def createRidgeRegressor(params=None): info("Creating Ridge Regressor", ind=4) ## Params params = mergeParams(RidgeCV(), params) params = mergeParams(Ridge(), params) ## Estimator if params.get('cv') is True: info("Using Built-In Cross Validation With Parameters", ind=4) tuneParams = getRidgeRegressorParams(cv=True) grid = tuneParams['grid'] alphas = setParam('alphas', params, grid, force=True) info("Param: alphas = {0}".format(alphas), ind=6) reg = RidgeCV(alphas=alphas) else: info("With Parameters", ind=4) tuneParams = getRidgeRegressorParams(cv=False) grid = tuneParams['grid'] alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) reg = Ridge(alpha=alpha) return {"estimator": reg, "params": tuneParams}
def createElasticNetRegressor(params=None): info("Creating ElasticNet Regressor", ind=4) ## Params params = mergeParams(ElasticNetCV(), params) params = mergeParams(ElasticNet(), params) ## Estimator if params.get('cv') is True: info("Using Built-In Cross Validation With Parameters", ind=4) tuneParams = getElasticNetRegressorParams(cv=True) grid = tuneParams['grid'] alphas = setParam('alphas', params, grid, force=True) info("Param: alphas = {0}".format(alphas), ind=6) l1_ratio = setParam('l1_ratio', params, grid, force=True) info("Param: l1_ratio = {0}".format(l1_ratio), ind=6) reg = ElasticNetCV(alphas=alphas, l1_ratio=l1_ratio) else: info("With Parameters", ind=4) tuneParams = getElasticNetRegressorParams(cv=False) grid = tuneParams['grid'] alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) l1_ratio = setParam('l1_ratio', params, grid, force=False) info("Param: l1_ratio = {0}".format(l1_ratio), ind=6) reg = ElasticNet(alpha=alpha, l1_ratio=l1_ratio) return {"estimator": reg, "params": tuneParams}
def createLogisticRegressionClassifier(params=None): info("Creating Logistic Regression Classifier", ind=4) ## Params params = mergeParams(LogisticRegression(), params) params = mergeParams(LogisticRegressionCV(), params) tuneParams = getLogisticRegressionClassifer() grid = tuneParams['grid'] ## Estimator if params.get('cv'): info("Using Built-In Cross Validation With Parameters", ind=4) tuneParams = getLogisticRegressionClassifer(cv=True) grid = tuneParams['grid'] Cs = setParam('Cs', params, grid, force=True) info("Param: Cs = {0}".format(Cs), ind=6) penalty = setParam('penalty', params, grid, force=True) info("Param: penalty = {0}".format(penalty), ind=6) solver = setParam('solver', params, grid, force=False) info("Param: solver = {0}".format(solver), ind=6) #n_jobs = -1 #info("Param: n_jobs = {0}".format(n_jobs), ind=6) clf = LogisticRegressionCV(Cs=Cs, penalty=penalty, solver=solver) else: info("With Parameters", ind=4) tuneParams = getLogisticRegressionClassifer(cv=False) grid = tuneParams['grid'] C = setParam('C', params, grid, force=False) info("Param: C = {0}".format(C), ind=6) penalty = setParam('penalty', params, grid, force=False) info("Param: penalty = {0}".format(penalty), ind=6) solver = setParam('solver', params, grid, force=False) info("Param: solver = {0}".format(solver), ind=6) #n_jobs = -1 #info("Param: n_jobs = {0}".format(n_jobs), ind=6) clf = LogisticRegression(C=C, penalty=penalty, solver=solver) return {"estimator": clf, "params": tuneParams}
def createPassiveAggressiveClassifier(params): info("Creating Passive Aggressive Classifier", ind=4) error("Does not give probabilities.") return {"estimator": None, "params": None} ## Params params = mergeParams(PassiveAggressiveClassifier(), params) tuneParams = getPassiveAggressiveClassifierParams() grid = tuneParams['grid'] info("With Parameters", ind=4) C = setParam('C', params, grid, force=False) info("Param: C = {0}".format(C), ind=6) loss = setParam('loss', params, grid, force=False) info("Param: loss = {0}".format(loss), ind=6) max_iter = setParam('max_iter', params, grid, force=False) info("Param: max_iter = {0}".format(max_iter), ind=6) tol = setParam('tol', params, grid, force=False) info("Param: tol = {0}".format(tol), ind=6) ## Estimator clf = PassiveAggressiveClassifier(C=C, loss=loss, max_iter=max_iter, tol=tol) return {"estimator": clf, "params": tuneParams}
