from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA, AutoETS, DynamicOptimizedTheta
from statsforecast.utils import ConformalIntervals
import numpy as np
import polars as pl

# Polars option to display all rows
pl.Config.set_tbl_rows(None)

# Initialize the models
models = [
    AutoARIMA(season_length=12),
    AutoETS(season_length=12),
    DynamicOptimizedTheta(season_length=12)
]

# Initialize the StatsForecast model
sf = StatsForecast(models=models, freq='1w', n_jobs=-1)

# Perform cross-validation with a step size of 1 to mimic an expanding window
crossvalidation_df = sf.cross_validation(df=y_cl4_filtered, h=2, step_size=1, n_windows=8, sort_df=True)

# Calculate the ensemble mean
ensemble = crossvalidation_df[['AutoARIMA', 'AutoETS', 'DynamicOptimizedTheta']].mean(axis=1)

# Create a Series for the ensemble mean
ensemble_series = pl.Series('Ensemble', ensemble)

# Add the ensemble mean as a new column to the DataFrame
crossvalidation_df = crossvalidation_df.with_columns(ensemble_series)

def wmape(y_true, y_pred):
    return np.abs(y_true - y_pred).sum() / np.abs(y_true).sum()

# Calculate the WMAPE for the ensemble model
wmape_value = wmape(crossvalidation_df['y'], crossvalidation_df['Ensemble'])
print('Average WMAPE for Ensemble: ', round(wmape_value, 4))

# Calculate the errors for the ensemble model
errors = crossvalidation_df['y'] - crossvalidation_df['Ensemble']

# For an individual forecast
individual_accuracy = 1 - (abs(crossvalidation_df['y'] - crossvalidation_df['Ensemble']) / crossvalidation_df['y'])
individual_bias = (crossvalidation_df['Ensemble'] / crossvalidation_df['y']) - 1

# Add these calculations as new columns to DataFrame
crossvalidation_df = crossvalidation_df.with_columns([
    individual_accuracy.alias("individual_accuracy"),
    individual_bias.alias("individual_bias")
])

# Print the individual accuracy and bias for each week
for row in crossvalidation_df.to_dicts():
    id = row['unique_id']
    date = row['ds'] 
    accuracy = row['individual_accuracy']
    bias = row['individual_bias']
    print(f"{id}, {date}, Individual Accuracy: {accuracy:.4f}, Individual Bias: {bias:.4f}")

# For groups of forecasts
group_accuracy = 1 - (errors.abs().sum() / crossvalidation_df['y'].sum())
group_bias = (crossvalidation_df['Ensemble'].sum() / crossvalidation_df['y'].sum()) - 1

# Print the average group accuracy and group bias over all folds for the ensemble model
print('Average Group Accuracy: ', round(group_accuracy, 4))
print('Average Group Bias: ', round(group_bias, 4))


# Fit the models on the entire dataset
sf.fit(y_cl4_fit_1)

# Instantiate the ConformalIntervals class
prediction_intervals = ConformalIntervals()

# Generate 24 months forecasts
forecasts_df = sf.forecast(h=52*2, prediction_intervals=prediction_intervals, level=[95], id_col='unique_id', sort_df=True)

# Apply the non-negative constraint to the forecasts of individual models
forecasts_df = forecasts_df.with_columns([
    pl.when(pl.col('AutoARIMA') < 0).then(0).otherwise(pl.col('AutoARIMA')).alias('AutoARIMA'),
    pl.when(pl.col('AutoETS') < 0).then(0).otherwise(pl.col('AutoETS')).alias('AutoETS'),
    pl.when(pl.col('DynamicOptimizedTheta') < 0).then(0).otherwise(pl.col('DynamicOptimizedTheta')).alias('DynamicOptimizedTheta'),
])

# Calculate the ensemble forecast
ensemble_forecast = forecasts_df[['AutoARIMA', 'AutoETS', 'DynamicOptimizedTheta']].mean(axis=1)

# Calculate the lower and upper prediction intervals for the ensemble forecast
ensemble_lo_95 = forecasts_df.select(
    [
        pl.when(pl.col('AutoARIMA-lo-95') < 0).then(0).otherwise(pl.col('AutoARIMA-lo-95')).alias('AutoARIMA-lo-95'),
        pl.when(pl.col('AutoETS-lo-95') < 0).then(0).otherwise(pl.col('AutoETS-lo-95')).alias('AutoETS-lo-95'),
        pl.when(pl.col('DynamicOptimizedTheta-lo-95') < 0).then(0).otherwise(pl.col('DynamicOptimizedTheta-lo-95')).alias('DynamicOptimizedTheta-lo-95'),
   ]
).mean(axis=1)
ensemble_hi_95 = forecasts_df[['AutoARIMA-hi-95', 'AutoETS-hi-95', 'DynamicOptimizedTheta-hi-95']].mean(axis=1)

