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import joblib
from sklearn.datasets import fetch_openml
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report
dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto")
data_df = dataset.data
target = 'Machine failure'
numeric_features = [
'Air temperature [K]',
'Process temperature [K]',
'Rotational speed [rpm]',
'Torque [Nm]',
'Tool wear [min]'
]
categorical_features = ['Type']
print("Creating data subsets")
X = data_df[numeric_features + categorical_features]
y = data_df[target]
Xtrain, Xtest, ytrain, ytest = train_test_split(
X, y,
test_size=0.2,
random_state=42
)
preprocessor = make_column_transformer(
(StandardScaler(), numeric_features),
(OneHotEncoder(handle_unknown='ignore'), categorical_features)
)
model_logistic_regression = LogisticRegression(n_jobs=-1)
print("Estimating Best Model Pipeline")
model_pipeline = make_pipeline(
preprocessor,
model_logistic_regression
)
param_distribution = {
"logisticregression__C": [0.001, 0.01, 0.1, 0.5, 1, 5, 10]
}
rand_search_cv = RandomizedSearchCV(
model_pipeline,
param_distribution,
n_iter=3,
cv=3,
random_state=42
)
rand_search_cv.fit(Xtrain, ytrain)
print("Logging Metrics")
print(f"Accuracy: {rand_search_cv.best_score_}")
print("Serializing Model")
saved_model_path = "model.joblib"
joblib.dump(rand_search_cv.best_estimator_, saved_model_path)
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