Dev ktrx rebase (#453)

* Prediction Plot Bug Fix and Enhance (#428) * Update plot.py * fix bug and refine prediction plot * Update quick_start.ipynb * Update plot.py * Predicion refactor (#430) * resolve conflicts * resolve conflicts * rebase commit * Prediction Plot Bug Fix and Enhance (#428) * Update plot.py * fix bug and refine prediction plot * Update quick_start.ipynb * Update plot.py * rebase commit * Refine KTRLite plotting and tutorial (#431) * rebase from dev * temp * Update build_your_own_model.ipynb * refine tutorials and ktrlite knots plotting * Update ktrlite.ipynb * palette and docstring * more verbiage Co-authored-by: Zhishi Wang <zhishiw@uber.com> * Update build_your_own_model.ipynb * Update build_your_own_model.ipynb * Feat arviz (#433) * arviz * get_posterior_samples * arviz plotting * arviz demo * rename * fix kwargs * refine plotting * more robust way to extract signatures of orbit models (#438) * more robust way to extract signatures of orbit models * unit test for grid search * replace regressor beta prior tuning with other params * ktr lite house keeping (#440) * ktr lite house keeping - revert level knots plot - refine tutorial - refine some plotting code * more house keeping - [x] enhance unit test - [x] relable pool - [x] initializer of ktrlite * notebook update * Update test_ktrlite.py * Update test_ktrlite.py * add comment for stan_extract cleaning Co-authored-by: Zhishi Wang <zhishiw@uber.com> * ELBO Loss Extraction (#443) * refine estimator to include training metrics for NUTS and SVI * Update pyro_basic.ipynb * add comment for stan_extract cleaning Co-authored-by: Zhishi Wang <zhishiw@uber.com> Co-authored-by: Zhishi Wang <wangzhishi@users.noreply.github.com> Co-authored-by: Zhishi Wang <zhishiw@uber.com> * use newly written arviz wrappers for diagnostic tutorial (#446) * resolve conflicts * rebase * rebase with dev * Update template.py Co-authored-by: Zhishi Wang <zhishiw@uber.com> Co-authored-by: Zhishi Wang <wangzhishi@users.noreply.github.com>
uber · Jun 3, 2021 · c791d50 · c791d50
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diff --git a/docs/tutorials/build_your_own_model.ipynb b/docs/tutorials/build_your_own_model.ipynb
diff --git a/docs/tutorials/ktrlite.ipynb b/docs/tutorials/ktrlite.ipynb
diff --git a/docs/tutorials/lgt.ipynb b/docs/tutorials/lgt.ipynb
diff --git a/docs/tutorials/model_diagnostics.ipynb b/docs/tutorials/model_diagnostics.ipynb
diff --git a/docs/tutorials/pyro_basic.ipynb b/docs/tutorials/pyro_basic.ipynb
diff --git a/docs/tutorials/quick_start.ipynb b/docs/tutorials/quick_start.ipynb
diff --git a/examples/build_your_own_model.ipynb b/examples/build_your_own_model.ipynb
diff --git a/examples/ets_example.ipynb b/examples/ets_example.ipynb
diff --git a/examples/hyper_parameters_tuning.ipynb b/examples/hyper_parameters_tuning.ipynb
diff --git a/examples/ktrlite.ipynb b/examples/ktrlite.ipynb
diff --git a/examples/mcmc_diagnosttic.ipynb → examples/mcmc_diagnostic.ipynb b/examples/mcmc_diagnosttic.ipynb → examples/mcmc_diagnostic.ipynb
diff --git a/examples/mcmc_diagnostic_arviz.ipynb b/examples/mcmc_diagnostic_arviz.ipynb
diff --git a/orbit/constants/constants.py b/orbit/constants/constants.py
@@ -38,25 +38,14 @@ class EstimatorOptionsMapper(Enum):
     }
 
 
-# TODO: consider combining this with PredictedComponents
-class PredictionColumnNames(Enum):
-    """
-    In the output of SLGTModel.transform() and SLGT.predict(), the column names if
-    'return_decomposed_components' = True.
-    """
-    PREDICTED_RESPONSE = 'predicted'
-    LEVEL = 'level'
-    SEASONALITY = 'seasonality'
-    REGRESSOR = 'regressor'
-    ACTUAL_RESPONSE = 'actual'
-
-
-class PredictedComponents(Enum):
+class PredictionKeys(Enum):
     """ column names for the data frame of predicted result with decomposed components
     """
     TREND = 'trend'
     SEASONALITY = 'seasonality'
     REGRESSION = 'regression'
+    REGRESSOR = 'regressor'
+    PREDICTION = 'prediction'
 
 
 class PlotLabels(Enum):

