analysis.py v 1.2.0.005

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ltcptgeneral 2020-05-01 22:59:54 -05:00
parent 3ab1d0f50a
commit 43bb9ef2bb

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@ -7,10 +7,18 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.2.0.004"
__version__ = "1.2.0.005"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.2.0.005:
- moved random_forrest_regressor and random_forrest_classifier to RandomForrest class
- renamed Metrics to Metric
- renamed RegressionMetrics to RegressionMetric
- renamed ClassificationMetrics to ClassificationMetric
- renamed CorrelationTests to CorrelationTest
- renamed StatisticalTests to StatisticalTest
- reflected rafactoring to all mentions of above classes/functions
1.2.0.004:
- fixed __all__ to reflected the correct functions and classes
- fixed CorrelationTests and StatisticalTests class functions to require self invocation
@ -282,19 +290,18 @@ __all__ = [
'z_normalize',
'histo_analysis',
'regression',
'Metrics',
'RegressionMetrics',
'ClassificationMetrics',
'Metric',
'RegressionMetric',
'ClassificationMetric',
'kmeans',
'pca',
'decisiontree',
'KNN',
'NaiveBayes',
'SVM',
'random_forest_classifier',
'random_forest_regressor',
'CorrelationTests',
'StatisticalTests',
'RandomForrest',
'CorrelationTest',
'StatisticalTest',
# all statistics functions left out due to integration in other functions
]
@ -470,7 +477,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
return regressions
class Metrics:
class Metric:
def elo(self, starting_score, opposing_score, observed, N, K):
@ -497,7 +504,7 @@ class Metrics:
return Trueskill.rate(team_ratings, ranks=observations)
class RegressionMetrics():
class RegressionMetric():
def __new__(cls, predictions, targets):
@ -515,7 +522,7 @@ class RegressionMetrics():
return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
class ClassificationMetrics():
class ClassificationMetric():
def __new__(cls, predictions, targets):
@ -583,7 +590,7 @@ 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)
metrics = ClassificationMetrics(predictions, labels_test)
metrics = ClassificationMetric(predictions, labels_test)
return model, metrics
@ -596,7 +603,7 @@ class KNN:
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetrics(predictions, labels_test)
return model, ClassificationMetric(predictions, labels_test)
def knn_regressor(self, data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
@ -605,7 +612,7 @@ class KNN:
model.fit(data_train, outputs_train)
predictions = model.predict(data_test)
return model, RegressionMetrics(predictions, outputs_test)
return model, RegressionMetric(predictions, outputs_test)
class NaiveBayes:
@ -616,7 +623,7 @@ class NaiveBayes:
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetrics(predictions, labels_test)
return model, ClassificationMetric(predictions, labels_test)
def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
@ -625,7 +632,7 @@ class NaiveBayes:
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetrics(predictions, labels_test)
return model, ClassificationMetric(predictions, labels_test)
def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
@ -634,7 +641,7 @@ class NaiveBayes:
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetrics(predictions, labels_test)
return model, ClassificationMetric(predictions, labels_test)
def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
@ -643,7 +650,7 @@ class NaiveBayes:
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetrics(predictions, labels_test)
return model, ClassificationMetric(predictions, labels_test)
class SVM:
@ -693,33 +700,35 @@ class SVM:
predictions = kernel.predict(test_data)
return ClassificationMetrics(predictions, test_outputs)
return ClassificationMetric(predictions, test_outputs)
def eval_regression(self, kernel, test_data, test_outputs):
predictions = kernel.predict(test_data)
return RegressionMetrics(predictions, test_outputs)
return RegressionMetric(predictions, test_outputs)
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):
class RandomForrest:
def random_forest_classifier(self, 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)
return kernel, ClassificationMetrics(predictions, labels_test)
return kernel, ClassificationMetric(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):
def random_forest_regressor(self, 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(data, 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)
return kernel, RegressionMetrics(predictions, outputs_test)
return kernel, RegressionMetric(predictions, outputs_test)
class CorrelationTests:
class CorrelationTest:
def anova_oneway(self, *args): #expects arrays of samples
@ -756,7 +765,7 @@ class CorrelationTests:
results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value
class StatisticalTests:
class StatisticalTest:
def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'):