def createSVMEpsRbfClassifier(params=None): info("Creating SVM Epsilon Rbf Classifier", ind=4) ## Params params = mergeParams(SVC(), params) kernel = 'rbf' tuneParams = getSVMEpsClassifierParams(kernel) grid = tuneParams['grid'] info("With Parameters", ind=4) C = setParam('C', params, grid, force=False) info("Param: C = {0}".format(C), ind=6) info("Param: kernel = {0}".format(kernel), ind=6) gamma = setParam('gamma', params, grid, force=False) info("Param: gamma = {0}".format(gamma), ind=6) probability = True info("Param: probability = {0}".format(probability), ind=6) ## estimator reg = SVC(C=C, probability=probability, gamma=gamma, kernel=kernel) return {"estimator": reg, "params": tuneParams}
def createSVMNuRbfClassifier(params=None): info("Creating SVM Nu Rbf Classifier", ind=4) ## Params params = mergeParams(NuSVC(), params) kernel = 'rbf' tuneParams = getSVMNuClassifierParams(kernel) grid = tuneParams['grid'] info("With Parameters", ind=4) nu = setParam('nu', params, grid, force=False) info("Param: nu = {0}".format(nu), ind=6) info("Param: kernel = {0}".format(kernel), ind=6) gamma = setParam('gamma', params, grid, force=False) info("Param: gamma = {0}".format(gamma), ind=6) probability = True info("Param: probability = {0}".format(probability), ind=6) ## estimator reg = NuSVC(kernel=kernel, nu=nu, probability=probability, gamma=gamma) return {"estimator": reg, "params": tuneParams}
def createKernelRidgeRegressor(params=None): info("Creating SVM Regressor", ind=4) ## Params params = mergeParams(KernelRidge(), params) tuneParams = getKernelRidgeRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) coef0 = setParam('coef0', params, grid, force=False) info("Param: coef0 = {0}".format(coef0), ind=6) degree = setParam('degree', params, grid, force=False) info("Param: degree = {0}".format(degree), ind=6) kernel = setParam('kernel', params, grid, force=False) info("Param: kernel = {0}".format(kernel), ind=6) ## estimator reg = KernelRidge(alpha=alpha, coef0=coef0, degree=degree, kernel=kernel) return {"estimator": reg, "params": tuneParams}
def createARDRegressor(params=None): info("Creating ARD Regressor", ind=4) ## Params params = mergeParams(ARDRegression(), params) tuneParams = getARDRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) alpha_1 = setParam('alpha_1', params, grid, force=False) info("Param: alpha_1 = {0}".format(alpha_1), ind=6) lambda_1 = setParam('lambda_1', params, grid, force=False) info("Param: lambda_1 = {0}".format(lambda_1), ind=6) alpha_2 = setParam('alpha_2', params, grid, force=False) info("Param: alpha_2 = {0}".format(alpha_2), ind=6) lambda_2 = setParam('lambda_2', params, grid, force=False) info("Param: lambda_2 = {0}".format(lambda_2), ind=6) ## estimator reg = ARDRegression(alpha_1=alpha_1, alpha_2=alpha_2, lambda_1=lambda_1, lambda_2=lambda_2) return {"estimator": reg, "params": tuneParams}
def createHuberRegressor(params): info("Creating Huber Regressor", ind=4) ## Params params = mergeParams(HuberRegressor(), params) tuneParams = getHuberRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) epsilon = setParam('epsilon', params, grid, force=False) info("Param: epsilon = {0}".format(epsilon), ind=6) max_iter = setParam('max_iter', params, grid, force=False) info("Param: max_iter = {0}".format(max_iter), ind=6) tol = setParam('tol', params, grid, force=False) info("Param: tol = {0}".format(tol), ind=6) # estimator reg = HuberRegressor(alpha=alpha, epsilon=epsilon, max_iter=max_iter, tol=tol) return {"estimator": reg, "params": tuneParams}
