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my_lda.py
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from data_utils import get_training
from my_pca import my_pca
from my_lasso import my_lasso
from sklearn.model_selection import KFold, cross_val_score
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import matplotlib.pyplot as plt
def best_lda():
#TODO find best LDA using data
#
pass
def generate_subsets():
"""Returns some of the subsets of predictors
"""
out = [ range(30),
[0,1,2,3,4,5],
[5,6,7,8,9,10],
[1,2,3],
[21,23,5,6,2],
[4,19,12,5,6]]
return out
def getScores(X, y, subsets, num_cv_folds):
kFold = KFold(n_splits=num_cv_folds)
subsets = [X[:, subset] for subset in subsets]
scores = []
my_lda = LinearDiscriminantAnalysis()
for subset in subsets:
print "Evaluating LDA with a subset of predictors"
my_lda.fit(subset, Y)
scores.append(cross_val_score(my_lda, subset, Y, cv = kFold).mean())
return scores
def getScores_pca(X, Y, num_pred_list, num_cv_folds):
kfold = KFold(n_splits=num_cv_folds)
scores = []
for p in num_pred_list:
print("Evaluating LDA with predictors=%2d" % p)
my_LDA = LinearDiscriminantAnalysis()
my_LDA.fit(my_pca(X, p), Y)
scores.append(cross_val_score(my_LDA, X, Y, cv = kfold).mean())
return scores
def getScores_lasso(X, Y, alpha_list, num_cv_folds):
kfold = KFold(n_splits=num_cv_folds)
scores = []
for a in alpha_list:
print("Evaluating LDA with alpha=%2d" % a)
my_LDA = LinearDiscriminantAnalysis()
my_LDA.fit(my_lasso(X, Y, a), Y)
scores.append(cross_val_score(my_LDA, X, Y, cv = kfold).mean())
return scores
def plotAccuracy(accuracy, pred, title):
fig = plt.figure(figsize=(10,4),tight_layout=True)
ax = fig.add_subplot(1,1,1)
plt.plot(k, accuracy)
ax.set_xlabel("Predictors")
ax.set_ylabel("Accuracy")
ax.set_title(title, fontsize = 12)
plt.show()
if __name__ == '__main__':
X, Y = get_training()
num_pred_list = [3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 30]
alpha_list = [0,.125, .25, .375, .5, .625, .75, .875, 1]
subsets = generate_subsets()
ten_scores_subsets = getScores(X, Y, subsets, 10)
loocv_scores_subsets = getScores(X, Y, subsets, len(X))
ten_scores_pca = getScores_pca(X, Y, num_pred_list, 10)
loocv_scores_pca = getScores_pca(X, Y, num_pred_list, len(X))
ten_scores_lasso = getScores_lasso(X, Y, alpha_list, 10)
loocv_scores_lasso = getScores_lasso(X, Y, alpha_list, len(X))
print("_______________________Subsets______________________________")
for i in range(len(subsets)):
acc1 = ten_scores_subsets[i]
acc2 = loocv_scores_subsets[i]
print("| subset = %2d | 10-Fold Accuracy: %.3f | LOOCV Accuracy: %.3f |" % (i, acc1, acc2))
plotAccuracy(ten_scores, subsets, "(10 Fold CV)")
plotAccuracy(loocv_scores, subsets, "(LOOCV)")
print("____________________________________________________________")
print("_______________________PCA__________________________________")
for i in range(len(num_pred_list)):
p = num_pred_list[i]
acc1 = ten_scores_pca[i]
acc2 = loocv_scores_pca[i]
print("| predictors = %2d | 10-Fold Accuracy: %.3f | LOOCV Accuracy: %.3f |" % (p, acc1, acc2))
plotAccuracy(ten_scores, num_pred_list, "(10 Fold CV)")
plotAccuracy(loocv_scores, num_pred_list, "(LOOCV)")
print("____________________________________________________________")
print("_______________________Lasso________________________________")
for i in range(len(alpha_list)):
a = alpha_list[i]
acc1 = ten_scores_lasso[i]
acc2 = loocv_scores_lasso[i]
print("| alpha = %.3f | 10-Fold Accuracy: %.3f | LOOCV Accuracy: %.3f |" % (a, acc1, acc2))
plotAccuracy(ten_scores, alpha_list, "(10 Fold CV)")
plotAccuracy(loocv_scores, alpha_list, "(LOOCV)")
print("____________________________________________________________")
# TODO Generate graphs of num_pred vs. accuracy
"""
output:
_______________________Subsets______________________________
| subset = 0 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| subset = 1 | 10-Fold Accuracy: 0.902 | LOOCV Accuracy: 0.914 |
| subset = 2 | 10-Fold Accuracy: 0.906 | LOOCV Accuracy: 0.908 |
| subset = 3 | 10-Fold Accuracy: 0.885 | LOOCV Accuracy: 0.902 |
| subset = 4 | 10-Fold Accuracy: 0.921 | LOOCV Accuracy: 0.929 |
| subset = 5 | 10-Fold Accuracy: 0.894 | LOOCV Accuracy: 0.900 |
____________________________________________________________
_______________________PCA__________________________________
| predictors = 3 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 5 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 7 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 9 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 11 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 13 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 15 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 17 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 19 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 21 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| predictors = 30 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
____________________________________________________________
_______________________Lasso________________________________
| alpha = 0.000 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 0.125 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 0.250 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 0.375 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 0.500 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 0.625 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 0.750 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 0.875 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
| alpha = 1.000 | 10-Fold Accuracy: 0.964 | LOOCV Accuracy: 0.960 |
____________________________________________________________
"""