Feat pickle fix (#342)

* first pickle fix attempt

* better attempt by using initializer

creating an initializer class to tackle problem of #340 fixed #40

* ETS Initializer

add ets initializer and change some wording from stan to generic

* change wording from stan to generic [minor]

minor wording

orbit/constants/constants.py

@@ -28,8 +28,8 @@ class EstimatorOptionsMapper(Enum):
     set of options)
     """
     ENGINE_TO_SAMPLE = {
-        'stan': ['map','vi','mcmc'],
-        'pyro': ['map','vi']
+        'stan': ['map', 'vi', 'mcmc'],
+        'pyro': ['map', 'vi']
     }
     SAMPLE_TO_PREDICT = {
         'map': ['map'],

orbit/constants/dlt.py

@@ -53,7 +53,7 @@ class GlobalTrendOption(Enum):
 
 class BaseSamplingParameters(Enum):
     """
-    The stan output sampling parameters related with DLT base model.
+    base parameters in posteriors sampling
     """
     # ---------- Common Local Trend ---------- #
     LOCAL_TREND_LEVELS = 'l'
@@ -75,19 +75,29 @@ class GlobalTrendSamplingParameters(Enum):
 
 class SeasonalitySamplingParameters(Enum):
     """
-    The stan output sampling parameters related with seasonality component.
+    seasonality component related parameters in posteriors sampling
     """
     SEASONALITY_LEVELS = 's'
     SEASONALITY_SMOOTHING_FACTOR = 'sea_sm'
 
 
 class RegressionSamplingParameters(Enum):
     """
-    The stan output sampling parameters related with regression component.
+    regression component related parameters in posteriors sampling
     """
     REGRESSION_COEFFICIENTS = 'beta'
 
 
+class LatentSamplingParameters(Enum):
+    """
+    latent variables to be sampled
+    """
+    REGRESSION_POSITIVE_COEFFICIENTS = 'pr_beta'
+    REGRESSION_NEGATIVE_COEFFICIENTS = 'nr_beta'
+    REGRESSION_REGULAR_COEFFICIENTS = 'rr_beta'
+    INITIAL_SEASONALITY = 'init_sea'
+
+
 class RegressionPenalty(Enum):
     fixed_ridge = 0
     lasso = 1

orbit/constants/ets.py

@@ -3,9 +3,8 @@
 
 class DataInputMapper(Enum):
     """
-    mapping from object input to stan file
+    mapping from object input to sampler
     """
-    # All of the following have default defined in DEFAULT_SLGT_FIT_ATTRIBUTES
     # ----------  Data Input ---------- #
     # observation related
     _NUM_OF_OBSERVATIONS = 'NUM_OF_OBS'
@@ -21,7 +20,7 @@ class DataInputMapper(Enum):
 
 class BaseSamplingParameters(Enum):
     """
-    The stan output sampling parameters related with LGT base model.
+    base parameters in posteriors sampling
     """
     # ---------- Common Local Trend ---------- #
     LOCAL_TREND_LEVELS = 'l'
@@ -32,7 +31,14 @@ class BaseSamplingParameters(Enum):
 
 class SeasonalitySamplingParameters(Enum):
     """
-    The stan output sampling parameters related with seasonality component.
+    seasonality component related parameters in posteriors sampling
     """
     SEASONALITY_LEVELS = 's'
     SEASONALITY_SMOOTHING_FACTOR = 'sea_sm'
+
+
+class LatentSamplingParameters(Enum):
+    """
+    latent variables to be sampled
+    """
+    INITIAL_SEASONALITY = 'init_sea'

orbit/constants/lgt.py

@@ -41,7 +41,7 @@ class DataInputMapper(Enum):
 
 class BaseSamplingParameters(Enum):
     """
-    The stan output sampling parameters related with LGT base model.
+    base parameters in posteriors sampling
     """
     # ---------- Common Local Trend ---------- #
     LOCAL_TREND_LEVELS = 'l'
@@ -60,19 +60,29 @@ class BaseSamplingParameters(Enum):
 
 class SeasonalitySamplingParameters(Enum):
     """
-    The stan output sampling parameters related with seasonality component.
+    seasonality component related parameters in posteriors sampling
     """
     SEASONALITY_LEVELS = 's'
     SEASONALITY_SMOOTHING_FACTOR = 'sea_sm'
 
 
 class RegressionSamplingParameters(Enum):
     """
-    The stan output sampling parameters related with regression component.
+    regression component related parameters in posteriors sampling
     """
     REGRESSION_COEFFICIENTS = 'beta'
 
