alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-26 01:29:10 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis.py 1.1.9.001

Browse Source
This commit is contained in:
ltcptgeneral 2019-11-06 15:32:21 -06:00
parent a51f1f134d
commit 456836bdb8
1 changed files with 18 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

34
data analysis/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.9.000"
__version__ = "1.1.9.001"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.9.001:
- fixed bugs with SVM and NaiveBayes
1.1.9.000:
- added SVM class, subclasses, and functions
- note: untested
@ -202,7 +204,7 @@ __all__ = [
'pca',
'decisiontree',
'knn',
'NaiveBayes'
'NaiveBayes',
'Regression',
'Gliko2',
# all statistics functions left out due to integration in other functions
@ -466,7 +468,7 @@ class NaiveBayes:
def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
model = sklearn.naive_bayes.ComplementNB(aplha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
@ -478,39 +480,39 @@ class SVM:
class CustomKernel:
def __init__(self, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
def __new__(self, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking + shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
class StandardKernel:
def __init__(self, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
def __new__(self, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking + shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
class PrebuiltKernel:
class Linear:
def __init__(self):
def __new__(self):
return sklearn.svm.SVC(kernel = 'linear')
class Polynomial:
def __init__(self, power, r_bias):
def __new__(self, power, r_bias):
return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
class RBF:
def __init__(self, gamma):
def __new__(self, gamma):
return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
class Sigmoid:
def __init__(self, r_bias):
def __new__(self, r_bias):
return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
@ -521,8 +523,8 @@ class SVM:
def eval_classification(self, kernel, test_data, test_outputs):
predictions = kernel.predict(test_data)
cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
cr = sklearn.metrics.classification_report(labels_test, predictions)
cm = sklearn.metrics.confusion_matrix(predictions, predictions)
cr = sklearn.metrics.classification_report(predictions, predictions)
return cm, cr
@ -530,10 +532,10 @@ class SVM:
predictions = kernel.predict(test_data)
r_2 = r_squared(predictions, test_outputs)
mse = mse(predictions, test_outputs)
rms = rms(predictions, test_outputs)
_mse = mse(predictions, test_outputs)
_rms = rms(predictions, test_outputs)
return r_2, mse, rms
return r_2, _mse, _rms
class Regression: