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Add query.py #5

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Sep 7, 2021
Merged

Add query.py #5

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3 changes: 2 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
@@ -1,2 +1,3 @@
.env
*.pkl
*.pkl
__pycache__
61 changes: 61 additions & 0 deletions module/query.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,61 @@
# INSERT
INSERT_REG_MODEL = """
INSERT INTO reg_model (
model_name,
path
) VALUES(
{},
{}
)
"""

INSERT_REG_MODEL_METADATA = """
INSERT INTO reg_model_metadata (
experiment_name,
reg_model_name,
experimenter,
version,
train_mae,
val_mae,
train_mse,
val_mse
) VALUES (
{},
{},
{},
{},
{},
{},
{},
{}
)
"""

# UPDATE
UPDATE_REG_MODEL_METADATA = """
UPDATE reg_model_metadata
SET
train_mae = {},
val_mae = {},
train_mse = {},
val_mse = {}
WHERE experiment_name = {}
"""

# pd READ_SQL
ALL_INSURANCE = """
SELECT *
FROM insurance
"""

VAL_MAE = """
SELECT val_mae
FROM reg_model_metadata
WHERE reg_model_name = %s
"""

MODEL = """
SELECT *
FROM reg_model
WHERE model_name = %s
"""
77 changes: 24 additions & 53 deletions module/trial.py
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Expand Up @@ -12,6 +12,8 @@
from sqlalchemy.engine import create_engine
from xgboost.sklearn import XGBRegressor

from query import *

load_dotenv(verbose=True)


Expand Down Expand Up @@ -106,65 +108,37 @@ def main(params, df, engine, experiment_info, connection):
tr_mse_mean = np.mean(tr_mse)
tr_mae_mean = np.mean(tr_mae)

best_model = pd.read_sql(f"""
SELECT *
FROM reg_model
WHERE model_name = {model_name}
""", engine)
best_model = pd.read_sql(MODEL % (model_name), engine)

if len(best_model) == 0:

with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f:
pickle.dump(model, f)
connection.execute(f"""
INSERT INTO reg_model (
model_name,
path
) VALUES(
{model_name},
{path}
)
""")
connection.execute(f"""
INSERT INTO reg_model_metadata (
experiment_name,
reg_model_name,
experimenter,
version,
train_mae,
val_mae,
train_mse,
val_mse
) VALUES (
{experiment_name},
{model_name},
{experimenter},
{version},
{tr_mae_mean},
{tr_mse_mean},
{cv_mae_mean},
{cv_mse_mean}
)
""")
connection.execute(INSERT_REG_MODEL % (model_name, path))
connection.execute(INSERT_REG_MODEL_METADATA % (
experiment_name,
model_name,
experimenter,
version,
tr_mae_mean,
tr_mse_mean,
cv_mae_mean,
cv_mse_mean)
)

else:
best_model_metadata = pd.read_sql(f"""
SELECT val_mae
FROM reg_model_metadata
WHERE reg_model_name = {model_name}
""", engine)
best_model_metadata = pd.read_sql(VAL_MAE % (model_name), engine)
saved_score = best_model_metadata.values[0]
if saved_score > valid_mae:
with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f:
pickle.dump(model, f)
connection.execute(f"""
UPDATE reg_model_metadata
SET
train_mae = {tr_mae_mean},
val_mae = {cv_mae_mean},
train_mse = {tr_mse_mean},
val_mse = {cv_mse_mean}
WHERE experiment_name = {experiment_name}
""")
connection.execute(UPDATE_REG_MODEL_METADATA % (
tr_mae_mean,
cv_mae_mean,
tr_mse_mean,
cv_mse_mean,
experiment_name)
)

nni.report_final_result(cv_mae_mean)
print('Final result is %g', cv_mae_mean)
Expand All @@ -191,10 +165,7 @@ def main(params, df, engine, experiment_info, connection):
'version': 0.1
}

df = pd.read_sql("""
SELECT *
FROM insurance
""", engine)
df = pd.read_sql(ALL_INSURANCE, engine)

with engine.connect() as connection:
with connection.begin():
Expand Down