Support numpy.random.RandomState objects (take 2) (#556)

* Support numpy.random.RandomState objects (#530)

* Squash random_state work from 347_random_state

* Lint

* Lint

* Changelog

* Lint

* Test update

* Always pass random_state to components

* Lint

* Fix bug: set random state first. Remove usages of random_state as dict param item in test_pipelines.py

* update test for clarity

* Fix catboost

* Update logreg test

* Lint catboost

* Update tuner impl to handle random_state

* Test changes

* Lint

* Docs changes

* Add unit test for get_random_state

* Update test

* Remove uncalled code after my changes

* Fix tests after rebase

* Add unit test coverage for RandomSearchTuner.is_search_space_exhausted

* Add unit test coverage for max_time

* Add test coverage of get_pipeline when invalid

* Lint

* Add unit test coverage of when fit/score throws in autobase

* Remove duplicate

* Lets try that again... got mysterious docs failure

* Get min/max int instead of using fixed number which is incorrect for 32bit systems

* Add limits to seed range for xgboost too

* Introduce a sustainable pattern for generating random seeds from RNGs for different classes

* Update changelog

* Use SEED_BOUNDS in unit tests

* Update comment

docs/source/changelog.rst

@@ -10,6 +10,7 @@ Changelog
         * Added access to parameters in Pipelines with `PipelineBase.parameters` (used to be return of `PipelineBase.describe`) :pr:`501`
         * Added `fill_value` parameter for SimpleImputer :pr:`509`    
         * Added functionality to override component hyperparemeters and made pipelines take hyperparemeters from components :pr:`516`
+        * Allow numpy.random.RandomState for random_state parameters :pr:`556`
     * Fixes
     * Changes
         * Undo version cap in XGBoost placed in :pr:`402` and allowed all released of XGBoost :pr:`407`

docs/source/pipelines/custom_pipelines.ipynb

@@ -46,11 +46,10 @@
     "    'Logistic Regression Classifier':{\n",
     "        'penalty':'l2',\n",
     "        'C':5,\n",
-    "        'random_state':3\n",
     "    }\n",
     "}\n",
     "\n",
-    "pipeline = CustomPipeline(parameters={}, objective=objective)"
+    "pipeline = CustomPipeline(parameters={}, objective=objective, random_state=3)"
    ]
   },
   {

docs/source/pipelines/overview.ipynb

@@ -50,11 +50,10 @@
     "            \"eta\": 0.5,\n",
     "            \"min_child_weight\": 5,\n",
     "            \"max_depth\": 10,\n",
-    "            \"random_state\":5\n",
     "        }\n",
     "    }\n",
     "\n",
-    "xgp = XGBoostPipeline(objective='recall', parameters=parameters)\n",
+    "xgp = XGBoostPipeline(objective='recall', parameters=parameters, random_state=5)\n",
     "xgp.graph()"
    ]
   },

evalml/automl/auto_base.py

@@ -1,5 +1,4 @@
 import inspect
-import random
 import time
 from collections import OrderedDict
 from sys import stdout
@@ -16,7 +15,7 @@
 from evalml.pipelines.components import handle_component
 from evalml.problem_types import ProblemTypes
 from evalml.tuners import SKOptTuner
-from evalml.utils import Logger, convert_to_seconds
+from evalml.utils import Logger, convert_to_seconds, get_random_state
 
 logger = Logger()
 
@@ -78,10 +77,7 @@ def __init__(self, problem_type, tuner, cv, objective, max_pipelines, max_time,
             'search_order': []
         }
         self.trained_pipelines = {}
-
-        self.random_state = random_state
-        random.seed(self.random_state)
-        np.random.seed(seed=self.random_state)
+        self.random_state = get_random_state(random_state)
 
         self.n_jobs = n_jobs
         self.possible_model_families = list(set([p.model_family for p in self.possible_pipelines]))
@@ -90,7 +86,7 @@ def __init__(self, problem_type, tuner, cv, objective, max_pipelines, max_time,
         self.search_spaces = {}
         for p in self.possible_pipelines:
             space = list(p.hyperparameters.items())
-            self.tuners[p.name] = tuner([s[1] for s in space], random_state=random_state)
+            self.tuners[p.name] = tuner([s[1] for s in space], random_state=self.random_state)
             self.search_spaces[p.name] = [s[0] for s in space]
         self.additional_objectives = additional_objectives
         self._MAX_NAME_LEN = 40
@@ -110,8 +106,8 @@ def search(self, X, y, feature_types=None, raise_errors=False, show_iteration_pl
 
             y (pd.Series): the target training labels of length [n_samples]
 