def createSVMNuRbfRegressor(params=None): info("Creating SVM Nu Rbf Regressor", ind=4) ## Params params = mergeParams(NuSVR(), params) kernel = 'rbf' tuneParams = getSVMNuRegressorParams(kernel) grid = tuneParams['grid'] info("With Parameters", ind=4) C = setParam('C', params, grid, force=False) info("Param: C = {0}".format(C), ind=6) nu = setParam('nu', params, grid, force=False) info("Param: nu = {0}".format(nu), ind=6) info("Param: kernel = {0}".format(kernel), ind=6) gamma = setParam('gamma', params, grid, force=False) info("Param: gamma = {0}".format(gamma), ind=6) ## estimator reg = NuSVR(C=C, kernel=kernel, nu=nu, gamma=gamma) return {"estimator": reg, "params": tuneParams}
def createRadiusNeighborsRegressor(params=None): info("Creating Radius Neighbors Regressor", ind=4) error("This doesn't work") return {"estimator": None, "params": None} ## Params params = mergeParams(RadiusNeighborsRegressor(), params) tuneParams = getRadiusNeighborsRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) algorithm = setParam('algorithm', params, grid, force=False) info("Param: algorithm = {0}".format(algorithm), ind=6) leaf_size = setParam('leaf_size', params, grid, force=False) info("Param: leaf_size = {0}".format(leaf_size), ind=6) metric = setParam('metric', params, grid, force=False) info("Param: metric = {0}".format(metric), ind=6) radius = setParam('radius', params, grid, force=False) info("Param: radius = {0}".format(radius), ind=6) weights = setParam('weights', params, grid, force=False) info("Param: weights = {0}".format(weights), ind=6) ## Estimator reg = RadiusNeighborsRegressor(algorithm=algorithm, leaf_size=leaf_size, metric=metric, radius=radius, weights=weights) return {"estimator": reg, "params": tuneParams}
def createPassiveAggressiveRegressor(params): info("Creating Passive Aggressive Regressor", ind=4) ## Params params = mergeParams(PassiveAggressiveRegressor(), params) tuneParams = getPassiveAggressiveRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) C = setParam('C', params, grid, force=False) info("Param: C = {0}".format(C), ind=6) loss = setParam('loss', params, grid, force=False) info("Param: loss = {0}".format(loss), ind=6) max_iter = setParam('max_iter', params, grid, force=False) info("Param: max_iter = {0}".format(max_iter), ind=6) tol = setParam('tol', params, grid, force=False) info("Param: tol = {0}".format(tol), ind=6) ## Estimator reg = PassiveAggressiveRegressor(C=C, loss=loss, max_iter=max_iter, tol=tol) return {"estimator": reg, "params": tuneParams}
def createSVMEpsRbfRegressor(params=None): info("Creating SVM Epsilon Rbf Regressor", ind=4) ## Params params = mergeParams(SVR(), params) kernel = 'rbf' tuneParams = getSVMEpsRegressorParams(kernel) grid = tuneParams['grid'] info("With Parameters", ind=4) C = setParam('C', params, grid, force=False) info("Param: C = {0}".format(C), ind=6) epsilon = setParam('epsilon', params, grid, force=False) info("Param: epsilon = {0}".format(epsilon), ind=6) info("Param: kernel = {0}".format(kernel), ind=6) gamma = setParam('gamma', params, grid, force=False) info("Param: gamma = {0}".format(gamma), ind=6) ## estimator reg = SVR(C=C, epsilon=epsilon, gamma=gamma, kernel=kernel) return {"estimator": reg, "params": tuneParams}
def createLDAClassifier(params = None): info("Creating LDA Classifier", ind=4) ## Params params = mergeParams(LinearDiscriminantAnalysis(), params) tuneParams = getLinearDiscriminantAnalysisParams() grid = tuneParams['grid'] info("With Parameters", ind=6) n_components = setParam('n_components', params, grid) info("Param: n_components = {0}".format(n_components), ind=6) solver = setParam('solver', params, grid) info("Param: solver = {0}".format(solver), ind=6) shrinkage = setParam('shrinkage', params, grid) info("Param: shrinkage = {0}".format(shrinkage), ind=6) ## Estimator clf = LinearDiscriminantAnalysis(n_components=n_components, solver=solver, shrinkage=shrinkage) return {"estimator": clf, "params": tuneParams}
