Python Code Examples

import pandas as pd

# File path
ruta = "/content/drive/MyDrive/Prueba_tecnica/Datos3/OFEI1204.txt"

# List to store final records
data = []

# Variable to store current agent
agente_actual = None

with open(ruta, "r", encoding="latin-1") as file:
    for line in file:
        line = line.strip()

        # Detect agent lines
        if line.startswith("AGENTE:"):
            agente_actual = line.replace("AGENTE:", "").strip()

        # Process only lines containing ", D,"
        elif ", D," in line:
            partes = line.split(",")
            planta = partes[0].strip()
            tipo = partes[1].strip()

            # Extract 24 hours
            horas = [float(h.strip()) for h in partes[2:26]]

            # Build record
            registro = [agente_actual, tipo, planta] + horas

            data.append(registro)

# Create column names
columnas = ["Agente", "Tipo", "Planta"] + [f"Hora_{i}" for i in range(1, 25)]

# Create final DataFrame
df_final = pd.DataFrame(data, columns=columnas)

import pandas as pd

# 1. Load Master Data with requested columns
cols_master = [
    'Nombre visible Agente',
    'AGENTE (OFEI)',
    'CENTRAL (dDEC, dSEGDES, dPRU…)',
    'Tipo de central (Hidro, Termo, Filo, Menor)'
]

df_master = pd.read_excel('/path/to/Datos Maestros VF.xlsx', usecols=cols_master)

# 2. Filter by Agent (EMGESA) and Type (H or T)
agentes = ['EMGESA', 'EMGESA S.A.']
tipos = ['H', 'T']
df_m_filt = df_master[
    (df_master['AGENTE (OFEI)'].isin(agentes)) &
    (df_master['Tipo de central (Hidro, Termo, Filo, Menor)'].isin(tipos))
].copy()

# 3. Load the TXT file
nombres_columnas = ['CENTRAL'] + [f'H{i}' for i in range(1, 25)]

df_ddec = pd.read_csv(
    '/path/to/dDEC1204.TXT',
    header=None,
    names=nombres_columnas,
    quotechar='"',
    skipinitialspace=True,
    encoding='latin-1'
)

# 4. Clean text to ensure proper merge
df_m_filt['CENTRAL (dDEC, dSEGDES, dPRU…)'] = df_m_filt['CENTRAL (dDEC, dSEGDES, dPRU…)'].str.strip().str.upper()
df_ddec['CENTRAL'] = df_ddec['CENTRAL'].str.strip().str.upper()

# 5. Perform the Merge
df_merged = pd.merge(
    df_m_filt,
    df_ddec,
    left_on='CENTRAL (dDEC, dSEGDES, dPRU…)',
    right_on='CENTRAL'
)

# 6. Calculate Horizontal Sum (H1 to H24)
columnas_horas = [f'H{i}' for i in range(1, 25)]
df_merged['Suma_Total'] = df_merged[columnas_horas].sum(axis=1)

# 7. Select only plants with sum greater than zero
df_resultado = df_merged[df_merged['Suma_Total'] > 0].copy()

# Data cleaning - Numeric variables
numericas = X.select_dtypes(include=['int64','float64']).columns

for col in numericas:
    X.loc[:, col] = X[col].fillna(X[col].mean())  # Replace nulls with mean
    if col in X_test.columns:
        X_test.loc[:, col] = X_test[col].fillna(X[col].mean())

# Data cleaning - Categorical variables
categoricas = X.select_dtypes(include=['object']).columns

for col in categoricas:
    X.loc[:, col] = X[col].fillna("Desconocido")  # Replace nulls with "Desconocido"
    if col in X_test.columns:
        X_test.loc[:, col] = X_test[col].fillna("Desconocido")

# One-hot encoding
X = pd.get_dummies(X, drop_first=True)  # Convert categorical to binary
X_test = pd.get_dummies(X_test, drop_first=True)

X_test = X_test.reindex(columns=X.columns, fill_value=0)  # Align test columns

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score

# Split data into train and validation
X_train, X_val, y_train, y_val = train_test_split(
    X,  # Predictor variables
    y,  # Target variable
    test_size=0.2,  # 20% for validation
    random_state=42,  # Seed for reproducibility
    stratify=y  # Maintain class proportion
)

# Initialize Random Forest model
model = RandomForestClassifier(
    n_estimators=200,  # Number of trees
    random_state=42,
    class_weight='balanced'  # Adjust for class imbalance
)

# Train model
model.fit(X_train, y_train)

# Predictions on validation set
y_val_pred = model.predict(X_val)
y_val_proba = model.predict_proba(X_val)[:, 1]

print("CONFUSION MATRIX")
print(confusion_matrix(y_val, y_val_pred))

print("\nCLASSIFICATION REPORT")
print(classification_report(y_val, y_val_pred))

print("\nAUC-ROC:", roc_auc_score(y_val, y_val_proba))

# Train final model with all training data
model.fit(X, y)

# Threshold adjustment
threshold = 0.5
y_pred_test = (model.predict_proba(X_test)[:, 1] >= threshold).astype(int)

# Save final result
submission = pd.DataFrame({
    "id": ids_test,
    "FRAUDE": y_pred_test
})

submission.to_csv("/content/test_evaluado.csv", index=False)