# Create Series for the ensemble forecast and its prediction intervals
ensemble_forecast_series = pl.Series('EnsembleForecast', ensemble_forecast)
ensemble_lo_95_series = pl.Series('Ensemble-lo-95', ensemble_lo_95)
ensemble_hi_95_series = pl.Series('Ensemble-hi-95', ensemble_hi_95)

# Add the ensemble forecast and its prediction intervals as new columns to the DataFrame
forecasts_df = forecasts_df.with_columns([ensemble_forecast_series, ensemble_lo_95_series, ensemble_hi_95_series])

# Round the ensemble forecast and prediction intervals and convert to integer
forecasts_df = forecasts_df.with_columns([
    pl.col("EnsembleForecast").round().cast(pl.Int32),
    pl.col("Ensemble-lo-95").round().cast(pl.Int32),
    pl.col("Ensemble-hi-95").round().cast(pl.Int32)
])

# Split the unique_id concat into the original columns
forecasts_df = (
    forecasts_df
    .with_columns(pl.col('unique_id').str.split('_').alias('id_list'))  
    .with_columns(pl.col('id_list').arr.get(0).alias('MaterialID'))  
    .with_columns(pl.col('id_list').arr.get(1).alias('SalesOrg'))  
    .with_columns(pl.col('id_list').arr.get(2).alias('DistrChan')) 
    .with_columns(pl.col('id_list').arr.get(3).alias('CL4'))  
    .drop('id_list')   
    .drop('unique_id')  
)

# Rename ‘ds’ to ‘WeekDate’
forecasts_df = forecasts_df.rename({'ds': 'WeekDate'})

# Reorder the columns
forecasts_df = forecasts_df.select([
    "MaterialID",
    "SalesOrg",
    "DistrChan", 
    "CL4",
    "WeekDate",
    "EnsembleForecast", 
    "Ensemble-lo-95", 
    "Ensemble-hi-95",
    "AutoARIMA", 
    "AutoARIMA-lo-95", 
    "AutoARIMA-hi-95", 
    "AutoETS", 
    "AutoETS-lo-95", 
    "AutoETS-hi-95", 
    "DynamicOptimizedTheta", 
    "DynamicOptimizedTheta-lo-95", 
    "DynamicOptimizedTheta-hi-95"
]) 

# Create an empty list
forecasts_list = []

# Append each row to the list
for row in forecasts_df.to_dicts():
    forecasts_list.append(row)

# Print the list
for forecast in forecasts_list:
    print(forecast) ---------------------------------------------------------------------------
SchemaError                               Traceback (most recent call last)
Cell In[14], line 117
    107 forecasts_df = forecasts_df.with_columns([
    108     pl.col("EnsembleForecast").round().cast(pl.Int32),
    109     pl.col("Ensemble-lo-95").round().cast(pl.Int32),
    110     pl.col("Ensemble-hi-95").round().cast(pl.Int32)
    111 ])
    113 # Split the unique_id concat into the original columns
    114 forecasts_df = (
    115     forecasts_df
    116     .with_columns(pl.col('unique_id').str.split('_').alias('id_list'))  
--> 117     .with_columns(pl.col('id_list').arr.get(0).alias('MaterialID'))  
    118     .with_columns(pl.col('id_list').arr.get(1).alias('SalesOrg'))  
    119     .with_columns(pl.col('id_list').arr.get(2).alias('DistrChan')) 
    120     .with_columns(pl.col('id_list').arr.get(3).alias('CL4'))  
    121     .drop('id_list')   
    122     .drop('unique_id')  
    123 )
    125 # Rename ‘ds’ to ‘WeekDate’
    126 forecasts_df = forecasts_df.rename({'ds': 'WeekDate'})

File ~/anaconda3/lib/python3.10/site-packages/polars/dataframe/frame.py:8315, in DataFrame.with_columns(self, *exprs, **named_exprs)
   8169 def with_columns(
   8170     self,
   8171     *exprs: IntoExpr | Iterable[IntoExpr],
   8172     **named_exprs: IntoExpr,
   8173 ) -> DataFrame:
   8174     """
   8175     Add columns to this DataFrame.
   8176 
   (...)
   8313     └─────┴──────┴─────────────┘
   8314     """
-> 8315     return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)

File ~/anaconda3/lib/python3.10/site-packages/polars/lazyframe/frame.py:1940, in LazyFrame.collect(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, no_optimization, streaming, background, _eager)
   1937 if background:
   1938     return InProcessQuery(ldf.collect_concurrently())
-> 1940 return wrap_df(ldf.collect())

SchemaError: invalid series dtype: expected `FixedSizeList`, got `list[str]`