move multiple files

surfaces/_base_test_function.py

@@ -0,0 +1,102 @@
+# Author: Simon Blanke
+# Email: [email protected]
+# License: MIT License
+
+
+import time
+import numpy as np
+import pandas as pd
+from functools import reduce
+
+from hyperactive import Hyperactive
+from hyperactive.optimizers import GridSearchOptimizer
+
+from ..machine_learning.data_collector import SurfacesDataCollector
+
+
+class ObjectiveFunction:
+    explanation = """ """
+
+    dimensions = " "
+    formula = r" "
+    global_minimum = r" "
+
+    def __init__(self, metric="score", input_type="dictionary", sleep=0):
+        self.metric = metric
+        self.input_type = input_type
+        self.sleep = sleep
+
+        self.sql_data = SurfacesDataCollector()
+
+    def search_space(self, min=-5, max=5, step=0.1, value_typ="array"):
+        search_space_ = {}
+
+        for dim in range(self.n_dim):
+            dim_str = "x" + str(dim)
+
+            values = np.arange(min, max, step)
+            if value_typ == "list":
+                values = list(values)
+            search_space_[dim_str] = values
+
+        return search_space_
+
+    def collect_data(self, if_exists="append"):
+        para_names = list(self.search_space().keys())
+        search_data_cols = para_names + ["score"]
+        search_data = pd.DataFrame([], columns=search_data_cols)
+        search_data_length = 0
+
+        dim_sizes_list = [len(array) for array in self.search_space().values()]
+        search_space_size = reduce((lambda x, y: x * y), dim_sizes_list)
+
+        while search_data_length < search_space_size:
+            hyper = Hyperactive(verbosity=["progress_bar"])
+            hyper.add_search(
+                self.objective_function_dict,
+                self.search_space(value_typ="list"),
+                initialize={},
+                n_iter=search_space_size,
+                optimizer=GridSearchOptimizer(direction="orthogonal"),
+                memory_warm_start=search_data,
+            )
+            hyper.run()
+
+            search_data = pd.concat(
+                [search_data, hyper.search_data(self.objective_function_dict)],
+                ignore_index=True,
+            )
+
+            search_data = search_data.drop_duplicates(subset=para_names)
+            search_data_length = len(search_data)
+
+        self.sql_data.save(self.__name__, search_data, if_exists)
+
+    def load_search_data(self):
+        try:
+            dataframe = self.sql_data.load(self.__name__)
+        except:
+            print("Path 2 database: ", self.sql_data.path)
+        return dataframe
+
+    def return_metric(self, loss):
+        if self.metric == "score":
+            return -loss
+        elif self.metric == "loss":
+            return loss
+
+    def objective_function_np(self, *args):
+        para = {}
+        for i, arg in enumerate(args):
+            dim_str = "x" + str(i)
+            para[dim_str] = arg
+
+        return self.objective_function_dict(para)
+
+    def __call__(self, *input):
+        time.sleep(self.sleep)
+
+        if self.input_type == "dictionary":
+            return self.objective_function_dict(*input)
+        elif self.input_type == "arrays":
+            return self.objective_function_np(*input)

surfaces/machine_learning/machine_learning_functions/base_machine_learning_function.py → surfaces/machine_learning_functions/base_machine_learning_function.py

@@ -1,4 +1,8 @@
-import os
+# Author: Simon Blanke
+# Email: [email protected]
+# License: MIT License
+
+
 import time
 
 import numpy as np

surfaces/machine_learning/machine_learning_functions/tabular_classifiers.py → surfaces/machine_learning_functions/tabular_classifiers.py

@@ -4,7 +4,7 @@
 from sklearn.neighbors import KNeighborsClassifier
 
 from sklearn.model_selection import cross_val_score
-from ..datasets import digits_data, wine_data, iris_data
+from .datasets import digits_data, wine_data, iris_data
 
 from .base_machine_learning_function import BaseMachineLearningFunction
 

surfaces/machine_learning/machine_learning_functions/tabular_regressors.py → surfaces/machine_learning_functions/tabular_regressors.py

@@ -5,7 +5,7 @@
 from sklearn.ensemble import GradientBoostingRegressor
 
 from sklearn.model_selection import cross_val_score
-from ..datasets import diabetes_data
+from .datasets import diabetes_data
 
 from .base_machine_learning_function import BaseMachineLearningFunction
 

surfaces/test_functions/_base_objective_function.py → surfaces/mathematical_functions/_base_objective_function.py

@@ -11,10 +11,16 @@
 from hyperactive import Hyperactive
 from hyperactive.optimizers import GridSearchOptimizer
 
-from ..machine_learning.data_collector import SurfacesDataCollector
+from ..data_collector import SurfacesDataCollector
 
 
 class ObjectiveFunction:
+    explanation = """ """
+
+    dimensions = " "
+    formula = r" "
+    global_minimum = r" "
+
     def __init__(self, metric="score", input_type="dictionary", sleep=0):
         self.metric = metric
         self.input_type = input_type

surfaces/test_functions/sphere_function.py → surfaces/mathematical_functions/sphere_function.py

@@ -10,6 +10,12 @@ class SphereFunction(ObjectiveFunction):
     _name_ = "sphere_function"
     __name__ = "SphereFunction"
 
+    explanation = """The Sphere function has d local minima except for the global one. It is continuous, convex and unimodal. The plot shows its two-dimensional form."""
+
+    dimensions = "n"
+    formula = r"f(\vec{x}) = \sum^n_{i=1}x^2_i"
+    global_minimum = r"f(\vec{x}=0) = 0"
+
     def __init__(self, n_dim, A=1, metric="score", input_type="dictionary", sleep=0):
         super().__init__(metric, input_type, sleep)
         self.n_dim = n_dim