def part3(): dim = 5 rp = SparseRandomProjection(n_components=dim, random_state=5) cancer_x2 = rp.fit_transform(cancer_x) dim = 9 rp = SparseRandomProjection(n_components=dim, random_state=5) housing_x2 = rp.fit_transform(housing_x) run_clustering(out, cancer_x2, cancer_y, housing_x2, housing_y)
def part3(): dim = 6 pca = PCA(n_components=dim, random_state=10) cancer_x2 = pca.fit_transform(cancer_x) dim = 9 pca = PCA(n_components=dim, random_state=10) housing_x2 = pca.fit_transform(housing_x) run_clustering(out, cancer_x2, cancer_y, housing_x2, housing_y)
tmp = pd.DataFrame(gs.cv_results_) tmp.to_csv(out + 'perm dim red.csv') grid = { 'ica__n_components': dims_big, 'NN__alpha': nn_reg, 'NN__hidden_layer_sizes': nn_layers } ica = FastICA(random_state=5) mlp = MLPClassifier(activation='relu', max_iter=nn_iter, early_stopping=True, random_state=5) pipe = Pipeline([('ica', ica), ('NN', mlp)]) gs = GridSearchCV(pipe, grid, verbose=10, cv=5) gs.fit(housing_x, housing_y) tmp = pd.DataFrame(gs.cv_results_) tmp.to_csv(out + 'housing dim red.csv') #3 dim = 5 ica = FastICA(n_components=dim, random_state=10) perm_x2 = ica.fit_transform(perm_x) dim = 9 ica = FastICA(n_components=dim, random_state=10) housing_x2 = ica.fit_transform(housing_x) run_clustering(out, perm_x2, perm_y, housing_x2, housing_y)
grid ={'filter__n':dims,'NN__alpha':nn_reg,'NN__hidden_layer_sizes':nn_layers} mlp = MLPClassifier(activation='relu',max_iter=nn_iter,early_stopping=True,random_state=5) pipe = Pipeline([('filter',filtr),('NN',mlp)]) gs = GridSearchCV(pipe,grid,verbose=10,cv=5) gs.fit(cancer_x,cancer_y) tmp = pd.DataFrame(gs.cv_results_) tmp.to_csv(out+'cancer part 4.csv') grid ={'filter__n':dims_big,'NN__alpha':nn_reg,'NN__hidden_layer_sizes':nn_layers} mlp = MLPClassifier(activation='relu',max_iter=nn_iter,early_stopping=True,random_state=5) pipe = Pipeline([('filter',filtr),('NN',mlp)]) gs = GridSearchCV(pipe,grid,verbose=10,cv=5) gs.fit(housing_x,housing_y) tmp = pd.DataFrame(gs.cv_results_) tmp.to_csv(out+'housing part 4.csv') #3 dim = 7 filtr = ImportanceSelect(rfc,dim) cancer_x2 = filtr.fit_transform(cancer_x,cancer_y) dim = 7 filtr = ImportanceSelect(rfc,dim) housing_x2 = filtr.fit_transform(housing_x,housing_y) run_clustering(out, cancer_x2, cancer_y, housing_x2, housing_y)