File size: 8,145 Bytes
b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 b8bf9dd 6454725 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
import pandas as pd
import numpy as np
import time
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from imblearn.over_sampling import SMOTE
# ===========================
# CONFIGURATION
# ===========================
TRAIN_PATH = "data/train_dataset_full - train_dataset_full.csv"
# TRAIN_PATH = "data/train_dataset_full - train_dataset_partial_for_testing.csv"
TEST_PATH = "data/X_test_1st.csv" # Replace with actual test dataset path
CATEGORICAL_COLUMNS = ["gender", "product",]
IDS_COLUMNS = [ "user_id", "session_id", "campaign_id", "webpage_id"]
TARGET_COLUMN = "is_click"
FEATURE_COLUMNS = [
"age_level", "gender", "product",
"product_category_1", "product_category_2", "user_group_id",
"user_depth", "city_development_index", "var_1"
]
AGGREGATED_COLUMNS = [
"click_sum_age_sex_prod", "click_count_age_sex_prod",
"unique_campaigns_age_sex_prod", "unique_webpages_age_sex_prod",
"click_sum_city_age_prod", "click_count_city_age_prod",
"unique_campaigns_city_age_prod", "unique_webpages_city_age_prod"
]
TEMPORAL_COLUMNS = ["year", "month", "day", "hour", "minute", "weekday"]
# ===========================
# LOAD DATASETS
# ===========================
def load_data(train_path=TRAIN_PATH, test_path=TEST_PATH):
"""Load train & test datasets, handling missing values."""
train_df = pd.read_csv(train_path)
y_train = train_df[TARGET_COLUMN]
train_df = train_df[~y_train.isnull()]
test_df = pd.read_csv(test_path)
train_df["DateTime"] = pd.to_datetime(train_df["DateTime"])
test_df["DateTime"] = pd.to_datetime(test_df["DateTime"])
train_df["DateTime"].fillna(train_df["DateTime"].mode()[0], inplace=True)
test_df["DateTime"].fillna(test_df["DateTime"].mode()[0], inplace=True)
if "DateTime" in train_df.columns:
train_df["DateTime"] = pd.to_datetime(train_df["DateTime"])
train_df["year"] = train_df["DateTime"].dt.year
train_df["month"] = train_df["DateTime"].dt.month
train_df["day"] = train_df["DateTime"].dt.day
train_df["hour"] = train_df["DateTime"].dt.hour
train_df["minute"] = train_df["DateTime"].dt.minute
train_df["weekday"] = train_df["DateTime"].dt.weekday
train_df.drop("DateTime", axis=1, inplace=True)
if "DateTime" in test_df.columns:
test_df["DateTime"] = pd.to_datetime(test_df["DateTime"])
test_df["year"] = test_df["DateTime"].dt.year
test_df["month"] = test_df["DateTime"].dt.month
test_df["day"] = test_df["DateTime"].dt.day
test_df["hour"] = test_df["DateTime"].dt.hour
test_df["minute"] = test_df["DateTime"].dt.minute
test_df["weekday"] = test_df["DateTime"].dt.weekday
test_df.drop("DateTime", axis=1, inplace=True)
# Fill missing values
train_df.fillna(-1, inplace=True)
test_df.fillna(-1, inplace=True)
return train_df, test_df
# ===========================
# FEATURE ENGINEERING: AGGREGATIONS
# ===========================
def add_aggregated_features(df, test_df):
"""Creates aggregated features based on age, gender, and product interactions."""
