mudaças pré formatação do pc

PREVISAO_VENDA_DDV/previsao.py

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+# file: forecast_ruptura_multioutput.py
+
+import pandas as pd
+import numpy as np
+from datetime import timedelta
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+import lightgbm as lgb
+
+# =========================
+# 1. Dataset fictício
+# =========================
+np.random.seed(42)
+datas = pd.date_range(start="2024-03-01", periods=400, freq="D")
+# vendas com tendência + sazonalidade semanal + ruído
+vendas = 50 + 5*np.sin(2*np.pi*datas.dayofyear/7) + np.random.poisson(5, len(datas))
+
+df = pd.DataFrame({"data": datas, "vendas": vendas})
+
+# =========================
+# 2. Feature Engineering
+# =========================
+def criar_features(df):
+    df["dia_semana"] = df["data"].dt.weekday
+    df["mes"] = df["data"].dt.month
+    df["lag_1"] = df["vendas"].shift(1)
+    df["lag_7"] = df["vendas"].shift(7)
+    df["mm_7"] = df["vendas"].rolling(7).mean().shift(1)
+    df["mm_30"] = df["vendas"].rolling(30).mean().shift(1)
+    return df
+
+df = criar_features(df)
+
+# =========================
+# 3. Criar targets multi-step (7 dias)
+# =========================
+horizon = 7
+for i in range(1, horizon+1):
+    df[f"target_d{i}"] = df["vendas"].shift(-i)
+
+df = df.dropna()
+
+# =========================
+# 4. Split treino/teste
+# =========================
+X = df[["dia_semana", "mes", "lag_1", "lag_7", "mm_7", "mm_30"]]
+y = df[[f"target_d{i}" for i in range(1, horizon+1)]]
+
+train_size = int(len(df) * 0.8)
+X_train, X_test = X.iloc[:train_size], X.iloc[train_size:]
+y_train, y_test = y.iloc[:train_size], y.iloc[train_size:]
+
+# =========================
+# 5. Treino modelo multi-output
+# =========================
+base_model = lgb.LGBMRegressor()
+model = MultiOutputRegressor(base_model)
+model.fit(X_train, y_train)
+
+# =========================
+# 6. Avaliar no teste
+# =========================
+y_pred = model.predict(X_test)
+
+# Calcular erro por horizonte
+for i in range(horizon):
+    mae = mean_absolute_error(y_test.iloc[:, i], y_pred[:, i])
+    rmse = mean_squared_error(y_test.iloc[:, i], y_pred[:, i], squared=False)
+    print(f"D+{i+1}: MAE={mae:.2f}, RMSE={rmse:.2f}")
+
+# =========================
+# 7. Exemplo de previsão real
+# =========================
+# Última linha do dataset como input
+X_last = X.iloc[[-1]]
+y_future = model.predict(X_last)[0]
+
+future_dates = [df["data"].iloc[-1] + timedelta(days=i) for i in range(1, horizon+1)]
+df_forecast = pd.DataFrame({"data": future_dates, "previsao_vendas": y_future})
+
+print("\nPrevisão para os próximos 7 dias:")
+print(df_forecast)