grid search

ecust201810 · Jul 27, 2018 · f62a16c · f62a16c
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diff --git a/...inear-Data-With-Noise/points_leftside.txt → ...Linear-Data-With-Noise/points_class_0.txt b/...inear-Data-With-Noise/points_leftside.txt → ...Linear-Data-With-Noise/points_class_0.txt
diff --git a/...near-Data-With-Noise/points_rightside.txt → ...Linear-Data-With-Noise/points_class_1.txt b/...near-Data-With-Noise/points_rightside.txt → ...Linear-Data-With-Noise/points_class_1.txt
diff --git a/SVM-using-Python/Non-Linear-Data/classification/classify-with-grid-search.py b/SVM-using-Python/Non-Linear-Data/classification/classify-with-grid-search.py
diff --git a/...ing-Python/Non-Linear-Data/classification/decision-function-on-testing-data.png b/...ing-Python/Non-Linear-Data/classification/decision-function-on-testing-data.png
diff --git a/...ng-Python/Non-Linear-Data/classification/decision-function-on-training-data.png b/...ng-Python/Non-Linear-Data/classification/decision-function-on-training-data.png
diff --git a/...on-Linear-Data/classification/grid-search-decision-function-on-testing-data.png b/...on-Linear-Data/classification/grid-search-decision-function-on-testing-data.png
diff --git a/SVM-using-Python/Non-Linear-Data/classification/non-linear-train-data.png b/SVM-using-Python/Non-Linear-Data/classification/non-linear-train-data.png
diff --git a/...ython/Non-Linear-Data/points_leftside.txt → ...Python/Non-Linear-Data/points_class_0.txt b/...ython/Non-Linear-Data/points_leftside.txt → ...Python/Non-Linear-Data/points_class_0.txt
diff --git a/...thon/Non-Linear-Data/points_rightside.txt → ...Python/Non-Linear-Data/points_class_1.txt b/...thon/Non-Linear-Data/points_rightside.txt → ...Python/Non-Linear-Data/points_class_1.txt
diff --git a/SVM-using-Python/Non-Linear-Data/svm-classify-with-gridsearch.py b/SVM-using-Python/Non-Linear-Data/svm-classify-with-gridsearch.py
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+import sys, os
+import matplotlib.pyplot as plt
+from sklearn import svm
+from sklearn.model_selection import train_test_split, GridSearchCV
+
+
+sys.path.append(os.path.abspath("../"))
+from utils import read_data, plot_data, plot_decision_function
+
+# Read data
+x, labels = read_data("points_class_0.txt", "points_class_1.txt")
+
+# Split data to train and test on 80-20 ratio
+X_train, X_test, y_train, y_test = train_test_split(x, labels, test_size = 0.2, random_state=0)
+
+print("Displaying data. Close window to continue")
+# Plot data 
+plot_data(X_train, y_train, X_test, y_test)
+
+print("Training SVM ...")
+# make a classifier
+clf = svm.SVC(C = 10.0, kernel='rbf', gamma=0.1)
+
+# Train classifier
+clf.fit(X_train, y_train)
+
+# Make predictions on unseen test data
+clf_predictions = clf.predict(X_test)
+
+print("Displaying decision function. Close window to continue")
+# Plot decision function on training and test data
+plot_decision_function(X_train, y_train, X_test, y_test, clf)
+
+# Grid Search
+print("Performing grid search ... ")
+
+# Parameter Grid
+param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001, 0.00001, 10]}
+
+# Make grid search classifier
+clf_grid = GridSearchCV(svm.SVC(), param_grid, verbose=1)
+
+# Train the classifier
+clf_grid.fit(X_train, y_train)
+
+# clf = grid.best_estimator_()
+print("Best Parameters:\n", clf_grid.best_params_)
+print("Best Estimators:\n", clf_grid.best_estimator_)
+
+print("Displaying decision function for best estimator. Close window to continue.")
+# Plot decision function on training and test data
+plot_decision_function(X_train, y_train, X_test, y_test, clf_grid)