diff --git a/orbit/constants/ktrlite.py b/orbit/constants/ktrlite.py
@@ -24,8 +24,7 @@ class DataInputMapper(Enum):
     KERNEL_COEFFICIENTS = 'K_COEF'
     NUM_OF_REGRESSORS = 'P'
     REGRESSOR_MATRIX = 'REGRESSORS'
-    COEFFICIENTS_KNOT_POOLING_LOC = 'COEF_KNOT_POOL_LOC'
-    COEFFICIENTS_KNOT_POOLING_SCALE = 'COEF_KNOT_POOL_SCALE'
+    COEFFICIENTS_INITIAL_KNOT_SCALE = 'COEF_INIT_KNOT_SCALE'
     COEFFICIENTS_KNOT_SCALE = 'COEF_KNOT_SCALE'
 
 

diff --git a/orbit/constants/palette.py b/orbit/constants/palette.py
@@ -12,7 +12,17 @@ class QualitativePalette(Enum):
     PostQ = ['#1fc600', '#ff4500']
     # large amount of stacking series
     Stack = ["#12939A", "#F15C17", "#DDB27C", "#88572C", "#FF991F", "#DA70BF", "#125C77",
-            "#4DC19C", "#776E57", "#17B8BE", "#F6D18A", "#B7885E", "#FFCB99", "#F89570",
-            "#829AE3", "#E79FD5", "#1E96BE", "#89DAC1", "#B3AD9E"]
+             "#4DC19C", "#776E57", "#17B8BE", "#F6D18A", "#B7885E", "#FFCB99", "#F89570",
+             "#829AE3", "#E79FD5", "#1E96BE", "#89DAC1", "#B3AD9E"]
     # bar plot
-    Bar5 = ["#ef476fff", "#ffd166ff", "#06d6a0ff", "#118ab2ff", "#073b4cff"]
+    Bar5 = ["#ef476fff", "#ffd166ff", "#06d6a0ff", "#118ab2ff", "#073b4cff"]
+
+
+class OrbitPalette(Enum):
+    Black = '#000000'
+    DarkGrey = '#333131'
+    SafetyBlue = '#276EF1'
+    LightBlue = '#5B91F5'
+    Green = '#05A357'
+    Orange = '#05A357'
+    Yellow = '#FFC043'
diff --git a/orbit/diagnostics/backtest.py b/orbit/diagnostics/backtest.py
@@ -4,15 +4,13 @@
 from copy import deepcopy
 import inspect
 import tqdm
+from itertools import product
 
 from .metrics import smape, wmape, mape, mse, mae, rmsse
 from ..exceptions import BacktestException
 from ..constants.constants import TimeSeriesSplitSchemeNames
 from ..constants.palette import QualitativePalette
-
-from itertools import product
 from collections.abc import Mapping, Iterable
-from orbit.diagnostics.metrics import smape, mape, wmape
 
 
 class TimeSeriesSplitter(object):
@@ -427,18 +425,38 @@ def grid_search_orbit(param_grid, model, df, min_train_len,
         data frame of tuning results
 
     """
-    def _get_params(model):
-        # get all the model params for orbit typed models
-        params = {}
-        for key, val in model.__dict__.items():
-            if not key.startswith('_') and key != 'estimator':
-                params[key] = val
+    # def _get_params(model):
+    #     # get all the model params for orbit typed models
+    #     params = {}
+    #     for key, val in model.__dict__.items():
+    #         if not key.startswith('_') and key != 'estimator':
+    #             params[key] = val
+
+    #     for key, val in model.__dict__['estimator'].__dict__.items():
+    #         if not key.startswith('_') and key != 'stan_init':
+    #             params[key] = val
 
-        for key, val in model.__dict__['estimator'].__dict__.items():
-            if not key.startswith('_') and key != 'stan_init':
-                params[key] = val
+    #     return params.copy()
 
-        return params.copy()
+    def _get_params(model):
+        init_args = dict()
+
+        # get all the parent classes and their signatures
+        for cls in inspect.getmro(model.__class__):
+            sig = inspect.signature(cls)
+            for key in sig.parameters.keys():
+                if key != 'kwargs':
+                    if hasattr(model, key):
+                        init_args[key] = getattr(model, key)
+        # deal with the estimator separately
+        for cls in inspect.getmro(model.estimator_type):
+            sig = inspect.signature(cls)
+            for key in sig.parameters.keys():
+                if key != 'kwargs':
+                    if hasattr(model.estimator, key):
+                        init_args[key] = getattr(model.estimator, key)
+
+        return init_args.copy()
 
     def _yield_param_grid(param_grid):
         # an internal function to mimic the ParameterGrid from scikit-learn