 
+class LatentSamplingParameters(Enum):
+    """
+    latent variables to be sampled
+    """
+    REGRESSION_POSITIVE_COEFFICIENTS = 'pr_beta'
+    REGRESSION_NEGATIVE_COEFFICIENTS = 'nr_beta'
+    REGRESSION_REGULAR_COEFFICIENTS = 'rr_beta'
+    INITIAL_SEASONALITY = 'init_sea'
+
+
 class RegressionPenalty(Enum):
     fixed_ridge = 0
     lasso = 1

orbit/diagnostics/plot.py

@@ -49,7 +49,7 @@ def plot_predicted_data(training_actual_df, predicted_df, date_col, actual_col,
         figsize pass through to `matplotlib.pyplot.figure()`
     path: str
         path to save the figure
-    fontzise: int
+    fontsize: int
         fontsize of the title
     Returns
     -------
@@ -146,7 +146,7 @@ def plot_predicted_components(predicted_df, date_col, prediction_percentiles=Non
         figsize pass through to `matplotlib.pyplot.figure()`
     path: str; optional
         path to save the figure
-    fontzise: int; optional
+    fontsize: int; optional
         fontsize of the title
     is_visible: boolean
         whether we want to show the plot. If called from unittest, is_visible might = False.

orbit/estimators/base_estimator.py

@@ -1,4 +1,4 @@
-from abc import ABCMeta, abstractmethod
+from abc import abstractmethod
 import numpy as np
 
 
@@ -10,7 +10,7 @@ class BaseEstimator(object):
     seed : int
         seed number for initial random values
     verbose : bool
-        If True, output all stan diagnostics messages
+        If True, output all diagnostics messages from estimators
 
     """
     def __init__(self, seed=8888, verbose=False):
@@ -27,11 +27,11 @@ def fit(self, model_name, model_param_names, data_input, init_values=None):
         Parameters
         ----------
         model_name : str
-            name of stan model
+            name of model - used in mapping the right sampling file (stan/pyro/...)
         model_param_names : list
             list of strings of model parameters names to extract
         data_input : dict
-            key-value pairs of data input as required by definition in stan model
+            key-value pairs of data input as required by definition in samplers (stan/pyro/...)
         init_values : float or np.array
             initial sampler value. If None, 'random' is used
 

orbit/estimators/pyro_estimator.py

@@ -111,10 +111,10 @@ def fit(self, model_name, model_param_names, data_input, init_values=None):
         # make sure that model param names are a subset of stan extract keys
         invalid_model_param = set(model_param_names) - set(list(extract.keys()))
         if invalid_model_param:
-            raise EstimatorException("Stan model definition does not contain required parameters")
+            raise EstimatorException("Pyro model definition does not contain required parameters")
 
         # `stan.optimizing` automatically returns all defined parameters
-        # filter out unecessary keys
+        # filter out unnecessary keys
         extract = {param: extract[param] for param in model_param_names}
 
         return extract
@@ -165,10 +165,10 @@ def fit(self, model_name, model_param_names, data_input, init_values=None):
         # make sure that model param names are a subset of stan extract keys
         invalid_model_param = set(model_param_names) - set(list(extract.keys()))
         if invalid_model_param:
-            raise EstimatorException("Stan model definition does not contain required parameters")
+            raise EstimatorException("Pyro model definition does not contain required parameters")
 