-            feature_types (list, optional): list of feature types. either numeric of categorical.
-                categorical features will automatically be encoded
+            feature_types (list, optional): list of feature types, either numerical or categorical.
+                Categorical features will automatically be encoded
 
             raise_errors (boolean): If true, raise errors and exit search if a pipeline errors during fitting
 
@@ -245,8 +241,6 @@ def _transform_parameters(self, pipeline_class, parameters, number_features):
             component_class = component.__class__
 
             # Inspects each component and adds the following parameters when needed
-            if 'random_state' in inspect.signature(component_class.__init__).parameters:
-                component_parameters['random_state'] = self.random_state
             if 'n_jobs' in inspect.signature(component_class.__init__).parameters:
                 component_parameters['n_jobs'] = self.n_jobs
             if 'number_features' in inspect.signature(component_class.__init__).parameters:
@@ -325,7 +319,7 @@ def _do_iteration(self, X, y, pbar, raise_errors):
             print('')
 
     def _select_pipeline(self):
-        return random.choice(self.possible_pipelines)
+        return self.random_state.choice(self.possible_pipelines)
 
     def _propose_parameters(self, pipeline_class):
         values = self.tuners[pipeline_class.name].propose()

evalml/automl/auto_classification_search.py

@@ -67,7 +67,7 @@ def __init__(self,
             additional_objectives (list): Custom set of objectives to score on.
                 Will override default objectives for problem type if not empty.
 
-            random_state (int): the random_state
+            random_state (int, np.random.RandomState): The random seed/state. Defaults to 0.
 
             n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines.
                 None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.

evalml/automl/auto_regression_search.py

@@ -64,7 +64,7 @@ def __init__(self,
             additional_objectives (list): Custom set of objectives to score on.
                 Will override default objectives for problem type if not empty.
 
-            random_state (int): the random_state
+            random_state (int, np.random.RandomState): The random seed/state. Defaults to 0.
 
             n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines.
                 None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.

evalml/pipelines/classification/catboost.py

@@ -15,8 +15,9 @@ class CatBoostClassificationPipeline(PipelineBase):
         "impute_strategy": ["most_frequent"],
     }
 
-    def __init__(self, parameters, objective):
+    def __init__(self, parameters, objective, random_state=0):
 
         # note: impute_strategy must support both string and numeric data
         super().__init__(parameters=parameters,
-                         objective=objective)
+                         objective=objective,
+                         random_state=random_state)

evalml/pipelines/classification/xgboost.py

@@ -8,6 +8,7 @@ class XGBoostPipeline(PipelineBase):
     component_graph = ['One Hot Encoder', 'Simple Imputer', 'RF Classifier Select From Model', 'XGBoost Classifier']
     supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
 
-    def __init__(self, parameters, objective):
+    def __init__(self, parameters, objective, random_state=0):
         super().__init__(parameters=parameters,
-                         objective=objective)
+                         objective=objective,
+                         random_state=random_state)

evalml/pipelines/components/component_base.py

@@ -1,14 +1,14 @@
 from abc import ABC, abstractmethod
 
 from evalml.exceptions import MethodPropertyNotFoundError
-from evalml.utils import Logger
+from evalml.utils import Logger, get_random_state
 
 logger = Logger()
 
 
 class ComponentBase(ABC):
     def __init__(self, parameters, component_obj, random_state):
-        self.random_state = random_state
+        self.random_state = get_random_state(random_state)
         self._component_obj = component_obj
         self.parameters = parameters
 

evalml/pipelines/components/estimators/classifiers/catboost_classifier.py

@@ -6,7 +6,7 @@
 from evalml.model_family import ModelFamily
 from evalml.pipelines.components.estimators import Estimator
 from evalml.problem_types import ProblemTypes
-from evalml.utils import import_or_raise
+from evalml.utils import SEED_BOUNDS, get_random_seed, import_or_raise
 
 
 class CatBoostClassifier(Estimator):
@@ -26,6 +26,7 @@ class CatBoostClassifier(Estimator):
     supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
 
     def __init__(self, n_estimators=1000, eta=0.03, max_depth=6, bootstrap_type=None, random_state=0):
+        random_seed = get_random_seed(random_state, 0, SEED_BOUNDS.max_bound)
         parameters = {"n_estimators": n_estimators,
                       "eta": eta,
                       "max_depth": max_depth}
@@ -36,6 +37,7 @@ def __init__(self, n_estimators=1000, eta=0.03, max_depth=6, bootstrap_type=None
         catboost = import_or_raise("catboost", error_msg=cb_error_msg)
         self._label_encoder = None
         cb_classifier = catboost.CatBoostClassifier(**parameters,
+                                                    random_seed=random_seed,
                                                     silent=True,
                                                     allow_writing_files=False)
         super().__init__(parameters=parameters,