def createOrthogonalMatchingPursuitRegressor(params=None): info("Creating Orthogonal Matching Pursuit Regressor", ind=4) ## Params params = mergeParams(OrthogonalMatchingPursuit(), params) params = mergeParams(OrthogonalMatchingPursuitCV(), params) tuneParams = getOrthogonalMatchingPursuitRegressorParams() ## estimator if params.get('cv') is True: info("Using Built-In Cross Validation With Parameters", ind=4) reg = OrthogonalMatchingPursuitCV() else: info("Without Parameters", ind=4) reg = OrthogonalMatchingPursuit() return {"estimator": reg, "params": tuneParams}
def createMLPRegressor(params=None): info("Creating MLP Regressor", ind=4) ## Params params = mergeParams(MLPRegressor(), params) tuneParams = getMLPRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) activation = setParam('activation', params, grid, force=False) info("Param: activation = {0}".format(activation), ind=6) alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) beta_1 = setParam('beta_1', params, grid, force=False) info("Param: beta_1 = {0}".format(beta_1), ind=6) beta_2 = setParam('beta_2', params, grid, force=False) info("Param: beta_2 = {0}".format(beta_2), ind=6) hidden_layer_sizes = setParam('hidden_layer_sizes', params, grid, force=False) info("Param: hidden_layer_sizes = {0}".format(hidden_layer_sizes), ind=6) learning_rate = setParam('learning_rate', params, grid, force=False) info("Param: learning_rate = {0}".format(learning_rate), ind=6) max_iter = setParam('max_iter', params, grid, force=False) info("Param: max_iter = {0}".format(max_iter), ind=6) momentum = setParam('momentum', params, grid, force=False) info("Param: momentum = {0}".format(momentum), ind=6) power_t = setParam('power_t', params, grid, force=False) info("Param: power_t = {0}".format(power_t), ind=6) solver = setParam('solver', params, grid, force=False) info("Param: solver = {0}".format(solver), ind=6) reg = MLPRegressor(activation=activation, alpha=alpha, beta_1=beta_1, beta_2=beta_2, hidden_layer_sizes=hidden_layer_sizes, learning_rate=learning_rate, max_iter=max_iter, momentum=momentum, power_t=power_t, solver=solver) return {"estimator": reg, "params": tuneParams}
def createIsotonicRegressor(params=None): info("Creating Isotonic Regressor", ind=4) ## Params params = mergeParams(IsotonicRegression(), params) tuneParams = getIsotonicRegressionParams() info("Without Parameters", ind=4) reg = IsotonicRegression(increasing="auto") return {"estimator": reg, "params": tuneParams}
def createRANSACRegressor(params): info("Creating TheilSen Regressor", ind=4) ## Params params = mergeParams(RANSACRegressor(), params) tuneParams = getRANSACRegressorParams() info("Without Parameters", ind=4) ## estimator reg = RANSACRegressor() return {"estimator": reg, "params": tuneParams}
def createLinearRegressor(params=None): info("Creating Linear Regressor", ind=4) ## Params params = mergeParams(LinearRegression(), params) tuneParams = getLinearRegressorParams() info("Without Parameters", ind=4) ## estimator reg = LinearRegression() return {"estimator": reg, "params": tuneParams}
def createEARTHRegressor(params=None): info("Creating EARTH Regressor", ind=4) ## Params params = mergeParams(Earth(), params) tuneParams = getEarthParams() info("Without Parameters", ind=4) # Estimator reg = Earth() return {"estimator": reg, "params": tuneParams}
def createGaussianNaiveBayesClassifier(params): info("Creating Gaussian Naive Bayes Classifier", ind=4) ## Params params = mergeParams(GaussianNB(), params) tuneParams = getGaussianNaiveBayesClassifierParams() info("Without Parameters", ind=4) ## Estimator clf = GaussianNB() return {"estimator": clf, "params": tuneParams}