         # `stan.optimizing` automatically returns all defined parameters
-        # filter out unecessary keys
+        # filter out unnecessary keys
         extract = {param: extract[param] for param in model_param_names}
 
         return extract

orbit/initializer/dlt.py

@@ -0,0 +1,33 @@
+import numpy as np
+from ..constants import dlt as constants
+
+
+class DLTInitializer(object):
+    def __init__(self, s, n_pr, n_nr, n_rr):
+        self.s = s
+        self.n_pr = n_pr
+        self.n_nr = n_nr
+        self.n_rr = n_rr
+
+    def __call__(self):
+        init_values = dict()
+        if self.s > 1:
+            init_sea = np.random.normal(loc=0, scale=0.05, size=self.s  - 1)
+            # catch cases with extreme values
+            init_sea[init_sea > 1.0] = 1.0
+            init_sea[init_sea < -1.0] = -1.0
+            init_values[constants.LatentSamplingParameters.INITIAL_SEASONALITY.value] = init_sea
+        if self.n_pr > 0:
+            x = np.random.normal(loc=0, scale=0.1, size=self.n_pr)
+            x[x < 0] = -1 * x[x < 0]
+            init_values[constants.LatentSamplingParameters.REGRESSION_POSITIVE_COEFFICIENTS.value] = \
+                x
+        if self.n_nr > 0:
+            x = np.random.normal(loc=-0, scale=0.1, size=self.n_nr)
+            x[x > 0] = -1 * x[x > 0]
+            init_values[constants.LatentSamplingParameters.REGRESSION_NEGATIVE_COEFFICIENTS.value] = \
+                x
+        if self.n_rr > 0:
+            init_values[constants.LatentSamplingParameters.REGRESSION_REGULAR_COEFFICIENTS.value] = \
+                np.random.normal(loc=-0, scale=0.1, size=self.n_rr)
+        return init_values

orbit/initializer/ets.py

@@ -0,0 +1,16 @@
+import numpy as np
+from ..constants import ets as constants
+
+
+class ETSInitializer(object):
+    def __init__(self, s):
+        self.s = s
+
+    def __call__(self):
+        init_values = dict()
+        init_sea = np.random.normal(loc=0, scale=0.05, size=self.s - 1)
+        # catch cases with extreme values
+        init_sea[init_sea > 1.0] = 1.0
+        init_sea[init_sea < -1.0] = -1.0
+        init_values[constants.LatentSamplingParameters.INITIAL_SEASONALITY.value] = init_sea
+        return init_values

orbit/initializer/lgt.py

@@ -0,0 +1,33 @@
+import numpy as np
+from ..constants import lgt as constants
+
+
+class LGTInitializer(object):
+    def __init__(self, s, n_pr, n_nr, n_rr):
+        self.s = s
+        self.n_pr = n_pr
+        self.n_nr = n_nr
+        self.n_rr = n_rr
+
+    def __call__(self):
+        init_values = dict()
+        if self.s > 1:
+            init_sea = np.random.normal(loc=0, scale=0.05, size=self.s  - 1)
+            # catch cases with extreme values
+            init_sea[init_sea > 1.0] = 1.0
+            init_sea[init_sea < -1.0] = -1.0
+            init_values[constants.LatentSamplingParameters.INITIAL_SEASONALITY.value] = init_sea
+        if self.n_pr > 0:
+            x = np.random.normal(loc=0, scale=0.1, size=self.n_pr)
+            x[x < 0] = -1 * x[x < 0]
+            init_values[constants.LatentSamplingParameters.REGRESSION_POSITIVE_COEFFICIENTS.value] = \
+                x
+        if self.n_nr > 0:
+            x = np.random.normal(loc=-0, scale=0.1, size=self.n_nr)
+            x[x > 0] = -1 * x[x > 0]
+            init_values[constants.LatentSamplingParameters.REGRESSION_NEGATIVE_COEFFICIENTS.value] = \
+                x
+        if self.n_rr > 0:
+            init_values[constants.LatentSamplingParameters.REGRESSION_REGULAR_COEFFICIENTS.value] = \
+                np.random.normal(loc=-0, scale=0.1, size=self.n_rr)
+        return init_values

orbit/models/dlt.py

@@ -16,6 +16,7 @@
 from ..models.ets import BaseETS, ETSMAP, ETSFull, ETSAggregated
 from ..estimators.stan_estimator import StanEstimatorMCMC, StanEstimatorVI, StanEstimatorMAP
 from ..exceptions import IllegalArgument, ModelException, PredictionException
+from ..initializer.dlt import DLTInitializer
 from ..utils.general import is_ordered_datetime
 
