preprocessing.py

"""Data preprocessing and feature engineering"""

import pandas as pd
import numpy as np
from miner_specs import MINER_SPECS, ELECTRICITY_RATES
from fetch_blockchain_data import get_days_since_halving
from electricity_prices import get_electricity_rate


def engineer_features(blockchain_df):
    """Engineer features from blockchain data - keep it simple"""
    
    df = blockchain_df.copy().sort_values('date').reset_index(drop=True)
    df['date'] = pd.to_datetime(df['date'])
    
    # Just return as is, we'll select features later
    return df


def prepare_miner_features(
    blockchain_df,
    miner_name,
    miner_price,
    region="texas",
    machine_hashrate=None,
    power=None,
    efficiency=None,
    electricity_rate=None,
):
    """
    Add miner-specific features - EXACTLY 14 features.

    Now uses user-specified:
    - machine_price
    - machine_hashrate
    - power
    - efficiency
    - electricity_rate

    If any of these are None, we fall back to MINER_SPECS / region,
    but for your app you will always pass explicit values.
    """
    df = blockchain_df.copy()
    specs = MINER_SPECS[miner_name]

    # Keep only these columns from blockchain data
    df = df[[
        "date",
        "bitcoin_price",
        "difficulty",
        "fees",
        "hashrate",
        "revenue",
        "block_reward",
    ]].copy()
    df["date"] = pd.to_datetime(df["date"])

    # ---- user-provided constants (same value for all 30 days) ----
    df["machine_price"] = float(miner_price)

    if machine_hashrate is not None:
        df["machine_hashrate"] = float(machine_hashrate)
    else:
        df["machine_hashrate"] = specs["hashrate"]

    if power is not None:
        df["power"] = float(power)
    else:
        df["power"] = specs["power"]

    if efficiency is not None:
        df["efficiency"] = float(efficiency)
    else:
        df["efficiency"] = specs["efficiency"]

    # Calculate age_days (days since miner was released)
    release_date = pd.to_datetime(specs["release_date"])
    df["age_days"] = (df["date"] - release_date).dt.days

    # Days since halving
    df["days_since_halving"] = df["date"].apply(get_days_since_halving)

    # Revenue potential (same as your original code)
    hashrate_hs = df["machine_hashrate"] * 1e12
    btc_per_day = (
        (hashrate_hs * 86400)
        / (df["difficulty"] * (2**32))
        * (df["block_reward"] + (df["fees"] / 144))
    )
    df["Revenue_Potential"] = btc_per_day * df["bitcoin_price"]

    # ---- electricity_rate constant across all rows ----
    if electricity_rate is not None:
        df["electricity_rate"] = float(electricity_rate)
    else:
        df["electricity_rate"] = specs["electricity_rate"]
        
        # # fallback: keep old behaviour if not provided
        # df["electricity_rate"] = df["date"].dt.date.apply(
        #     lambda day: get_electricity_rate(region, day)
        # )

    return df



def get_latest_sequence(
    blockchain_df,
    miner_name,
    miner_price,
    region="texas",
    window_size=30,
    machine_hashrate=None,
    power=None,
    efficiency=None,
    electricity_rate=None,
):
    """
    Get the most recent sequence for prediction - EXACTLY 14 features in CORRECT ORDER.

    Now also accepts user-specified:
    - machine_hashrate
    - power
    - efficiency
    - electricity_rate
    """
    df_features = engineer_features(blockchain_df)
    df_miner = prepare_miner_features(
        df_features,
        miner_name,
        miner_price,
        region,
        machine_hashrate=machine_hashrate,
        power=power,
        efficiency=efficiency,
        electricity_rate=electricity_rate,
    )

    # CRITICAL: This order MUST match your training data CSV exactly!
    feature_cols = [
        "bitcoin_price",       # 1
        "difficulty",          # 2
        "fees",                # 3
        "hashrate",            # 4
        "revenue",             # 5
        "machine_price",       # 6
        "machine_hashrate",    # 7
        "power",               # 8
        "efficiency",          # 9
        "block_reward",        # 10
        "age_days",            # 11
        "days_since_halving",  # 12
        "Revenue_Potential",   # 13
        "electricity_rate",    # 14
    ]

    df_miner = df_miner.dropna().reset_index(drop=True)

    if len(df_miner) < window_size:
        raise ValueError(
            f"Not enough data to build a {window_size}-day window, got {len(df_miner)} rows."
        )

    df_window = df_miner.tail(window_size).reset_index(drop=True)
    sequence = df_window[feature_cols].values.astype(float)
    pred_date = df_window["date"].iloc[-1]

    return sequence, df_window, pred_date




if __name__ == "__main__":
    from fetch_blockchain_data import get_latest_blockchain_data
    
    print("\n" + "="*80)
    print("TESTING PREPROCESSING PIPELINE")
    print("="*80 + "\n")
    