def createSGDRegressor(params): info("Creating SGD Regressor", ind=4) ## Params params = mergeParams(SGDRegressor(), params) tuneParams = getSGDRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) epsilon = setParam('epsilon', params, grid, force=False) info("Param: epsilon = {0}".format(epsilon), ind=6) eta0 = setParam('eta0', params, grid, force=False) info("Param: eta0 = {0}".format(eta0), ind=6) l1_ratio = setParam('l1_ratio', params, grid, force=False) info("Param: l1_ratio = {0}".format(l1_ratio), ind=6) learning_rate = setParam('learning_rate', params, grid, force=False) info("Param: learning_rate = {0}".format(learning_rate), ind=6) loss = setParam('loss', params, grid, force=False) info("Param: loss = {0}".format(loss), ind=6) max_iter = setParam('max_iter', params, grid, force=False) info("Param: max_iter = {0}".format(max_iter), ind=6) penalty = setParam('penalty', params, grid, force=False) info("Param: penalty = {0}".format(penalty), ind=6) power_t = setParam('power_t', params, grid, force=False) info("Param: power_t = {0}".format(power_t), ind=6) tol = setParam('tol', params, grid, force=False) info("Param: tol = {0}".format(tol), ind=6) ## Estimator reg = SGDRegressor(alpha=alpha, epsilon=epsilon, eta0=eta0, l1_ratio=l1_ratio, learning_rate=learning_rate, loss=loss, penalty=penalty, power_t=power_t) return {"estimator": reg, "params": tuneParams}
def createDecisionTreeClassifier(params): info("Creating Decision Tree Classifier", ind=4) ## Params params = mergeParams(DecisionTreeClassifier(), params) tuneParams = getDecisionTreeClassifierParams() grid = tuneParams['grid'] info("With Parameters", ind=6) criterion = setParam('criterion', params, grid) info("Param: criterion = {0}".format(criterion), ind=6) max_depth = setParam('max_depth', params, grid) info("Param: max_depth = {0}".format(max_depth), ind=6) max_features = setParam('max_features', params, grid) info("Param: max_features = {0}".format(max_features), ind=6) max_leaf_nodes = setParam('max_leaf_nodes', params, grid) info("Param: max_leaf_nodes = {0}".format(max_leaf_nodes), ind=6) min_impurity_decrease = setParam('min_impurity_decrease', params, grid) info("Param: min_impurity_decrease = {0}".format(min_impurity_decrease), ind=6) min_samples_leaf = setParam('min_samples_leaf', params, grid) info("Param: min_samples_leaf = {0}".format(min_samples_leaf), ind=6) min_samples_split = setParam('min_samples_split', params, grid) info("Param: min_samples_split = {0}".format(min_samples_split), ind=6) min_weight_fraction_leaf = setParam('min_weight_fraction_leaf', params, grid) info("Param: min_weight_fraction_leaf = {0}".format( min_weight_fraction_leaf), ind=6) ## Estimator reg = DecisionTreeClassifier( criterion=criterion, max_depth=max_depth, max_features=max_features, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, min_samples_leaf=min_samples_leaf, min_samples_split=min_samples_split, min_weight_fraction_leaf=min_weight_fraction_leaf) return {"estimator": reg, "params": tuneParams}
def createGaussianProcessClassifier(params=None): info("Creating Gaussian Process Classifier", ind=4) error("This takes forever. Don't use it") return {"estimator": None, "params": None} ## Params params = mergeParams(GaussianProcessClassifier(), params) tuneParams = getGaussianProcessClassifierParams() info("Without Parameters", ind=4) kernel = kernels.ConstantKernel() ## Estimator reg = GaussianProcessClassifier(kernel=kernel) return {"estimator": reg, "params": tuneParams}
def createBernoulliNaiveBayesClassifier(params): info("Creating Bernoulli Naive Bayes Classifier", ind=4) ## Params params = mergeParams(BernoulliNB(), params) tuneParams = getBernoulliNaiveBayesClassifierParams() grid = tuneParams['grid'] info("With Parameters", ind=4) alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) ## Estimator clf = BernoulliNB(alpha=alpha) return {"estimator": clf, "params": tuneParams}
def createGBMRegressor(params): info("Creating GBM Regressor", ind=4) ## Params params = mergeParams(GradientBoostingRegressor(), params) tuneParams = getGradientBoostingRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=6) criterion = setParam('criterion', params, grid) info("Param: criterion = {0}".format(criterion), ind=6) learning_rate = setParam('learning_rate', params, grid) info("Param: learning_rate = {0}".format(learning_rate), ind=6) loss = setParam('loss', params, grid) info("Param: loss = {0}".format(loss), ind=6) max_depth = setParam('max_depth', params, grid) info("Param: max_depth = {0}".format(max_depth), ind=6) max_features = setParam('max_features', params, grid) info("Param: max_features = {0}".format(max_features), ind=6) min_impurity_decrease = setParam('min_impurity_decrease', params, grid) info("Param: min_impurity_decrease = {0}".format(min_impurity_decrease), ind=6) min_samples_leaf = setParam('min_samples_leaf', params, grid) info("Param: min_samples_leaf = {0}".format(min_samples_leaf), ind=6) n_estimators = setParam('n_estimators', params, grid) info("Param: n_estimator = {0}".format(n_estimators), ind=6) ## Estimator reg = GradientBoostingRegressor( criterion=criterion, learning_rate=learning_rate, loss=loss, max_depth=max_depth, max_features=max_features, min_impurity_decrease=min_impurity_decrease, min_samples_leaf=min_samples_leaf, n_estimators=n_estimators) return {"estimator": reg, "params": tuneParams}
def createRandomForestRegressor(params): info("Creating Random Forest Regressor", ind=4) ## Params params = mergeParams(RandomForestRegressor(), params) tuneParams = getRandomForestRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=6) bootstrap = setParam('bootstrap', params, grid) info("Param: bootstrap = {0}".format(bootstrap), ind=6) criterion = setParam('criterion', params, grid) info("Param: criterion = {0}".format(criterion), ind=6) max_depth = setParam('max_depth', params, grid) info("Param: max_depth = {0}".format(max_depth), ind=6) max_features = setParam('max_features', params, grid) info("Param: max_features = {0}".format(max_features), ind=6) min_impurity_decrease = setParam('min_impurity_decrease', params, grid) info("Param: min_impurity_decrease = {0}".format(min_impurity_decrease), ind=6) min_samples_leaf = setParam('min_samples_leaf', params, grid) info("Param: min_samples_leaf = {0}".format(min_samples_leaf), ind=6) n_estimators = setParam('n_estimators', params, grid) info("Param: n_estimator = {0}".format(n_estimators), ind=6) n_jobs = setParam('n_jobs', params, grid) n_jobs = -1 info("Param: n_jobs = {0}".format(n_jobs), ind=6) ## Estimator reg = RandomForestRegressor(bootstrap=bootstrap, criterion=criterion, max_depth=max_depth, max_features=max_features, min_impurity_decrease=min_impurity_decrease, min_samples_leaf=min_samples_leaf, n_estimators=n_estimators, n_jobs=n_jobs) return {"estimator": reg, "params": tuneParams}
def createMultinomialNaiveBayesClassifier(params): info("Creating Multinomial Naive Bayes Classifier", ind=4) error("Multinomial Naive Bayes Classifier does not work", ind=4) return {"estimator": None, "params": None} ## Params params = mergeParams(MultinomialNB(), params) tuneParams = getMultinomialNaiveBayesClassifierParams() grid = tuneParams['grid'] info("With Parameters", ind=4) alpha = setParam('alpha', params, grid, force=False) info("Param: alpha = {0}".format(alpha), ind=6) ## Estimator clf = MultinomialNB(alpha=alpha) return {"estimator": clf, "params": tuneParams}
def createQDAClassifier(params = None): info("Creating QDA Classifier", ind=4) ## Params params = mergeParams(QuadraticDiscriminantAnalysis(), params) tuneParams = getQuadraticDiscriminantAnalysisParams() grid = tuneParams['grid'] info("With Parameters", ind=6) reg_param = setParam('reg_param', params, grid) info("Param: reg_param = {0}".format(reg_param), ind=6) ## Estimator clf = QuadraticDiscriminantAnalysis(reg_param=reg_param) return {"estimator": clf, "params": tuneParams}
def createSVMLinearRegressor(params=None): info("Creating SVM Linear Regressor", ind=4) ## Params params = mergeParams(LinearSVR(), params) tuneParams = getSVMLinearRegressorParams() grid = tuneParams['grid'] info("With Parameters", ind=4) C = setParam('C', params, grid, force=False) info("Param: C = {0}".format(C), ind=6) loss = setParam('loss', params, grid, force=False) info("Param: loss = {0}".format(loss), ind=6) ## estimator reg = LinearSVR(C=C, loss=loss) return {"estimator": reg, "params": tuneParams}