
"Fare",
"Cabin",
"Embarked",
]
dtypes = [int, int, int, str, str, float, int, int, str, float, float, str]
generate_dataset("train.csv", columns, dtypes)
generate_dataset("test.csv", columns, dtypes)
ipynb = subprocess.Popen(
["python", "kaggle5.py"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=KAGGLE_DIR_PATH,
)
outs, errs = ipynb.communicate()
if ipynb.returncode:
logging.debug("Error message\n-------------\n %s", errs.decode("utf-8"))
logging.info("Finished kaggle5")
assert ipynb.returncode == 0
@pytest.mark.skip("Missing Original Data Schema")
def test_kaggle6(generate_dataset):
columns = []
dtypes = []
generate_dataset("test.csv", columns, dtypes)
generate_dataset("train.csv", columns, dtypes)
ipynb = subprocess.Popen(
["python", "kaggle6.py"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=KAGGLE_DIR_PATH,
)
outs, errs = ipynb.communicate()
if ipynb.returncode:
logging.debug("Error message\n-------------\n %s", errs.decode("utf-8"))
logging.info("Finished kaggle6")
assert ipynb.returncode == 0
def test_kaggle7(generate_dataset):
columns = [
"SK_ID_CURR",
"TARGET",
"NAME_CONTRACT_TYPE",
"CODE_GENDER",
"FLAG_OWN_CAR",
"FLAG_OWN_REALTY",
"CNT_CHILDREN",
"AMT_INCOME_TOTAL",
"AMT_CREDIT",
"AMT_ANNUITY",
"AMT_GOODS_PRICE",
"NAME_TYPE_SUITE",
"NAME_INCOME_TYPE",
"NAME_EDUCATION_TYPE",
"NAME_FAMILY_STATUS",
"NAME_HOUSING_TYPE",
"REGION_POPULATION_RELATIVE",
"DAYS_BIRTH",
"DAYS_EMPLOYED",
"DAYS_REGISTRATION",
"DAYS_ID_PUBLISH",
"OWN_CAR_AGE",
"FLAG_MOBIL",
"FLAG_EMP_PHONE",
"FLAG_WORK_PHONE",
"FLAG_CONT_MOBILE",
"FLAG_PHONE",
"FLAG_EMAIL",
"OCCUPATION_TYPE",
"CNT_FAM_MEMBERS",
"REGION_RATING_CLIENT",
"REGION_RATING_CLIENT_W_CITY",
"WEEKDAY_APPR_PROCESS_START",
"HOUR_APPR_PROCESS_START",
"REG_REGION_NOT_LIVE_REGION",
"REG_REGION_NOT_WORK_REGION",
"LIVE_REGION_NOT_WORK_REGION",
"REG_CITY_NOT_LIVE_CITY",
"REG_CITY_NOT_WORK_CITY",
"LIVE_CITY_NOT_WORK_CITY",
"ORGANIZATION_TYPE",
"EXT_SOURCE_1",
"EXT_SOURCE_2",
"EXT_SOURCE_3",
"APARTMENTS_AVG",
"BASEMENTAREA_AVG",
"YEARS_BEGINEXPLUATATION_AVG",
"YEARS_BUILD_AVG",
"COMMONAREA_AVG",
"ELEVATORS_AVG",
"ENTRANCES_AVG",
"FLOORSMAX_AVG",
"FLOORSMIN_AVG",
"LANDAREA_AVG",
"LIVINGAPARTMENTS_AVG",
"LIVINGAREA_AVG",
"NONLIVINGAPARTMENTS_AVG",
"NONLIVINGAREA_AVG",
"APARTMENTS_MODE",
"BASEMENTAREA_MODE",
"YEARS_BEGINEXPLUATATION_MODE",
"YEARS_BUILD_MODE",
"COMMONAREA_MODE",
"ELEVATORS_MODE",
"ENTRANCES_MODE",
"FLOORSMAX_MODE",
"FLOORSMIN_MODE",
"LANDAREA_MODE",
"LIVINGAPARTMENTS_MODE",
"LIVINGAREA_MODE",
"NONLIVINGAPARTMENTS_MODE",
"NONLIVINGAREA_MODE",
"APARTMENTS_MEDI",
"BASEMENTAREA_MEDI",
"YEARS_BEGINEXPLUATATION_MEDI",
"YEARS_BUILD_MEDI",
"COMMONAREA_MEDI",
"ELEVATORS_MEDI",
"ENTRANCES_MEDI",
"FLOORSMAX_MEDI",
"FLOORSMIN_MEDI",
"LANDAREA_MEDI",
"LIVINGAPARTMENTS_MEDI",
"LIVINGAREA_MEDI",
