update residual diagnostic plots and example notebook

docs/tutorials/pyro_basic.ipynb

@@ -296,7 +296,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -310,7 +310,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.7.7"
   },
   "toc": {
    "base_numbering": 1,

orbit/diagnostics/plot.py

@@ -8,15 +8,19 @@
 import math
 import os
 import pkg_resources
+import statsmodels.api as sm
+from scipy import stats
 
 from ..constants.constants import PredictionKeys
 from orbit.utils.general import is_empty_dataframe, is_ordered_datetime
 from ..constants.constants import BacktestFitKeys
 from ..constants.palette import PredictionPaletteClassic as PredPal
+from orbit.constants import palette
 from orbit.diagnostics.metrics import smape
 from orbit.utils.plot import orbit_style_decorator
-
 from ..exceptions import PlotException
+
+
 import logging
 
 logger = logging.getLogger("orbit")
@@ -715,3 +719,128 @@ def params_comparison_boxplot(
     plt.title(title)
 
     return ax
+
+
+@orbit_style_decorator
+def residual_diagnostic_plot(
+    df,
+    dist="norm",
+    date_col="week",
+    residual_col="residual",
+    fitted_col="prediction",
+    sparams=None,
+):
+    """
+    Parameters
+    ----------
+
+    df : pd.DataFrame
+    dist : str
+    date_col : str
+        column name of date
+    residual_col : str
+        column name of residual
+    fitted_col: str
+        column name of fitted value from model
+    sparams : float or list
+        extra parameters
+
+    Notes
+    -----
+    1. residual by time
+    2. residual vs fitted
+    3. residual histogram with vertical line as mean
+    4. residuals qq plot
+    5. residual ACF
+    6. residual PACF
+    """
+    fig, ax = plt.subplots(3, 2, figsize=(15, 12))
+
+    # plot 1 residual by time
+    sns.lineplot(
+        x=date_col,
+        y=residual_col,
+        data=df,
+        ax=ax[0, 0],
+        color=palette.OrbitPalette.BLUE.value,
+        alpha=0.8,
+        label="residual",
+    )
+    ax[0, 0].set_title("Residual by Time")
+    ax[0, 0].legend()
+
+    # plot 2 residual vs fitted
+    sns.scatterplot(
+        x=fitted_col,
+        y=residual_col,
+        data=df,
+        ax=ax[0, 1],
+        color=palette.OrbitPalette.BLUE.value,
+        alpha=0.8,
+        label="residual",
+    )
+    ax[0, 1].axhline(
+        y=0,
+        linestyle="--",
+        color=palette.OrbitPalette.BLACK.value,
+        alpha=0.5,
+        label="0",
+    )
+    ax[0, 1].set_title("Residual vs Fitted")
+    ax[0, 1].set_xlabel("fitted")
+    ax[0, 1].legend()
+
+    # plot 3 residual histogram with vertical line as mean
+    sns.distplot(
+        df[residual_col].values,
+        hist=True,
+        kde=True,
+        ax=ax[1, 0],
+        color=palette.OrbitPalette.BLUE.value,
+        label="residual",
+        hist_kws={
+            "edgecolor": "white",
+            "alpha": 0.5,
+            "facecolor": palette.OrbitPalette.BLUE.value,
+        },
+    )
+    ax[1, 0].set_title("Residual Distribution")
+    ax[1, 0].axvline(
+        df[residual_col].mean(),
+        color=palette.OrbitPalette.ORANGE.value,
+        linestyle="--",
+        alpha=0.9,
+        label="residual mean",
+    )
+    ax[1, 0].set_ylabel("density")
+    ax[1, 0].legend()
+
+    # plot 4 residual qq plot
+    if dist == "norm":
+        _ = stats.probplot(df[residual_col].values, dist="norm", plot=ax[1, 1])
+    elif dist == "t-dist":
+        # t-dist qq-plot
+        _ = stats.probplot(
+            df[residual_col].values, dist=stats.t, sparams=sparams, plot=ax[1, 1]
+        )
+
+    # plot 5 residual ACF
+    sm.graphics.tsa.plot_acf(
+        df[residual_col].values,
+        ax=ax[2, 0],
+        title="Residual ACF",
+        color=palette.OrbitPalette.BLUE.value,
+    )
+    ax[2, 0].set_xlabel("lag")
+    ax[2, 0].set_ylabel("acf")
+
+    # plot 6 residual PACF
+    sm.graphics.tsa.plot_pacf(
+        df[residual_col].values,
+        ax=ax[2, 1],
+        title="Residual PACF",
+        color=palette.OrbitPalette.BLUE.value,
+    )
+    ax[2, 1].set_xlabel("lag")
+    ax[2, 1].set_ylabel("pacf")
+    fig.tight_layout()

orbit/models/dlt.py

@@ -77,6 +77,8 @@ def DLT(
     global_floor : float
         Minimum value of global logistic trend. Default is set to 0.0. This value is used only when
         `global_trend_option` = 'logistic'
+    forecast_horizon : int
+        forecast_horizon will be used only when users want to specify optimization forecast horizon > 1
     estimator : string; {'stan-mcmc', 'stan-map'}
         default to be 'stan-mcmc'.
 
@@ -123,6 +125,7 @@ def DLT(
         global_floor=global_floor,
         forecast_horizon=forecast_horizon,
     )
+
     if estimator == EstimatorsKeys.StanMAP.value:
         dlt_forecaster = MAPForecaster(
             model=dlt, estimator_type=StanEstimatorMAP, **kwargs

orbit/template/dlt.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pandas as pd
 from scipy.stats import nct
-from statsmodels.api import OLS
 from copy import deepcopy
 import torch
 from enum import Enum
@@ -19,7 +18,6 @@
 from ..exceptions import IllegalArgument, ModelException, PredictionException
 from .ets import ETSModel
 from ..estimators.stan_estimator import StanEstimatorMCMC, StanEstimatorMAP
-from ..estimators.pyro_estimator import PyroEstimatorSVI
 
 
 class DataInputMapper(Enum):