tmp.to_csv('./P5_Neural_Networks_Reduced_With_Clusters/letter_original.csv')
algo_name.append('original')
# Run NN on dimensionally reduced and original datasets with addition cluster dimension
for i in range(len(algo_name)):
#for i in range(4,5):
# load datasets
letter = pd.read_hdf('datasets.hdf', 'letter_' + algo_name[i])
letterX = letter.drop('Y', 1).copy().values
letterY = letter['Y'].copy().values
le = preprocessing.LabelEncoder()
letterY = le.fit_transform(letterY)
km = kmeans(random_state=5)
gmm = myGMM(random_state=5)
grid = {
'addClustKM__n_clusters': clusters,
'NN__learning_rate_init': nn_lr,
'NN__hidden_layer_sizes': nn_arch
}
mlp = MLPClassifier(max_iter=2000, early_stopping=True, random_state=5)
pipe = Pipeline([('addClustKM', appendClusterDimKM(cluster_algo=km)),
('NN', mlp)])
gs = GridSearchCV(pipe, grid, verbose=10, cv=5)
gs.fit(letterX, letterY)
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv('./P5_Neural_Networks_Reduced_With_Clusters/letter_km_' +
algo_name[i] + '.csv')
def main_logic():
out = './BASE/'
# change the below value based on the readme.txt file instructions
base = './BASE/'
np.random.seed(0)
madelon = pd.read_hdf(base + 'datasets.hdf', 'madelon')
madelon_X = madelon.drop('Class', 1).copy().values
madelon_Y = madelon['Class'].copy().values
character = pd.read_hdf(base + 'datasets.hdf', 'character')
character_X = character.drop('Class', 1).copy().values
character_Y = character['Class'].copy().values
np.random.seed(0)
# clusters = [2]
clusters = [2, 5, 10, 15, 20, 25, 30, 35, 40]
madelon_X = StandardScaler().fit_transform(madelon_X)
character_X = StandardScaler().fit_transform(character_X)
# Data for 1-3
SSE = defaultdict(dict)
ll = defaultdict(dict)
Silhouette_dict = defaultdict(dict)
acc = defaultdict(lambda: defaultdict(dict))
adjMI = defaultdict(lambda: defaultdict(dict))
km = kmeans(random_state=5)
gmm = GMM(random_state=5)
for j in clusters:
st = clock()
km.set_params(n_clusters=j)
gmm.set_params(n_components=j)
km.fit(madelon_X)
gmm.fit(madelon_X)
SSE[j]['Madelon'] = km.score(madelon_X)
ll[j]['Madelon'] = gmm.score(madelon_X)
test = km.predict(madelon_X)
acc[j]['Madelon']['Kmeans'] = cluster_acc(madelon_Y,
km.predict(madelon_X), j)
acc[j]['Madelon']['GMM'] = cluster_acc(madelon_Y,
gmm.predict(madelon_X))
adjMI[j]['Madelon']['Kmeans'] = ami(madelon_Y, km.predict(madelon_X))
adjMI[j]['Madelon']['GMM'] = ami(madelon_Y, gmm.predict(madelon_X))
print("Homogenity Score ,{}, Kmeans,".format(j),
hs(madelon_Y, km.labels_))
print("Completeness Score ,{} ,Kmeans,".format(j),
cs(madelon_Y, km.labels_))
label = km.labels_
gmmm = gmm.predict_proba(madelon_X)
sil_coeff = silhouette_score(madelon_X, label, metric='euclidean')
Silhouette_dict[j]['Madelon'] = sil_coeff
print("For n_clusters={}, The Silhouette Coefficient is {}".format(
j, sil_coeff))
km.fit(character_X)
gmm.fit(character_X)
SSE[j]['character'] = km.score(character_X)
ll[j]['character'] = gmm.score(character_X)
best = km.predict(character_X)
acc[j]['character']['Kmeans'] = cluster_acc(character_Y,
km.predict(character_X), j)
acc[j]['character']['GMM'] = cluster_acc(character_Y,
gmm.predict(character_X))
adjMI[j]['character']['Kmeans'] = ami(character_Y,
km.predict(character_X))
adjMI[j]['character']['GMM'] = ami(character_Y,
gmm.predict(character_X))
label = km.labels_
sil_coeff = silhouette_score(character_X, label, metric='euclidean')
Silhouette_dict[j]['character'] = sil_coeff
