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ffdb9be | 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 | import pandas as pd
from src.features.geo_features import LondonPropertyGeoFeatures
# Optional:
# from src.features.feature_engineering import add_non_geo_features
# from src.features.nlp_features import PropertyTextEncoder
def is_numeric_and_true(value):
return isinstance(value, (int, float)) and bool(value)
def build_features(df: pd.DataFrame, geo_dir: str = "data/geo") -> pd.DataFrame:
"""
Compute all features used by the rent price model.
Combines geospatial, engineered, and NLP-derived features.
"""
# 1. Geospatial engineered features
geo = LondonPropertyGeoFeatures(geo_dir)
df = geo.add_features_to_df(df)
# 2. Other engineered features (optional)
# df = add_non_geo_features(df)
# 3. NLP / embeddings (optional)
# encoder = PropertyTextEncoder()
# df = encoder.add_nlp_embeddings(df, text_column="description")
df["deposit"] = df["deposit"].apply(is_numeric_and_true)
return df
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