def __init__(self, master, k=3):
super().__init__(master, self.CLASSIFIER_NAME)
self.k_val_slider = SliderWidget(self,
"k",
min_val=1,
max_val=32,
initial_value=k)
class ManualDataWidget(tk.Frame):
DISTRIBUTIONS = []
def __init__(self, master, initial_num_points=200, initial_dispersion=50):
super().__init__(master)
config_label = tk.Label(self, width=33, text="Manual", anchor="w")
config_label.pack()
self.distribution_dropdown = DropdownWidget(self, "Distribution",
["Uniform", "Gaussian"])
self.num_points_slider = SliderWidget(self,
"Number of Points",
min_val=1,
max_val=500,
initial_value=initial_num_points)
self.dispersion_slider = SliderWidget(self,
"Dispersion",
min_val=1,
max_val=200,
initial_value=initial_dispersion)
def get_distribution(self):
return self.distribution_dropdown.get_value()
def get_num_points(self):
return self.num_points_slider.get_value()
def get_dispersion(self):
return self.dispersion_slider.get_value()
def __init__(self, master, max_depth=5):
super().__init__(master, self.CLASSIFIER_NAME)
self.max_depth_val_slider = SliderWidget(self,
"Max tree depth",
min_val=1,
max_val=32,
initial_value=max_depth)
class MLPWidget(ClassifierWidget):
CLASSIFIER_NAME = "Multi-Layer Perceptron"
CLASSIFIER_FUNCTION = MLPClassifier
def __init__(self, master, initial_num_hidden_layers=3, initial_num_neurons_layer=10):
super().__init__(master, self.CLASSIFIER_NAME)
self.num_hidden_layers_val_slider = SliderWidget(self, "Hidden layers", min_val=1, max_val=10, initial_value=initial_num_hidden_layers)
self.num_neurons_layer_val_slider = SliderWidget(self, "Neurons/Layer", min_val=1, max_val=100, initial_value=initial_num_neurons_layer)
def get_classifier(self):
hidden_layer_sizes = tuple([self.num_neurons_layer_val_slider.get_value()]*self.num_neurons_layer_val_slider.get_value())
return self.CLASSIFIER_FUNCTION(hidden_layer_sizes=hidden_layer_sizes, max_iter=500)
def __init__(self, master):
super().__init__(master,
highlightbackground="gray",
highlightthickness=1)
self.master = master
config_label = tk.Label(self, width=33, text="General", anchor="w")
config_label.pack(side=tk.TOP)
self.train_test_slider = SliderWidget(
self,
"Train/Test ratio",
min_val=1,
max_val=99,
initial_value=self.INITIAL_TT_RATIO)
def __init__(self, master, initial_num_points=200, initial_dispersion=50):
super().__init__(master)
config_label = tk.Label(self, width=33, text="Manual", anchor="w")
config_label.pack()
self.distribution_dropdown = DropdownWidget(self, "Distribution",
["Uniform", "Gaussian"])
self.num_points_slider = SliderWidget(self,
"Number of Points",
min_val=1,
max_val=500,
initial_value=initial_num_points)
self.dispersion_slider = SliderWidget(self,
"Dispersion",
min_val=1,
max_val=200,
initial_value=initial_dispersion)
class KNNWidget(ClassifierWidget):
CLASSIFIER_NAME = "k-Nearest Neighbours"
CLASSIFIER_FUNCTION = KNeighborsClassifier
def __init__(self, master, k=3):
super().__init__(master, self.CLASSIFIER_NAME)
self.k_val_slider = SliderWidget(self,
"k",
min_val=1,
max_val=32,
initial_value=k)
def get_classifier(self):
return self.CLASSIFIER_FUNCTION(
n_neighbors=self.k_val_slider.get_value())
class DecisionTreeWidget(ClassifierWidget):
CLASSIFIER_NAME = "Decision Tree"
CLASSIFIER_FUNCTION = DecisionTreeClassifier
def __init__(self, master, max_depth=5):
super().__init__(master, self.CLASSIFIER_NAME)
self.max_depth_val_slider = SliderWidget(self,
"Max tree depth",
min_val=1,
max_val=32,
initial_value=max_depth)
def get_classifier(self):
return self.CLASSIFIER_FUNCTION(
max_depth=self.max_depth_val_slider.get_value())
def __init__(self, master, initial_num_hidden_layers=3, initial_num_neurons_layer=10):
super().__init__(master, self.CLASSIFIER_NAME)
self.num_hidden_layers_val_slider = SliderWidget(self, "Hidden layers", min_val=1, max_val=10, initial_value=initial_num_hidden_layers)
self.num_neurons_layer_val_slider = SliderWidget(self, "Neurons/Layer", min_val=1, max_val=100, initial_value=initial_num_neurons_layer)