hunga bunga

Author: Yam Peleg

.idea/workspace.xml

@@ -61,6 +61,8 @@ Dependencies
 - SciPy (>= 0.17.0)
 - joblib (>= 0.11)
 - scikit-learn (>=0.20.0)
+- tabulate (>=0.8.2)
+- tqdm (>=4.28.1)
 
 ~~~~~~~~~~~~
 

Readme.md

@@ -7,7 +7,7 @@
 
 
 class HungaBungaZeroKnowledge(BaseEstimator):
-    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=True, normalize_x = True, n_jobs =cpu_count() - 1):
+    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=True, normalize_x = True, n_jobs =cpu_count() - 1, grid_search=True):
         self.model = None
         self.brain = brain
         self.test_size = test_size
@@ -19,15 +19,16 @@ def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None
         self.n_jobs = n_jobs
         self.normalize_x = normalize_x
         self.problem_type = 'Unknown'
+        self.grid_search = grid_search
         super(HungaBungaZeroKnowledge, self).__init__()
 
     def fit(self, X, y):
         try:
-            self.model = HungaBungaClassifier(normalize_x=self.normalize_x, test_size=self.test_size, n_splits=self.n_splits, upsample=self.upsample, scoring=self.scoring, verbose=self.verbose, brain=self.brain, n_jobs=self.n_jobs)
+            self.model = HungaBungaClassifier(normalize_x=self.normalize_x, test_size=self.test_size, n_splits=self.n_splits, upsample=self.upsample, scoring=self.scoring, verbose=self.verbose, brain=self.brain, n_jobs=self.n_jobs, grid_search=self.grid_search)
             self.model.fit(X, y)
             self.problem_type = 'Classification'
         except:
-            self.model = HungaBungaRegressor(normalize_x=self.normalize_x, test_size=self.test_size, n_splits=self.n_splits, upsample=self.upsample, scoring=self.scoring, verbose=self.verbose, brain=self.brain, n_jobs=self.n_jobs)
+            self.model = HungaBungaRegressor(normalize_x=self.normalize_x, test_size=self.test_size, n_splits=self.n_splits, upsample=self.upsample, scoring=self.scoring, verbose=self.verbose, brain=self.brain, n_jobs=self.n_jobs, grid_search=self.grid_search)
             self.model.fit(X, y)
             self.problem_type = 'Regression'
         return self

hunga_bunga/__init__.py

@@ -174,13 +174,13 @@
 ]
 
 
-def run_all_classifiers(x, y, small = True, normalize_x = True, n_jobs=cpu_count()-1, brain=False, test_size=0.2, n_splits=5, upsample=True, scoring=None, verbose=False):
+def run_all_classifiers(x, y, small = True, normalize_x = True, n_jobs=cpu_count()-1, brain=False, test_size=0.2, n_splits=5, upsample=True, scoring=None, verbose=False, grid_search=True):
     all_params = (linear_models_n_params_small if small else linear_models_n_params) +  (nn_models_n_params_small if small else nn_models_n_params) + ([] if small else gaussianprocess_models_n_params) + neighbor_models_n_params + (svm_models_n_params_small if small else svm_models_n_params) + (tree_models_n_params_small if small else tree_models_n_params)
-    return main_loop(all_params, StandardScaler().fit_transform(x) if normalize_x else x, y, isClassification=True, n_jobs=n_jobs, verbose=verbose, brain=brain, test_size=test_size, n_splits=n_splits, upsample=upsample, scoring=scoring)
+    return main_loop(all_params, StandardScaler().fit_transform(x) if normalize_x else x, y, isClassification=True, n_jobs=n_jobs, verbose=verbose, brain=brain, test_size=test_size, n_splits=n_splits, upsample=upsample, scoring=scoring, grid_search=grid_search)
 
 
 class HungaBungaClassifier(ClassifierMixin):
-    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=False, normalize_x = True, n_jobs =cpu_count() - 1):
+    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=False, normalize_x = True, n_jobs =cpu_count() - 1, grid_search=True):
         self.model = None
         self.brain = brain
         self.test_size = test_size
@@ -191,10 +191,11 @@ def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None
         self.verbose = verbose
         self.n_jobs = n_jobs
         self.normalize_x = normalize_x
+        self.grid_search = grid_search
         super(HungaBungaClassifier, self).__init__()
 
     def fit(self, x, y):
-        self.model = run_all_classifiers(x, y, normalize_x=self.normalize_x, test_size=self.test_size, n_splits=self.n_splits, upsample=self.upsample, scoring=self.scoring, verbose=self.verbose, brain=self.brain, n_jobs=self.n_jobs)[0]
+        self.model = run_all_classifiers(x, y, normalize_x=self.normalize_x, test_size=self.test_size, n_splits=self.n_splits, upsample=self.upsample, scoring=self.scoring, verbose=self.verbose, brain=self.brain, n_jobs=self.n_jobs, grid_search=self.grid_search)[0]
         return self
 
     def predict(self, x):