# Aggregate by age & gender vs product
age_sex_product_agg = df.groupby(["age_level", "gender", "product"]).agg({
"is_click": ["sum", "count"],
"campaign_id": "nunique",
"webpage_id": "nunique"
}).reset_index()
# Rename columns after aggregation
age_sex_product_agg.columns = ["age_level", "gender", "product",
"click_sum_age_sex_prod", "click_count_age_sex_prod",
"unique_campaigns_age_sex_prod", "unique_webpages_age_sex_prod"]
# Merge into train & test datasets
df = df.merge(age_sex_product_agg, on=["age_level", "gender", "product"], how="left")
test_df = test_df.merge(age_sex_product_agg, on=["age_level", "gender", "product"], how="left")
# Aggregate by city, age, product
city_age_product_agg = df.groupby(["city_development_index", "age_level", "product"]).agg({
"is_click": ["sum", "count"],
"campaign_id": "nunique",
"webpage_id": "nunique"
}).reset_index()
# Rename columns
city_age_product_agg.columns = ["city_development_index", "age_level", "product",
"click_sum_city_age_prod", "click_count_city_age_prod",
"unique_campaigns_city_age_prod", "unique_webpages_city_age_prod"]
# Merge into train & test datasets
df = df.merge(city_age_product_agg, on=["city_development_index", "age_level", "product"], how="left")
test_df = test_df.merge(city_age_product_agg, on=["city_development_index", "age_level", "product"], how="left")
# Fill missing values after merging
df.fillna(0, inplace=True)
test_df.fillna(0, inplace=True)
return df, test_df
# ===========================
# ENCODE & NORMALIZE FEATURES
# ===========================
def preprocess_data(df, test_df, categorical_columns):
"""Encodes categorical features, normalizes numerical features, and prepares the dataset."""
label_encoders = {}
for col in categorical_columns:
le = LabelEncoder()
df[col] = le.fit_transform(df[col].astype(str))
test_df[col] = test_df[col].astype(str).map(lambda s: le.transform([s])[0] if s in le.classes_ else -1)
label_encoders[col] = le # Store encoders for later use
numerical_columns = [col for col in FEATURE_COLUMNS + AGGREGATED_COLUMNS if col not in categorical_columns]
# scaler = StandardScaler()
# df[numerical_columns] = scaler.fit_transform(df[numerical_columns])
# test_df[numerical_columns] = scaler.transform(test_df[numerical_columns])
return df, test_df, label_encoders,# scaler
# ===========================
# SPLIT DATA & HANDLE IMBALANCE
# ===========================
def split_and_balance_data(df, target_column):
"""Splits data into training and validation sets, applies SMOTE to balance classes."""
X = df[IDS_COLUMNS + FEATURE_COLUMNS + AGGREGATED_COLUMNS + TEMPORAL_COLUMNS]
y = df[target_column]
# Handle class imbalance using SMOTE
smote = SMOTE(sampling_strategy="auto", random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)
# Split into training & validation sets
X_train, X_val, y_train, y_val = train_test_split(
X_resampled, y_resampled, test_size=0.2, random_state=42, stratify=y_resampled
)
return X_train, X_val, y_train, y_val
# ===========================
# VISUALIZE FEATURES
# ===========================
def visualize_features():
"""Generates visualizations for aggregated features."""
df, _ = load_data()
df, _ = add_aggregated_features(df, df)
sns.set_style("whitegrid")
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
sns.barplot(x="age_level", y="click_sum_age_sex_prod", hue="gender",
data=df, ax=axes[0], palette="coolwarm")
axes[0].set_title("Total Clicks by Age & Gender vs Product")
sns.barplot(x="city_development_index", y="click_sum_city_age_prod", hue="age_level",
data=df, ax=axes[1], palette="viridis")
axes[1].set_title("Total Clicks by City Development Index & Age")
plt.tight_layout()
plt.show()
# ===========================
# RUN FULL DATA PROCESSING PIPELINE
# ===========================
def load_and_process_data():
"""Runs the full data processing pipeline and returns preprocessed training & test data."""
df, test_df = load_data()
df, test_df = add_aggregated_features(df, test_df)
df, test_df, label_encoders = preprocess_data(df, test_df, CATEGORICAL_COLUMNS)
X_train, X_val, y_train, y_val = split_and_balance_data(df, TARGET_COLUMN)
return X_train, X_val, y_train, y_val, test_df
if __name__ == "__main__":
print("🔹 Loading and processing data...")
X_train, X_val, y_train, y_val, test_df = load_and_process_data()
print("✅ Data successfully loaded and processed!")
|