evalml/pipelines/components/estimators/classifiers/xgboost_classifier.py

@@ -3,7 +3,7 @@
 from evalml.model_family import ModelFamily
 from evalml.pipelines.components.estimators import Estimator
 from evalml.problem_types import ProblemTypes
-from evalml.utils import import_or_raise
+from evalml.utils import SEED_BOUNDS, get_random_seed, import_or_raise
 
 
 class XGBoostClassifier(Estimator):
@@ -19,13 +19,14 @@ class XGBoostClassifier(Estimator):
     supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
 
     def __init__(self, eta=0.1, max_depth=3, min_child_weight=1, n_estimators=100, random_state=0):
+        random_seed = get_random_seed(random_state, SEED_BOUNDS.min_bound, SEED_BOUNDS.max_bound)
         parameters = {"eta": eta,
                       "max_depth": max_depth,
                       "min_child_weight": min_child_weight,
                       "n_estimators": n_estimators}
         xgb_error_msg = "XGBoost is not installed. Please install using `pip install xgboost.`"
         xgb = import_or_raise("xgboost", error_msg=xgb_error_msg)
-        xgb_classifier = xgb.XGBClassifier(random_state=random_state,
+        xgb_classifier = xgb.XGBClassifier(random_state=random_seed,
                                            eta=eta,
                                            max_depth=max_depth,
                                            n_estimators=n_estimators,

evalml/pipelines/components/estimators/regressors/catboost_regressor.py

@@ -3,7 +3,7 @@
 from evalml.model_family import ModelFamily
 from evalml.pipelines.components.estimators import Estimator
 from evalml.problem_types import ProblemTypes
-from evalml.utils import import_or_raise
+from evalml.utils import SEED_BOUNDS, get_random_seed, import_or_raise
 
 
 class CatBoostRegressor(Estimator):
@@ -23,6 +23,7 @@ class CatBoostRegressor(Estimator):
     supported_problem_types = [ProblemTypes.REGRESSION]
 
     def __init__(self, n_estimators=1000, eta=0.03, max_depth=6, bootstrap_type=None, random_state=0):
+        random_seed = get_random_seed(random_state, 0, SEED_BOUNDS.max_bound)
         parameters = {"n_estimators": n_estimators,
                       "eta": eta,
                       "max_depth": max_depth}
@@ -32,7 +33,7 @@ def __init__(self, n_estimators=1000, eta=0.03, max_depth=6, bootstrap_type=None
         cb_error_msg = "catboost is not installed. Please install using `pip install catboost.`"
         catboost = import_or_raise("catboost", error_msg=cb_error_msg)
         cb_regressor = catboost.CatBoostRegressor(**parameters,
-                                                  random_state=random_state,
+                                                  random_seed=random_seed,
                                                   silent=True,
                                                   allow_writing_files=False)
         super().__init__(parameters=parameters,

evalml/pipelines/components/estimators/regressors/linear_regressor.py

@@ -15,7 +15,7 @@ class LinearRegressor(Estimator):
     model_family = ModelFamily.LINEAR_MODEL
     supported_problem_types = [ProblemTypes.REGRESSION]
 
-    def __init__(self, fit_intercept=True, normalize=False, n_jobs=-1):
+    def __init__(self, fit_intercept=True, normalize=False, n_jobs=-1, random_state=0):
         parameters = {
             'fit_intercept': fit_intercept,
             'normalize': normalize
@@ -25,7 +25,7 @@ def __init__(self, fit_intercept=True, normalize=False, n_jobs=-1):
                                               n_jobs=n_jobs)
         super().__init__(parameters=parameters,
                          component_obj=linear_regressor,
-                         random_state=0)
+                         random_state=random_state)
 
     @property
     def feature_importances(self):

evalml/pipelines/components/transformers/imputers/simple_imputer.py

@@ -9,7 +9,7 @@ class SimpleImputer(Transformer):
     name = 'Simple Imputer'
     hyperparameter_ranges = {"impute_strategy": ["mean", "median", "most_frequent"]}
 
-    def __init__(self, impute_strategy="most_frequent", fill_value=None):
+    def __init__(self, impute_strategy="most_frequent", fill_value=None, random_state=0):
         """Initalizes an transformer that imputes missing data according to the specified imputation strategy."
 
