def __training(self, extractor: AppearanceExtractor) -> Classifier:
""" Execute the training phase of the Appearance Layer.
Arguments:
extractor (AppearanceExtractor): The extractor which delivers the training data.
Returns:
A classifier which classifies players as either Mol or non-Mol based on how often they appear.
"""
train_input, train_output = extractor.get_train_data()
classifier = NaiveKDEClassifier(cdf_cutoff = self.__cdf_cutoff)
classifier.train(train_input, train_output)
return classifier
def __training(
self, extractor: AppearanceExtractor
) -> Tuple[gaussian_kde, gaussian_kde]:
""" Execute the training phase of the Appearance Layer.
Arguments:
extractor (AppearanceExtractor): The extractor which delivers the training data.
Returns:
The kernel density estimator for respectively the Mol data and non-Mol data.
"""
train_input, train_output = extractor.get_train_data()
non_mol_input = np.array(
[ti[0] for ti, to in zip(train_input, train_output) if to == 0.0])
mol_input = np.array(
[ti[0] for ti, to in zip(train_input, train_output) if to == 1.0])
non_mol_kde = self.kernel_density_estimation(non_mol_input)
mol_kde = self.kernel_density_estimation(mol_input)
return non_mol_kde, mol_kde
from Layers.Appearance.AppearanceExtractor import AppearanceExtractor
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
TEST_SEASONS = {13, 14, 15, 16, 17, 18, 19, 20}
extractor = AppearanceExtractor(0, 0, TEST_SEASONS, 1, 1, 0.0)
train_input, train_output = extractor.get_train_data()
non_mol = [
data[0] for data, label in zip(train_input, train_output) if label == 0.0
]
mol = [
data[0] for data, label in zip(train_input, train_output) if label == 1.0
]
plt.figure(figsize=(12, 3))
plt.xlabel("Relative Appearance")
plt.ylabel("Is 'mol'")
plt.yticks(np.linspace(0.0, 1.0, 11))
plt.gcf().subplots_adjust(bottom=0.15)
mol_norm = norm.fit(mol)
X = np.linspace(-1.5, 1.0, 500)
mol_Y = [norm.pdf(x, loc=mol_norm[0], scale=mol_norm[1]) for x in X]
plt.plot(X, mol_Y, color='r')
non_mol_norm = norm.fit(non_mol)
non_mol_Y = [
norm.pdf(x, loc=non_mol_norm[0], scale=non_mol_norm[1]) for x in X
]
from Layers.Appearance.AppearanceExtractor import AppearanceExtractor
import matplotlib.pyplot as plt
import numpy as np
TEST_SEASONS = {13, 14, 15, 16, 17, 18, 19, 20, 21}
AUGMENTATION_CUTS = 1
AUGMENTATION_MIN_CUTS_ON = 1
OUTLIER_CUTOFF = 0.00
extractor = AppearanceExtractor(0, 0, TEST_SEASONS, AUGMENTATION_CUTS,
AUGMENTATION_MIN_CUTS_ON)
train_input, _ = extractor.get_train_data()
train_input = np.squeeze(np.exp(train_input) -
AppearanceExtractor.SMALL_LOG_ADDITION,
axis=1)
plt.hist(train_input, bins=10, edgecolor='black')
plt.xlabel("Absolute Appearance")
plt.ylabel("#Occurrences")
plt.show()
