final change of formatting

Author: sumeyaaaa

.gitignore

@@ -1,14 +1,10 @@
 
 #
 # DVC local storage
-.data/dvc_file/
 
 
-data\dvc_file\
-
 # Ignore actual data files and cache
 *.csv
-data/dvc_file/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

src/load.py

@@ -18,20 +18,17 @@ def load_data_csv(path):
 def summary_stats(df):
     """Print basic info and stats"""
     print(df.info())
-    print(
-        "\n--- Describe Numerical ---\n",
-        df.describe()
-    )
+    print("\n--- Describe Numerical ---\n", df.describe())
     print("\n--- Describe Categorical ---\n")
-    print(df.select_dtypes(include='object').describe())
+    print(df.select_dtypes(include="object").describe())
 
 
 def logarithmic_numerical_distribution(
     df,
     columns=None,
 ):
     if columns is None:
-        columns = ['Amount', 'Value', 'FraudResult']
+        columns = ["Amount", "Value", "FraudResult"]
 
     """
     Plot log-scale histograms for positive and negative values of specified columns.
@@ -44,49 +41,54 @@ def logarithmic_numerical_distribution(
         pos_vals = df[df[column] > 0][column]
         if not pos_vals.empty:
             plt.hist(pos_vals, bins=50, log=True)
-            plt.title(f'{column} (positive values, log scale)')
+            plt.title(f"{column} (positive values, log scale)")
             plt.xlabel(column)
-            plt.ylabel('Frequency (log scale)')
+            plt.ylabel("Frequency (log scale)")
             plt.show()
 
         neg_vals = np.abs(df[df[column] < 0][column])
         if not neg_vals.empty:
             plt.hist(neg_vals, bins=50, log=True)
-            plt.title(f'{column} (negative abs values, log scale)')
-            plt.xlabel(f'Absolute {column}')
-            plt.ylabel('Frequency (log scale)')
+            plt.title(f"{column} (negative abs values, log scale)")
+            plt.xlabel(f"Absolute {column}")
+            plt.ylabel("Frequency (log scale)")
             plt.show()
 
 
-num_cols = ['CurrencyCode', 'CountryCode', 'PricingStrategy']
+num_cols = ["CurrencyCode", "CountryCode", "PricingStrategy"]
 
 
 def plot_numeric_distributions(df, num_cols=num_cols):
     """Plot histograms for numeric columns"""
     if num_cols is None:
-        num_cols = df.select_dtypes(include=['int64', 'float64']).columns
+        num_cols = df.select_dtypes(include=["int64", "float64"]).columns
 
     for col in num_cols:
         plt.figure(figsize=(8, 4))
-        plt.hist(df[col].dropna(), bins=30, edgecolor='black')
+        plt.hist(df[col].dropna(), bins=30, edgecolor="black")
         plt.title(f"Distribution of {col}")
         plt.xlabel(col)
         plt.ylabel("Frequency")
-        plt.grid(True, linestyle='--', alpha=0.5)
+        plt.grid(True, linestyle="--", alpha=0.5)
         plt.tight_layout()
         plt.show()
 
 
 cat_cols = [
-    'CurrencyCode', 'CountryCode', 'ProviderId', 'ProductCategory',
-    'ChannelId', 'PricingStrategy', 'FraudResult'
+    "CurrencyCode",
+    "CountryCode",
+    "ProviderId",
+    "ProductCategory",
+    "ChannelId",
+    "PricingStrategy",
+    "FraudResult",
 ]
 
 
 def plot_categorical_distributions(df, cat_cols=cat_cols, top_k=10):
     """Plot bar plots for categorical features"""
     if cat_cols is None:
-        cat_cols = df.select_dtypes(include='object').columns
+        cat_cols = df.select_dtypes(include="object").columns
 
     for col in cat_cols:
         plt.figure(figsize=(8, 4))
@@ -101,18 +103,17 @@ def check_missing_values(df):
     """Display missing value counts and percentages"""
     missing = df.isnull().sum()
     missing_percent = (missing / len(df)) * 100
-    missing_df = pd.DataFrame({
-        'Missing Values': missing,
-        'Percent': missing_percent
-    })
-    print(missing_df[missing_df['Missing Values'] > 0])
+    missing_df = pd.DataFrame(
+        {"Missing Values": missing, "Percent": missing_percent}
+    )
+    print(missing_df[missing_df["Missing Values"] > 0])
 
 
 def plot_correlations(df):
     """Plot correlation heatmap for numeric features"""
-    corr = df.select_dtypes(include=['int64', 'float64']).corr()
+    corr = df.select_dtypes(include=["int64", "float64"]).corr()
     plt.figure(figsize=(12, 8))
-    sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=".2f", square=True)
+    sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f", square=True)
     plt.title("Correlation Matrix")
     plt.show()
 
@@ -123,9 +124,9 @@ def cramers_v(confusion_matrix):
     n = confusion_matrix.sum().sum()
     phi2 = chi2 / n
     r, k = confusion_matrix.shape
-    phi2_corr = max(0, phi2 - ((k - 1)*(r - 1)) / (n - 1))
-    r_corr = r - ((r - 1)**2) / (n - 1)
-    k_corr = k - ((k - 1)**2) / (n - 1)
+    phi2_corr = max(0, phi2 - ((k - 1) * (r - 1)) / (n - 1))
+    r_corr = r - ((r - 1) ** 2) / (n - 1)
+    k_corr = k - ((k - 1) ** 2) / (n - 1)
     return np.sqrt(phi2_corr / min((k_corr - 1), (r_corr - 1)))
 
 
@@ -134,7 +135,7 @@ def cramers_v_matrix(df, cat_cols):
     matrix = pd.DataFrame(
         np.zeros((len(cat_cols), len(cat_cols))),
         index=cat_cols,
-        columns=cat_cols
+        columns=cat_cols,
     )
     for col1 in cat_cols:
         for col2 in cat_cols:
@@ -147,22 +148,22 @@ def cramers_v_matrix(df, cat_cols):
 
 
 def plot_cramers_v_heatmap(
-    df, categorical_features, figsize=(6, 4), cmap='YlOrBr'
+    df, categorical_features, figsize=(6, 4), cmap="YlOrBr"
 ):
     """
     Plot Cramér's V heatmap for categorical columns.
     """
     cramers_matrix = cramers_v_matrix(df, categorical_features)
     plt.figure(figsize=figsize)
-    sns.heatmap(cramers_matrix, annot=True, cmap=cmap, fmt='.2f')
+    sns.heatmap(cramers_matrix, annot=True, cmap=cmap, fmt=".2f")
     plt.title("Cramér's V Correlation Between Categorical Features")
     plt.show()
 
 
 def detect_outliers(df, num_cols=None):
     """Boxplot for numeric outlier detection"""
     if num_cols is None:
-        num_cols = df.select_dtypes(include=['int64', 'float64']).columns
+        num_cols = df.select_dtypes(include=["int64", "float64"]).columns
 
     for col in num_cols:
         plt.figure(figsize=(8, 4))

src/saveFile.py

@@ -19,7 +19,7 @@ def convert_tz_aware_to_naive(df):
     """
     for col in df.columns:
         if pd.api.types.is_datetime64_any_dtype(df[col]):
-            if hasattr(df[col].dt, 'tz') and df[col].dt.tz is not None:
+            if hasattr(df[col].dt, "tz") and df[col].dt.tz is not None:
                 df[col] = df[col].dt.tz_localize(None)
     return df