 
@@ -145,39 +146,17 @@ def _set_init_values(self):
         See: https://pystan.readthedocs.io/en/latest/api.htm
         Overriding :func: `~orbit.models.BaseETS._set_init_values`
         """
-        def init_values_function(s, n_pr, n_nr, n_rr):
-            init_values = dict()
-            if s > 1:
-                init_sea = np.random.normal(loc=0, scale=0.05, size=s - 1)
-                # catch cases with extreme values
-                init_sea[init_sea > 1.0] = 1.0
-                init_sea[init_sea < -1.0] = -1.0
-                init_values['init_sea'] = init_sea
-            if n_pr > 0:
-                x = np.random.normal(loc=0, scale=0.1, size=n_pr)
-                x[x < 0] = -1 * x[x < 0]
-                init_values['pr_beta'] = x
-            if n_nr > 0:
-                x = np.random.normal(loc=-0, scale=0.1, size=n_nr)
-                x[x > 0] = -1 * x[x > 0]
-                init_values['nr_beta'] = x
-            if n_rr > 0:
-                init_values['rr_beta'] = np.random.normal(loc=-0, scale=0.1, size=n_rr)
-            return init_values
-
-        seasonality = self._seasonality
-
         # init_values_partial = partial(init_values_callable, seasonality=seasonality)
         # partialfunc does not work when passed to PyStan because PyStan uses
         # inspect.getargspec(func) which seems to raise an exception with keyword-only args
         # caused by using partialfunc
-        # lambda as an alternative workaround
-        if seasonality > 1 or self._num_of_regressors > 0:
-            init_values_callable = lambda: init_values_function(
-                seasonality,
-                self._num_of_positive_regressors,
-                self._num_of_negative_regressors,
-                self._num_of_regular_regressors)
+        # lambda does not work in serialization in pickle
+        # callable object as an alternative workaround
+        if self._seasonality > 1 or self._num_of_regressors > 0:
+            init_values_callable = DLTInitializer(
+                self._seasonality, self._num_of_positive_regressors, self._num_of_negative_regressors,
+                self._num_of_regular_regressors
+            )
             self._init_values = init_values_callable
 
     def _set_additional_trend_attributes(self):

orbit/models/ets.py

@@ -10,6 +10,7 @@
 from ..estimators.stan_estimator import StanEstimatorMCMC, StanEstimatorVI, StanEstimatorMAP
 from ..estimators.pyro_estimator import PyroEstimatorVI, PyroEstimatorMAP
 from ..exceptions import IllegalArgument, ModelException, PredictionException
+from ..initializer.ets import ETSInitializer
 from .base_model import BaseModel
 from ..utils.general import is_ordered_datetime
 
@@ -113,26 +114,16 @@ def _set_init_values(self):
         """Set init as a callable (for Stan ONLY)
         See: https://pystan.readthedocs.io/en/latest/api.htm
         """
-        def init_values_function(s):
-            init_values = dict()
-            if s > 1:
-                init_sea = np.random.normal(loc=0, scale=0.05, size=s-1)
-                # catch cases with extreme values
-                init_sea[init_sea > 1.0] = 1.0
-                init_sea[init_sea < -1.0] = -1.0
-                init_values['init_sea'] = init_sea
-
-            return init_values
-
-        seasonality = self._seasonality
-
         # init_values_partial = partial(init_values_callable, seasonality=seasonality)
         # partialfunc does not work when passed to PyStan because PyStan uses
         # inspect.getargspec(func) which seems to raise an exception with keyword-only args
         # caused by using partialfunc
-        # lambda as an alternative workaround
-        if seasonality > 1:
-            init_values_callable = lambda: init_values_function(seasonality)  # noqa
+        # lambda does not work in serialization in pickle
+        # callable object as an alternative workaround
+        if self._seasonality > 1:
+            init_values_callable = ETSInitializer(
+                self._seasonality
+            )
             self._init_values = init_values_callable
 
     def _set_static_data_attributes(self):