Codecademy export

script.py

import codecademylib3_seaborn
import matplotlib.pyplot as plt
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from svm_visualization import draw_boundary
from players import aaron_judge, jose_altuve, david_ortiz

def find_opt_SVC_paremeters(training_data, training_set, validation_data, validation_set):
  gamma_p = 30
  C_p = 30
  opt_gamma = 0 
  opt_c = 0
  max_gamma_score = []
  max_c_score = []
  for i in range(1, gamma_p):
    gamma_p-=1  
    classifier = SVC(gamma=gamma_p, C=C_p)
    classifier.fit(training_data, training_set)
    max_gamma_score.append(classifier.score(validation_data, validation_set))
  opt_gamma = 29-np.argmax(max_gamma_score) 
  for j in range(1, 30):
    C_p-=1  
    classifier = SVC(gamma=opt_gamma, C=C_p)
    classifier.fit(training_data, training_set)
    max_c_score.append(classifier.score(validation_data, validation_set))
  opt_c = 29-np.argmax(max_c_score)
  return opt_gamma, opt_c

def SVM_prediction_machine(dataset, athlete_name):
  fig, ax = plt.subplots()

  dataset['type'] = dataset['type'].map({'S':1, 'B':0})

  dataset = dataset.dropna(subset = ['plate_x', 'plate_z', 'type'])

  plt.scatter(x = dataset['plate_x'], y = dataset['plate_z'], c = dataset['type'], cmap = plt.cm.coolwarm, alpha = 0.5)
  plt.xlabel('plate_x')
  plt.ylabel('plate_z')

  training_set, validation_set = train_test_split(dataset, random_state=1) 
  training_features = training_set[['plate_x', 'plate_z', 'strikes']]
  validation_features = validation_set[['plate_x', 'plate_z', 'strikes']]
  opt_gamma, opt_c = find_opt_SVC_paremeters(training_features, training_set['type'], validation_features, validation_set['type'])
  print(athlete_name)
  print('gamma -', opt_gamma, 'C -', opt_c, sep=' ')
  classifier = SVC(gamma=opt_gamma, C=opt_c)  
  classifier.fit(training_features, training_set['type'])
  print('model_score -', classifier.score(validation_features, validation_set['type']), sep=' ')

  #draw_boundary(ax, classifier)

  #plt.show()
  return
  
SVM_prediction_machine(aaron_judge, 'aaron_judge')
SVM_prediction_machine(jose_altuve, 'jose_altuve')
SVM_prediction_machine(david_ortiz, 'david_ortiz')