    # Fetch blockchain data
    print("📡 Fetching blockchain data...")
    blockchain_df = get_latest_blockchain_data(days=90)
    
    if blockchain_df is None:
        print("❌ Failed to fetch blockchain data")
        exit(1)
    
    print(f"✅ Fetched {len(blockchain_df)} days of data\n")
    
    # Test configuration
    miner_name = 's19pro'
    miner_price = 2500
    region = 'texas'
    window_size = 30
    
    print("⚙️  Test Configuration:")
    print(f"   Miner: {MINER_SPECS[miner_name]['full_name']}")
    print(f"   Price: ${miner_price:,}")
    print(f"   Region: {region.title()}")
    print(f"   Window: {window_size} days")
    print(f"   Electricity: ${ELECTRICITY_RATES[region]}/kWh\n")
    
    # Step 1: Engineer features
    print("="*80)
    print("STEP 1: ENGINEER FEATURES")
    print("="*80)
    df_engineered = engineer_features(blockchain_df)
    print(f"✅ Engineered features")
    print(f"   Shape: {df_engineered.shape}")
    print(f"   Columns: {list(df_engineered.columns)}\n")
    print("First 3 rows:")
    print(df_engineered.head(3))
    print("\nLast 3 rows:")
    print(df_engineered.tail(3))
    
    # Step 2: Prepare miner features
    print("\n" + "="*80)
    print("STEP 2: PREPARE MINER FEATURES")
    print("="*80)
    df_miner = prepare_miner_features(df_engineered, miner_name, miner_price, region)
    print(f"✅ Added miner-specific features")
    print(f"   Shape: {df_miner.shape}")
    print(f"   Columns: {list(df_miner.columns)}\n")
    
    print("Miner-specific values (constant across all days):")
    print(f"   machine_hashrate: {df_miner['machine_hashrate'].iloc[0]} TH/s")
    print(f"   power: {df_miner['power'].iloc[0]} W")
    print(f"   efficiency: {df_miner['efficiency'].iloc[0]} W/TH")
    print(f"   machine_price: ${df_miner['machine_price'].iloc[0]:,.2f}")
    print(f"   electricity_rate: ${df_miner['electricity_rate'].iloc[0]:.4f}/kWh")
    
    print("\nDynamic values (change over time):")
    print(f"   age_days: {df_miner['age_days'].iloc[0]} → {df_miner['age_days'].iloc[-1]} days")
    print(f"   days_since_halving: {df_miner['days_since_halving'].iloc[0]} → {df_miner['days_since_halving'].iloc[-1]} days")
    print(f"   Revenue_Potential: ${df_miner['Revenue_Potential'].iloc[0]:.2f} → ${df_miner['Revenue_Potential'].iloc[-1]:.2f}/day")
    
    print("\nFirst 3 rows:")
    print(df_miner.head(3))
    print("\nLast 3 rows:")
    print(df_miner.tail(3))
    
    # Step 3: Get latest sequence
    print("\n" + "="*80)
    print("STEP 3: GET LATEST SEQUENCE")
    print("="*80)
    sequence, feature_cols, latest_date = get_latest_sequence(blockchain_df, miner_name, miner_price, region, window_size)
    
    print(f"✅ Created sequence for model")
    print(f"   Shape: {sequence.shape}")
    print(f"   Expected: ({window_size}, 14)")
    print(f"   Latest date: {latest_date.strftime('%Y-%m-%d')}\n")
    
    print("14 Features (in order):")
    for i, col in enumerate(feature_cols, 1):
        print(f"   {i:2d}. {col:25s} → First: {sequence[0, i-1]:>15.2f}  Last: {sequence[-1, i-1]:>15.2f}")
    
    print("\n" + "="*80)
    print("SEQUENCE STATISTICS")
    print("="*80)
    print("\nFirst day in sequence:")
    for i, col in enumerate(feature_cols):
        print(f"   {col:25s} = {sequence[0, i]:>15.2f}")
    
    print(f"\nLast day in sequence (for prediction on {latest_date.strftime('%Y-%m-%d')}):")
    for i, col in enumerate(feature_cols):
        print(f"   {col:25s} = {sequence[-1, i]:>15.2f}")
    
    # Show some statistics
    print("\n" + "="*80)
    print("FEATURE RANGES")
    print("="*80)
    for i, col in enumerate(feature_cols):
        min_val = sequence[:, i].min()
        max_val = sequence[:, i].max()
        mean_val = sequence[:, i].mean()
        print(f"{col:25s} → Min: {min_val:>12.2f}  Max: {max_val:>12.2f}  Mean: {mean_val:>12.2f}")
    
    print("\n" + "="*80)
    print("✅ PREPROCESSING PIPELINE TEST COMPLETE")
    print("="*80 + "\n")