Spaces:
Running
Running
File size: 7,733 Bytes
226ac39 227cb22 226ac39 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 |
"""
Enhanced Feature Engineering - Additional robust features
"""
import polars as pl
import numpy as np
from typing import Dict, Any, List, Optional
from pathlib import Path
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from ..utils.polars_helpers import load_dataframe, save_dataframe, get_numeric_columns
from ..utils.validation import validate_file_exists, validate_dataframe
def create_ratio_features(file_path: str,
columns: Optional[List[str]] = None,
max_ratios: int = 20,
output_path: Optional[str] = None) -> Dict[str, Any]:
"""
Create ratio features (a/b) for all numeric column pairs.
ROBUST: Handles division by zero, infinity, and NaN values.
Args:
file_path: Path to dataset
columns: Columns to use (None = all numeric)
max_ratios: Maximum number of ratio features
output_path: Output file path
Returns:
Dictionary with results
"""
validate_file_exists(file_path)
df = load_dataframe(file_path)
validate_dataframe(df)
if columns is None:
columns = get_numeric_columns(df)
print(f"🔢 Creating ratio features from {len(columns)} columns...")
ratio_exprs = []
feature_names = []
for i, col1 in enumerate(columns[:15]):
for col2 in columns[i+1:16]:
if len(ratio_exprs) >= max_ratios:
break
# Safe division (avoid div by zero, replace inf/nan)
ratio_name = f"ratio_{col1}_div_{col2}"
ratio_expr = (
pl.when(pl.col(col2).abs() < 1e-10)
.then(0)
.otherwise(pl.col(col1) / pl.col(col2))
.clip(-1e6, 1e6) # Clip extreme values
.fill_nan(0)
.fill_null(0)
.alias(ratio_name)
)
ratio_exprs.append(ratio_expr)
feature_names.append(ratio_name)
df = df.with_columns(ratio_exprs)
if output_path:
save_dataframe(df, output_path)
return {
'success': True,
'tool': 'create_ratio_features',
'result': {
'new_features': len(ratio_exprs),
'feature_names': feature_names,
'output_path': output_path
}
}
def create_statistical_features(file_path: str,
columns: Optional[List[str]] = None,
output_path: Optional[str] = None) -> Dict[str, Any]:
"""
Create row-wise statistical features (mean, std, min, max, range).
ROBUST: Handles missing values and edge cases.
Args:
file_path: Path to dataset
columns: Columns to use (None = all numeric)
output_path: Output file path
Returns:
Dictionary with results
"""
validate_file_exists(file_path)
df = load_dataframe(file_path)
validate_dataframe(df)
if columns is None:
columns = get_numeric_columns(df)
print(f"📊 Creating statistical features across {len(columns)} columns...")
# Row-wise statistics
stat_features = [
pl.concat_list([pl.col(c) for c in columns]).list.mean().fill_null(0).alias('row_mean'),
pl.concat_list([pl.col(c) for c in columns]).list.std().fill_null(0).alias('row_std'),
pl.concat_list([pl.col(c) for c in columns]).list.min().fill_null(0).alias('row_min'),
pl.concat_list([pl.col(c) for c in columns]).list.max().fill_null(0).alias('row_max'),
(pl.concat_list([pl.col(c) for c in columns]).list.max() -
pl.concat_list([pl.col(c) for c in columns]).list.min()).fill_null(0).alias('row_range'),
pl.concat_list([pl.col(c) for c in columns]).list.sum().fill_null(0).alias('row_sum'),
]
df = df.with_columns(stat_features)
if output_path:
save_dataframe(df, output_path)
return {
'success': True,
'tool': 'create_statistical_features',
'result': {
'new_features': 6,
'feature_names': ['row_mean', 'row_std', 'row_min', 'row_max', 'row_range', 'row_sum'],
'output_path': output_path
}
}
def create_log_features(file_path: str,
columns: Optional[List[str]] = None,
output_path: Optional[str] = None) -> Dict[str, Any]:
"""
Create log-transformed features for skewed distributions.
ROBUST: Handles negative values and zeros.
Args:
file_path: Path to dataset
columns: Columns to use (None = all numeric with positive values)
output_path: Output file path
Returns:
Dictionary with results
"""
validate_file_exists(file_path)
df = load_dataframe(file_path)
validate_dataframe(df)
if columns is None:
columns = get_numeric_columns(df)
print(f"📈 Creating log-transformed features for {len(columns)} columns...")
log_exprs = []
feature_names = []
for col in columns:
# Check if column has positive values
min_val = df[col].min()
if min_val is not None and min_val > 0:
# log(x)
log_exprs.append(pl.col(col).log().fill_nan(0).alias(f"log_{col}"))
feature_names.append(f"log_{col}")
elif min_val is not None and min_val >= 0:
# log(x+1) for non-negative values
log_exprs.append((pl.col(col) + 1).log().fill_nan(0).alias(f"log1p_{col}"))
feature_names.append(f"log1p_{col}")
if log_exprs:
df = df.with_columns(log_exprs)
if output_path:
save_dataframe(df, output_path)
return {
'success': True,
'tool': 'create_log_features',
'result': {
'new_features': len(log_exprs),
'feature_names': feature_names,
'output_path': output_path
}
}
def create_binned_features(file_path: str,
columns: Optional[List[str]] = None,
n_bins: int = 5,
output_path: Optional[str] = None) -> Dict[str, Any]:
"""
Create binned (discretized) features from continuous variables.
ROBUST: Uses quantile-based binning to handle outliers.
Args:
file_path: Path to dataset
columns: Columns to use (None = all numeric)
n_bins: Number of bins
output_path: Output file path
Returns:
Dictionary with results
"""
validate_file_exists(file_path)
df = load_dataframe(file_path)
validate_dataframe(df)
if columns is None:
columns = get_numeric_columns(df)[:10] # Limit to 10 columns
print(f"🗂️ Creating binned features for {len(columns)} columns with {n_bins} bins...")
binned_exprs = []
feature_names = []
for col in columns:
# Quantile-based binning
bin_name = f"{col}_binned"
binned_exprs.append(
pl.col(col).qcut(n_bins, labels=[f"bin_{i}" for i in range(n_bins)],
allow_duplicates=True).fill_null("bin_0").alias(bin_name)
)
feature_names.append(bin_name)
df = df.with_columns(binned_exprs)
if output_path:
save_dataframe(df, output_path)
return {
'success': True,
'tool': 'create_binned_features',
'result': {
'new_features': len(binned_exprs),
'feature_names': feature_names,
'n_bins': n_bins,
'output_path': output_path
}
}
|