         Arguments:
@@ -24,7 +24,7 @@ def __init__(self, impute_strategy="most_frequent", fill_value=None):
                             fill_value=fill_value)
         super().__init__(parameters=parameters,
                          component_obj=imputer,
-                         random_state=0)
+                         random_state=random_state)
 
     def transform(self, X, y=None):
         """Transforms data X by imputing missing values

evalml/pipelines/components/transformers/scalers/standard_scaler.py

@@ -8,9 +8,9 @@ class StandardScaler(Transformer):
     name = "Standard Scaler"
     hyperparameter_ranges = {}
 
-    def __init__(self):
+    def __init__(self, random_state=0):
         parameters = {}
         scaler = SkScaler()
         super().__init__(parameters=parameters,
                          component_obj=scaler,
-                         random_state=0)
+                         random_state=random_state)

evalml/pipelines/components/utils.py

@@ -42,8 +42,6 @@ def _components_dict():
             if params.defaults:
                 if len(params.args) - 1 == len(params.defaults):
                     components[obj.name] = obj
-            elif len(params.args) == 1:
-                components[obj.name] = obj
     return components
 
 

evalml/pipelines/pipeline_base.py

@@ -13,7 +13,7 @@
 from evalml.exceptions import IllFormattedClassNameError
 from evalml.objectives import get_objective
 from evalml.problem_types import handle_problem_types
-from evalml.utils import Logger, classproperty
+from evalml.utils import Logger, classproperty, get_random_state
 
 logger = Logger()
 
@@ -45,7 +45,7 @@ def supported_problem_types(cls):
 
     custom_hyperparameters = None
 
-    def __init__(self, parameters, objective):
+    def __init__(self, parameters, objective, random_state=0):
         """Machine learning pipeline made out of transformers and a estimator.
 
         Required Class Variables:
@@ -58,7 +58,9 @@ def __init__(self, parameters, objective):
 
             parameters (dict): dictionary with component names as keys and dictionary of that component's parameters as values.
                  An empty dictionary {} implies using all default values for component parameters.
+            random_state (int, np.random.RandomState): The random seed/state. Defaults to 0.
         """
+        self.random_state = get_random_state(random_state)
         self.component_graph = [self._instantiate_component(c, parameters) for c in self.component_graph]
         self.supported_problem_types = [handle_problem_types(problem_type) for problem_type in self.supported_problem_types]
         self.objective = get_objective(objective)
@@ -125,7 +127,7 @@ def _instantiate_component(self, component, parameters):
         component_name = component.name
         try:
             component_parameters = parameters.get(component_name, {})
-            new_component = component_class(**component_parameters)
+            new_component = component_class(**component_parameters, random_state=self.random_state)
         except (ValueError, TypeError) as e:
             err = "Error received when instantiating component {} with the following arguments {}".format(component_name, component_parameters)
             raise ValueError(err) from e

evalml/pipelines/regression/catboost.py

@@ -16,6 +16,7 @@ class CatBoostRegressionPipeline(PipelineBase):
         "impute_strategy": ["most_frequent"],
     }
 
-    def __init__(self, parameters, objective):
+    def __init__(self, parameters, objective, random_state=0):
         super().__init__(parameters=parameters,
-                         objective=objective)
+                         objective=objective,
+                         random_state=random_state)

evalml/preprocessing/utils.py

@@ -40,12 +40,12 @@ def load_data(path, index, label, n_rows=None, drop=None, verbose=True, **kwargs
 def split_data(X, y, regression=False, test_size=.2, random_state=None):
     """Splits data into train and test sets.
 
-    Args:
+    Arguments:
         X (pd.DataFrame or np.array) : data of shape [n_samples, n_features]
         y (pd.Series) : labels of length [n_samples]
         regression (bool): if true, do not use stratified split
         test_size (float) : percent of train set to holdout for testing
-        random_state (int) : seed for the random number generator
+        random_state (int, np.random.RandomState) : seed for the random number generator
 
     Returns:
         pd.DataFrame, pd.DataFrame, pd.Series, pd.Series : features and labels each split into train and test sets

evalml/tests/automl_tests/test_auto_classification_search.py

@@ -47,6 +47,14 @@ def test_init(X_y):
     automl.describe_pipeline(0)
 
 
+def test_get_pipeline_none(X_y):
+    X, y = X_y
+
+    automl = AutoClassificationSearch()
+    with pytest.raises(RuntimeError, match="Pipeline not found"):
+        automl.describe_pipeline(0)
+
+
 def test_cv(X_y):
     X, y = X_y
     cv_folds = 5
@@ -386,3 +394,11 @@ def test_plot_iterations_ipython_mock_import_failure(mock_ipython_display, X_y):
     assert y.is_monotonic_increasing
     assert len(x) == 3
     assert len(y) == 3
+
+
+def test_max_time(X_y):
+    X, y = X_y
+    clf = AutoClassificationSearch(max_time=1e-16)
+    clf.search(X, y)
+    # search will always run at least one pipeline
+    assert len(clf.results['pipeline_results']) == 1