"NONLIVINGAPARTMENTS_MEDI",
"NONLIVINGAREA_MEDI",
"FONDKAPREMONT_MODE",
"HOUSETYPE_MODE",
"TOTALAREA_MODE",
"WALLSMATERIAL_MODE",
"EMERGENCYSTATE_MODE",
"OBS_30_CNT_SOCIAL_CIRCLE",
"DEF_30_CNT_SOCIAL_CIRCLE",
"OBS_60_CNT_SOCIAL_CIRCLE",
"DEF_60_CNT_SOCIAL_CIRCLE",
"DAYS_LAST_PHONE_CHANGE",
"FLAG_DOCUMENT_2",
"FLAG_DOCUMENT_3",
"FLAG_DOCUMENT_4",
"FLAG_DOCUMENT_5",
"FLAG_DOCUMENT_6",
"FLAG_DOCUMENT_7",
"FLAG_DOCUMENT_8",
"FLAG_DOCUMENT_9",
"FLAG_DOCUMENT_10",
"FLAG_DOCUMENT_11",
"FLAG_DOCUMENT_12",
"FLAG_DOCUMENT_13",
"FLAG_DOCUMENT_14",
"FLAG_DOCUMENT_15",
"FLAG_DOCUMENT_16",
"FLAG_DOCUMENT_17",
"FLAG_DOCUMENT_18",
"FLAG_DOCUMENT_19",
"FLAG_DOCUMENT_20",
"FLAG_DOCUMENT_21",
"AMT_REQ_CREDIT_BUREAU_HOUR",
"AMT_REQ_CREDIT_BUREAU_DAY",
"AMT_REQ_CREDIT_BUREAU_WEEK",
"AMT_REQ_CREDIT_BUREAU_MON",
"AMT_REQ_CREDIT_BUREAU_QRT",
"AMT_REQ_CREDIT_BUREAU_YEAR",
]
dtypes = [
int,
int,
str,
str,
str,
str,
int,
float,
float,
float,
float,
str,
str,
str,
str,
str,
float,
int,
int,
float,
int,
float,
int,
int,
int,
int,
int,
int,
str,
float,
int,
int,
str,
int,
int,
int,
int,
int,
int,
int,
str,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
float,
str,
str,
float,
str,
str,
float,
float,
float,
float,
float,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
float,
float,
float,
float,
float,
float,
]
generate_dataset(
"application_train.csv",
columns,
dtypes,
files_to_remove=[
"log_reg_baseline.csv",
"random_forest_baseline.csv",
"random_forest_baseline_engineered.csv",
"random_forest_baseline_domain.csv",
"baseline_lgb.csv",
"baseline_lgb_domain_features.csv",
],
)
generate_dataset("application_test.csv", columns, dtypes)
ipynb = subprocess.Popen(
["python", "kaggle7.py"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=KAGGLE_DIR_PATH,
)
outs, errs = ipynb.communicate()
if ipynb.returncode:
logging.debug("Error message\n-------------\n %s", errs.decode("utf-8"))
logging.info("Finished kaggle7")
assert ipynb.returncode == 0
def test_kaggle8(generate_dataset):
columns = [
"Id",
"MSSubClass",
"MSZoning",
"LotFrontage",
"LotArea",
"Street",
"Alley",
"LotShape",
"LandContour",
"Utilities",
"LotConfig",
"LandSlope",
"Neighborhood",
"Condition1",
"Condition2",
"BldgType",
"HouseStyle",
"OverallQual",
"OverallCond",
"YearBuilt",
"YearRemodAdd",
"RoofStyle",
"RoofMatl",
"Exterior1st",
"Exterior2nd",
"MasVnrType",
"MasVnrArea",
"ExterQual",
"ExterCond",
"Foundation",
"BsmtQual",
"BsmtCond",
"BsmtExposure",
"BsmtFinType1",
"BsmtFinSF1",
"BsmtFinType2",
"BsmtFinSF2",
"BsmtUnfSF",
"TotalBsmtSF",
"Heating",
"HeatingQC",
"CentralAir",
"Electrical",
"1stFlrSF",
"2ndFlrSF",
"LowQualFinSF",
"GrLivArea",
"BsmtFullBath",
"BsmtHalfBath",
"FullBath",
"HalfBath",
"BedroomAbvGr",
"KitchenAbvGr",
"KitchenQual",
"TotRmsAbvGrd",
"Functional",
"Fireplaces",
"FireplaceQu",
