added random classifier and random regressor

Author: Yam Peleg

dist/hunga_bunga-0.1.tar.gz

@@ -2,7 +2,7 @@
 
 import random
 
-from hunga_bunga import HungaBungaClassifier, HungaBungaRegressor, HungaBungaZeroKnowledge
+from hunga_bunga import HungaBungaClassifier, HungaBungaRegressor, HungaBungaZeroKnowledge, HungaBungaRandomClassifier, HungaBungaRandomRegressor
 from hunga_bunga.regression import gen_reg_data
 from sklearn import datasets
 
@@ -16,22 +16,38 @@
 
 
 
-# ---------- Classification ----------
+# ---------- Brute-Force Classification ----------
 
 clf = HungaBungaClassifier()
 clf.fit(X_c, y_c)
 print(clf.predict(X_c))
 
 
 
-# ---------- Regression ----------
+# ---------- Random Classification ----------
+
+clf = HungaBungaRandomClassifier()
+clf.fit(X_c, y_c)
+print(clf.predict(X_c))
+
+
+
+# ---------- Brute-Force Regression ----------
 
 mdl = HungaBungaRegressor()
 mdl.fit(X_r, y_r)
 print(mdl.predict(X_c))
 
 
 
+# ---------- Random Regression ----------
+
+mdl = HungaBungaRandomRegressor()
+mdl.fit(X_r, y_r)
+print(mdl.predict(X_c))
+
+
+
 # ---------- Zero Knowledge ----------
 
 X, y = random.choice(((X_c, y_c), (X_r, y_r)))

example.py

@@ -0,0 +1,12 @@
+Metadata-Version: 1.0
+Name: hunga-bunga
+Version: 0.1
+Summary: Brute-Force All of sklearn!
+Home-page: https://github.com/ypeleg/HungaBunga
+Author: Yam Peleg
+Author-email: yam@deeptrading.com
+License: MIT
+Description-Content-Type: UNKNOWN
+Description: Lol, why do you read this?
+        
+Platform: UNKNOWN

hunga_bunga.egg-info/PKG-INFO

@@ -0,0 +1,12 @@
+README.txt
+setup.py
+hunga_bunga/__init__.py
+hunga_bunga/classification.py
+hunga_bunga/core.py
+hunga_bunga/params.py
+hunga_bunga/regression.py
+hunga_bunga.egg-info/PKG-INFO
+hunga_bunga.egg-info/SOURCES.txt
+hunga_bunga.egg-info/dependency_links.txt
+hunga_bunga.egg-info/requires.txt
+hunga_bunga.egg-info/top_level.txt

hunga_bunga.egg-info/SOURCES.txt

@@ -0,0 +1 @@
+

hunga_bunga.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+numpy

hunga_bunga.egg-info/requires.txt

@@ -0,0 +1 @@
+hunga_bunga

hunga_bunga.egg-info/top_level.txt

@@ -3,8 +3,8 @@
 warnings.filterwarnings('ignore')
 from multiprocessing import cpu_count
 from sklearn.base import BaseEstimator
-from regression import HungaBungaRegressor
-from classification import HungaBungaClassifier
+from regression import HungaBungaRegressor, HungaBungaRandomRegressor
+from classification import HungaBungaClassifier, HungaBungaRandomClassifier
 
 
 class HungaBungaZeroKnowledge(BaseEstimator):

hunga_bunga/__init__.py

@@ -1,4 +1,5 @@
 
+import random
 import warnings
 warnings.filterwarnings('ignore')
 
@@ -15,6 +16,7 @@
 from sklearn.naive_bayes import GaussianNB
 from sklearn.neural_network import MLPClassifier
 from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import ParameterSampler
 
 from sklearn.ensemble import AdaBoostRegressor, ExtraTreesRegressor, RandomForestRegressor
 from sklearn.tree import DecisionTreeRegressor
@@ -178,6 +180,11 @@ def run_all_classifiers(x, y, small = True, normalize_x = True, n_jobs=cpu_count
     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, grid_search=grid_search)
 
+def run_one_classifier(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)
+    all_params = random.choice(all_params)
+    return all_params[0](**(list(ParameterSampler(all_params[1], n_iter=1))[0]))
+
 
 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, grid_search=True):
@@ -202,6 +209,30 @@ def predict(self, x):
         return self.model.predict(x)
 
 
+class HungaBungaRandomClassifier(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, grid_search=True):
+        self.model = None
+        self.brain = brain
+        self.test_size = test_size
+        self.n_splits = n_splits
+        self.random_state = random_state
+        self.upsample = upsample
+        self.scoring = None
+        self.verbose = verbose
+        self.n_jobs = n_jobs
+        self.normalize_x = normalize_x
+        self.grid_search = grid_search
+        super(HungaBungaRandomClassifier, self).__init__()
+
+    def fit(self, x, y):
+        self.model = run_one_classifier(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)
+        self.model.fit(x, y)
+        return self
+
+    def predict(self, x):
+        return self.model.predict(x)
+
+
 if __name__ == '__main__':
     iris = datasets.load_iris()
     X, y = iris.data, iris.target