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)