"GarageType",
"GarageYrBlt",
"GarageFinish",
"GarageCars",
"GarageArea",
"GarageQual",
"GarageCond",
"PavedDrive",
"WoodDeckSF",
"OpenPorchSF",
"EnclosedPorch",
"3SsnPorch",
"ScreenPorch",
"PoolArea",
"PoolQC",
"Fence",
"MiscFeature",
"MiscVal",
"MoSold",
"YrSold",
"SaleType",
"SaleCondition",
"SalePrice",
]
dtypes = [
int,
int,
str,
float,
int,
str,
float,
str,
str,
str,
str,
str,
str,
str,
str,
str,
str,
int,
int,
int,
int,
str,
str,
str,
str,
str,
float,
str,
str,
str,
str,
str,
str,
str,
float,
str,
float,
float,
float,
str,
str,
str,
str,
int,
int,
int,
int,
float,
float,
int,
int,
int,
int,
str,
int,
str,
int,
float,
str,
float,
str,
float,
float,
str,
str,
str,
int,
int,
int,
int,
int,
int,
float,
str,
float,
int,
int,
int,
str,
str,
int,
]
generate_dataset("test.csv", columns, dtypes, files_to_remove=["submission.csv"])
generate_dataset("train.csv", columns, dtypes)
ipynb = subprocess.Popen(
["python", "kaggle8.py"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=KAGGLE_DIR_PATH,
)
outs, errs = ipynb.communicate()
if ipynb.returncode:
logging.debug("Error message\n-------------\n %s", errs.decode("utf-8"))
logging.info("Finished kaggle8")
assert ipynb.returncode == 0
def test_kaggle9(generate_dataset):
columns = [
"Id",
"MSSubClass",
"MSZoning",
"LotFrontage",
"LotArea",
"Street",
"Alley",
"LotShape",
"LandContour",
"Utilities",
"LotConfig",
"LandSlope",
"Neighborhood",
"Condition1",
"Condition2",
"BldgType",
"HouseStyle",
"OverallQual",
"OverallCond",
"YearBuilt",
"YearRemodAdd",
"RoofStyle",
"RoofMatl",
"Exterior1st",
"Exterior2nd",
"MasVnrType",
"MasVnrArea",
"ExterQual",
"ExterCond",
"Foundation",
"BsmtQual",
"BsmtCond",
"BsmtExposure",
"BsmtFinType1",
"BsmtFinSF1",
"BsmtFinType2",
"BsmtFinSF2",
"BsmtUnfSF",
"TotalBsmtSF",
"Heating",
"HeatingQC",
"CentralAir",
"Electrical",
"1stFlrSF",
"2ndFlrSF",
"LowQualFinSF",
"GrLivArea",
"BsmtFullBath",
"BsmtHalfBath",
"FullBath",
"HalfBath",
"BedroomAbvGr",
"KitchenAbvGr",
"KitchenQual",
"TotRmsAbvGrd",
"Functional",
"Fireplaces",
"FireplaceQu",
"GarageType",
"GarageYrBlt",
"GarageFinish",
"GarageCars",
"GarageArea",
"GarageQual",
"GarageCond",
"PavedDrive",
"WoodDeckSF",
"OpenPorchSF",
"EnclosedPorch",
"3SsnPorch",
"ScreenPorch",
"PoolArea",
"PoolQC",
"Fence",
"MiscFeature",
"MiscVal",
"MoSold",
"YrSold",
"SaleType",
"SaleCondition",
"SalePrice",
]
dtypes = [
int,
int,
str,
float,
int,
str,
float,
str,
str,
str,
str,
str,
str,
str,
str,
str,
str,
int,
int,
int,
int,
str,
str,
str,
str,
str,
float,
str,
str,
str,
str,
str,
str,
str,
int,
str,
int,
int,
int,
str,
str,
str,
str,
int,
int,
int,
int,
int,
int,
int,
int,
int,
int,
str,
int,
str,
int,
float,
str,
float,
str,
int,
int,
str,
str,
str,
int,
int,
int,
int,
int,
int,
float,
float,
float,
int,
int,
int,
str,
str,
int,