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analysis?py v 1.1.11.002

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ltcptgeneral 2019-11-10 02:04:48 -06:00
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commit cf14005b67
1 changed files with 61 additions and 46 deletions
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data analysis/analysis/analysis.py
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@ -11,6 +11,9 @@ __version__ = "1.1.11.001"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.11.002:
- consolidated matrics
- fixed __all__
1.1.11.001:
- added test/train split to RandomForestClassifier and RandomForestRegressor
1.1.11.000:
@ -206,15 +209,17 @@ __all__ = [
'elo',
'gliko2',
'trueskill',
'r_squared',
'mse',
'rms',
'RegressionMetrics',
'ClassificationMetrics',
'kmeans',
'pca',
'decisiontree',
'knn',
'knn_classifier',
'knn_regressor',
'NaiveBayes',
'SVM',
'random_forest_classifier',
'random_forest_regressor',
'Regression',
'Gliko2',
# all statistics functions left out due to integration in other functions
@ -372,19 +377,38 @@ def trueskill(teams_data, observations):#teams_data is array of array of tuples
return Trueskill.rate(teams_data, observations)
@jit(forceobj=True)
def r_squared(predictions, targets): # assumes equal size inputs
class RegressionMetrics():
return sklearn.metrics.r2_score(np.array(targets), np.array(predictions))
def __new__(self, predictions, targets):
return r_squared(predictions, targets), mse(predictions, targets), rms(predictions, targets)
def r_squared(predictions, targets): # assumes equal size inputs
return sklearn.metrics.r2_score(targets, predictions)
def mse(predictions, targets):
return sklearn.metrics.mean_squared_error(targets, predictions)
def rms(predictions, targets):
return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
@jit(forceobj=True)
def mse(predictions, targets):
class ClassificationMetrics():
return sklearn.metrics.mean_squared_error(np.array(targets), np.array(predictions))
def __new__(self, predictions, targets):
@jit(forceobj=True)
def rms(predictions, targets):
return cm(predictions, targets), cr(predictions, targets)
return math.sqrt(sklearn.metrics.mean_squared_error(np.array(targets), np.array(predictions)))
def cm(predictions, targets):
return sklearn.metrics.confusion_matrix(targets, predictions)
def cr(predictions, targets):
return sklearn.metrics.classification_report(targets, predictions)
@jit(nopython=True)
def mean(data):
@ -430,22 +454,29 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth)
model = model.fit(data_train,labels_train)
predictions = model.predict(data_test)
cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
cr = sklearn.metrics.classification_report(labels_test, predictions)
metrics = ClassificationMetrics(predictions, labels_test)
return model, cm, cr
return model, metrics
@jit(forceobj=True)
def knn(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
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.neighbors.KNeighborsClassifier()
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
cr = sklearn.metrics.classification_report(labels_test, predictions)
return model, cm, cr
return model, ClassificationMetrics(predictions, labels_test)
def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(inputs, outputs, test_size=test_size, random_state=1)
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, RegressionMetrics(predictions, labels_test)
@jit(forceobj=True)
class NaiveBayes:
@ -456,10 +487,8 @@ class NaiveBayes:
model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
cr = sklearn.metrics.classification_report(labels_test, predictions)
return model, cm, cr
return model, ClassificationMetrics(predictions, labels_test)
def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
@ -467,10 +496,8 @@ class NaiveBayes:
model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
cr = sklearn.metrics.classification_report(labels_test, predictions)
return model, cm, cr
return model, ClassificationMetrics(predictions, labels_test)
def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
@ -478,10 +505,8 @@ class NaiveBayes:
model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
cm = sklearn.metrics.confusion_matrix(labels_test, predictions)
cr = sklearn.metrics.classification_report(labels_test, predictions)
return model, cm, cr
return model, ClassificationMetrics(predictions, labels_test)
def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
@ -489,10 +514,8 @@ class NaiveBayes:
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)
cr = sklearn.metrics.classification_report(labels_test, predictions)
return model, cm, cr
return model, ClassificationMetrics(predictions, labels_test)
@jit(forceobj=True)
class SVM:
@ -542,40 +565,32 @@ class SVM:
def eval_classification(self, kernel, test_data, test_outputs):
predictions = kernel.predict(test_data)
cm = sklearn.metrics.confusion_matrix(predictions, predictions)
cr = sklearn.metrics.classification_report(predictions, predictions)
return cm, cr
return ClassificationMetrics(predictions, test_outputs)
def eval_regression(self, kernel, test_data, test_outputs):
predictions = kernel.predict(test_data)
r_2 = r_squared(predictions, test_outputs)
_mse = mse(predictions, test_outputs)
_rms = rms(predictions, test_outputs)
return r_2, _mse, _rms
return RegressionMetrics(predictions, test_outputs)
def RandomForestClassifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
def random_forest_classifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
kernel.fit(data_train, labels_train)
predictions = kernel.predict(data_test)
cm = sklearn.metrics.confusion_matrix(predictions, predictions)
cr = sklearn.metrics.classification_report(predictions, predictions)
return kernel, cm, cr
def RandomForestRegressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
return kernel, ClassificationMetrics(predictions, labels_test)
def random_forest_regressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(inputs, outputs, test_size=test_size, random_state=1)
kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
kernel.fit(data_train, outputs_train)
predictions = kernel.predict(data_test)
r_2 = r_squared(predictions, outputs_test)
_mse = mse(predictions, outputs_test)
_rms = rms(predictions, outputs_test)
return kernel, r_2, _mse, _rms
return kernel, RegressionMetrics(predictions, labels_test)
class Regression: