import sys sys.path.append("E:/New Folder/utils") import classification_utils as cutils from sklearn import model_selection, ensemble, tree, neighbors import xgboost as xgb #2-d classification pattern X, y = cutils.generate_linear_synthetic_data_classification(n_samples=1000, n_features=2, n_classes=2, weights=[0.5, 0.5], class_sep=2) X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1) cutils.plot_data_2d_classification(X, y) X_train, X_test, y_train, y_test = model_selection.train_test_split( X, y, test_size=0.2, random_state=1) cutils.plot_data_2d_classification(X_train, y_train) #grid search for parameter values dt_estimator = tree.DecisionTreeClassifier() dt_grid = {'criterion': ['gini', 'entropy'], 'max_depth': list(range(1, 9))} final_estimator = cutils.grid_search_best_model(dt_estimator, dt_grid, X_train, y_train) cutils.plot_model_2d_classification(final_estimator, X_train, y_train) knn_estimator = neighbors.KNeighborsClassifier() knn_grid = { 'n_neighbors': list(range(1, 21)),
import sys path = 'J://utils' sys.path.append(path) from sklearn import cluster, manifold import common_utils as utils import clustering_utils as cl_utils import classification_utils as cutils X, _ = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=300) utils.plot_data_2d(X) X, _ = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=300) utils.plot_data_2d(X) tsne = manifold.TSNE() X_tsne = tsne.fit_transform(X) utils.plot_data_2d(X_tsne) scoring = 's_score' kmeans_estimator = cluster.KMeans() kmeans_grid = {'n_clusters': list(range(2, 7))} kmeans_final_model = cl_utils.grid_search_best_model_clustering( kmeans_estimator, kmeans_grid, X, scoring=scoring) print(kmeans_final_model.labels_) print(kmeans_final_model.cluster_centers_) cl_utils.plot_model_2d_clustering(kmeans_final_model, X)
import pandas as pd import numpy as np import matplotlib.pyplot as plt X, y = generate_linear_synthetic_data_classification(n_samples=1000, n_features=3, n_redundant=0, n_classes=3, weights=[.3,.3,.4]) plot_data_3d(X) X_lpca = feature_reduction_linear_pca(X, 2) plot_data_2d(X_lpca, new_window=True) X_kpca = feature_reduction_kernel_pca(X, 2) plot_data_2d(X_kpca, new_window=True) X_tsne = feature_reduction_tsne(X, 2) plot_data_2d(X_tsne, new_window=True) X_isomap = feature_reduction_isomap(X, 2) plot_data_2d(X_isomap, new_window=True) X, y = generate_nonlinear_synthetic_data_classification2(n_samples=1000) plot_data_2d(X) X_lpca = feature_reduction_linear_pca(X, 2) plot_data_2d(X_lpca, new_window=True) X_kpca = feature_reduction_kernel_pca(X, 2, 'rbf', 15) plot_data_2d(X_kpca, new_window=True) X_tsne = feature_reduction_tsne(X, 2) plot_data_2d(X_tsne, new_window=True) X_isomap = feature_reduction_isomap(X, 2, 100) plot_data_2d(X_isomap, new_window=True) X, y = generate_linear_synthetic_data_regression(n_samples=100, n_features=2, n_informative=2, noise=0) plot_data_2d(X) X_lpca = feature_reduction_linear_pca(X, 2) plot_data_2d(X_lpca, new_window=True) X_kpca = feature_reduction_kernel_pca(X, 2, 'rbf', 15)