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import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest, chi2
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from imblearn.over_sampling import SMOTE
import kagglehub
import pickle
# Encoder Class
class Encoder(BaseEstimator, TransformerMixin):
def __init__(self, categorical_columns, target_column):
self.categorical_columns = categorical_columns
self.target_column = target_column
self.ohe = OneHotEncoder(sparse_output=False)
self.le = LabelEncoder()
self.encoded_feature_names = [] # Store encoded feature names
def fit(self, X, y=None):
self.ohe.fit(X[self.categorical_columns])
self.le.fit(X[self.target_column])
self.encoded_feature_names = self.ohe.get_feature_names_out(self.categorical_columns).tolist() # Store encoded feature names
return self
def transform(self, X):
encoded = self.ohe.transform(X[self.categorical_columns])
encoded_df = pd.DataFrame(
encoded,
columns=self.encoded_feature_names,
index=X.index
)
result = pd.concat([
X.drop(self.categorical_columns + [self.target_column], axis=1),
encoded_df
], axis=1)
result[self.target_column] = self.le.transform(X[self.target_column])
return result
class FeatureSelector(BaseEstimator, TransformerMixin):
def __init__(self, numeric_features, encoded_features, target_column, num_k=5, cat_k=5):
"""
:param numeric_features: List of numeric feature names
:param encoded_features: List of encoded feature names
:param target_column: Target column name
:param num_k: Number of top numeric features to select
:param cat_k: Number of top encoded features to select
"""
self.numeric_features = numeric_features
self.encoded_features = encoded_features # Use encoded features
self.target_column = target_column
self.num_k = num_k
self.cat_k = cat_k
self.chi2_selector = None
self.numeric_selector = None
def fit(self, X, y=None):
# Pearson correlation for numeric features
self.numeric_selector = X[self.numeric_features].corrwith(X[self.target_column]).abs().nlargest(self.num_k).index.tolist()
# Chi-Square for encoded categorical features
X_encoded = X[self.encoded_features]
y = X[self.target_column]
# Apply chi-squared test and select top k features
self.chi2_selector = SelectKBest(chi2, k=self.cat_k).fit(X_encoded, y)
return self
def transform(self, X):
# Select top numeric features based on Pearson correlation
X_selected_num = X[self.numeric_selector]
y = X[self.target_column]
# Select top encoded categorical features based on Chi-Square
X_encoded = X[self.encoded_features]
X_selected_cat = pd.DataFrame(self.chi2_selector.transform(X_encoded), columns=self.chi2_selector.get_feature_names_out(), index=X.index)
# Concatenate selected numeric and categorical features
return pd.concat([X_selected_num, X_selected_cat, y], axis=1)
# Splitter Class
class Splitter(BaseEstimator, TransformerMixin):
def __init__(self, target_column, test_size=0.3, random_state=42):
self.target_column = target_column
self.test_size = test_size
self.random_state = random_state
def fit(self, X, y=None):
return self
def transform(self, X):
y = X[self.target_column]
X = X.drop(self.target_column, axis=1)
return tuple(train_test_split(X, y, test_size=self.test_size, random_state=self.random_state))
# Scaler Class
class Scaler(BaseEstimator, TransformerMixin):
def __init__(self, scaler_type='standard'):
self.scaler = StandardScaler() if scaler_type == 'standard' else MinMaxScaler()
def fit(self, X, y=None):
return self
def transform(self, X):
if isinstance(X, tuple) and len(X) == 4:
X_train, X_test, y_train, y_test = X
X_train_scaled = self.scaler.fit_transform(X_train)
X_test_scaled = self.scaler.transform(X_test)
return X_train_scaled, X_test_scaled, y_train, y_test
else:
return self.scaler.fit_transform(X)
# Full pipeline with feature selection
class FullPipeline:
def __init__(self, categorical_columns, target_column, numeric_features, num_k=5, cat_k=5):
self.encoder = Encoder(categorical_columns, target_column)
self.feature_selector = None # Initialize after encoding to access encoded names
self.splitter = Splitter(target_column)
self.scaler = Scaler()
self.numeric_features = numeric_features
self.num_k = num_k
self.cat_k = cat_k
def fit_transform(self, X):
# Apply encoding and retrieve encoded feature names
X = self.encoder.fit_transform(X)
self.feature_selector = FeatureSelector(
numeric_features=self.numeric_features,
encoded_features=self.encoder.encoded_feature_names,
target_column=self.encoder.target_column,
num_k=self.num_k, cat_k=self.cat_k
)
X = self.feature_selector.fit_transform(X)
X_train, X_test, y_train, y_test = self.splitter.transform(X)
return self.scaler.transform((X_train, X_test, y_train, y_test))
class FullPipeline:
def __init__(self, categorical_columns, target_column, numeric_features, num_k=5, cat_k=5):
self.encoder = Encoder(categorical_columns, target_column)
self.feature_selector = None # Initialize after encoding to access encoded names
self.splitter = Splitter(target_column)
self.scaler = Scaler()
self.numeric_features = numeric_features
self.num_k = num_k
self.cat_k = cat_k
def fit_transform(self, X):
X = self.encoder.fit_transform(X)
pickle.dump(self.encoder, open("encoder.pkl", "wb"))
self.feature_selector = FeatureSelector(
numeric_features=self.numeric_features,
encoded_features=self.encoder.encoded_feature_names,
target_column=self.encoder.target_column,
num_k=self.num_k, cat_k=self.cat_k
)
X = self.feature_selector.fit_transform(X)
pickle.dump(self.feature_selector, open("feature_selector.pkl", "wb"))
X_train, X_test, y_train, y_test = self.splitter.transform(X)
pickle.dump(self.splitter, open("splitter.pkl", "wb"))
X_train_scaled, X_test_scaled, y_train, y_test = self.scaler.transform((X_train, X_test, y_train, y_test))
pickle.dump(self.scaler, open("scaler.pkl", "wb"))
return (X_train_scaled, X_test_scaled, y_train, y_test)
def main():
path = kagglehub.dataset_download("fedesoriano/heart-failure-prediction")
df = pd.read_csv(path + r"\heart.csv")
df.drop_duplicates(inplace=True) # dropping the duplicates
# defining the pipeline
pipeline = FullPipeline(
categorical_columns=['Sex', 'ChestPainType', 'RestingECG', 'ExerciseAngina', 'ST_Slope'],
target_column='HeartDisease',
numeric_features=['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak'],
num_k=3, # Select top 3 numeric features
cat_k=3 # Select top 3 categorical features
)
# transforming the data
X_train, X_test, y_train, y_test = pipeline.fit_transform(df)
with open("transformed_data.pkl", "wb") as f:
pickle.dump((X_train, X_test, y_train, y_test), f)
if __name__ == "__main__":
main() |