print(j, clock() - st)
print("Homogenity Score ,{}, Kmeans,".format(j),
hs(character_Y, km.labels_))
print("Completeness Score ,{} ,Kmeans,".format(j),
cs(character_Y, km.labels_))
print("For n_clusters={}, The Silhouette Coefficient is {}".format(
j, sil_coeff))
Silhouette_dict = pd.DataFrame(Silhouette_dict).to_csv(out +
'Silhouette.csv')
SSE = (-pd.DataFrame(SSE)).T
SSE.rename(columns=lambda x: x + ' SSE (left)', inplace=True)
ll = pd.DataFrame(ll).T
ll.rename(columns=lambda x: x + ' log-likelihood', inplace=True)
acc = pd.Panel(acc)
adjMI = pd.Panel(adjMI)
SSE.to_csv(out + 'SSE.csv')
ll.to_csv(out + 'logliklihood.csv')
acc.ix[:, :, 'character'].to_csv(out + 'character_acc.csv')
acc.ix[:, :, 'Madelon'].to_csv(out + 'Madelon acc.csv')
adjMI.ix[:, :, 'character'].to_csv(out + 'character_adjMI.csv')
adjMI.ix[:, :, 'Madelon'].to_csv(out + 'Madelon adjMI.csv')
# %% NN fit data (2,3)
grid = {
'km__n_clusters': clusters,
'NN__alpha': nn_reg,
'NN__hidden_layer_sizes': nn_arch
}
madelon = pd.read_hdf(base + 'datasets.hdf', 'madelon')
madelon_X = madelon.drop('Class', 1).copy().values
madelon_Y = madelon['Class'].copy().values
X_train, X_test, y_train, y_test = train_test_split(madelon_X,
madelon_Y,
test_size=0.3,
random_state=42)
np.random.seed(0)
for k in clusters:
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=5,
alpha=10**-5,
hidden_layer_sizes=(62, 62),
verbose=0)
km = kmeans(random_state=5, n_clusters=k)
pipe = Pipeline([('km', km), ('NN', mlp)])
# gs = GridSearchCV(pipe, grid, verbose=10)
tick = time.clock()
pipe.fit(X_train, y_train)
tock = time.clock() - tick
print("Traning time , {}, k means dataset".format(k), ',', tock)
tick = time.clock()
y_pred = pipe.predict(X_test)
tock = time.clock() - tick
print("Testing time , {}, k means component".format(k), ',', tock)
print("Accuracy Score , {}, kmeans Madelon".format(k), ',',
accuracy_score(y_test, y_pred))
grid = {'gmm__n_components': clusters}
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=5,
verbose=0,
alpha=10**-5,
hidden_layer_sizes=(62, 62))
gmm = myGMM(random_state=43, n_components=k)
pipe = Pipeline([('gmm', gmm), ('NN', mlp)])
# gs = GridSearchCV(pipe, grid, verbose=10, cv=5)
tick = time.clock()
pipe.fit(X_train, y_train)
tock = time.clock() - tick
print("Traning time , {}, gmm dataset".format(k), ',', tock)
tick = time.clock()
y_pred = pipe.predict(X_test)
tock = time.clock() - tick
print("Testing time , {}, gmm means component".format(k), ',', tock)
print("Accuracy Score , {}, gmm means Madelon".format(k), ',',
accuracy_score(y_test, y_pred))
grid = {
'km__n_clusters': clusters,
'NN__alpha': nn_reg,
'NN__hidden_layer_sizes': nn_arch
}
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=5)
km = kmeans(random_state=5)
pipe = Pipeline([('km', km), ('NN', mlp)])
gs = GridSearchCV(pipe, grid, verbose=10)
gs.fit(madelon_X, madelon_Y)
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(out + 'Madelon cluster Kmeans.csv')
grid = {
'gmm__n_components': clusters,
'NN__alpha': nn_reg,
'NN__hidden_layer_sizes': nn_arch
}
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=5)
gmm = myGMM(random_state=5)
pipe = Pipeline([('gmm', gmm), ('NN', mlp)])
gs = GridSearchCV(pipe, grid, verbose=10, cv=5)
gs.fit(madelon_X, madelon_Y)
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(out + 'Madelon cluster GMM.csv')
grid = {
'km__n_clusters': clusters,
'NN__alpha': nn_reg,
'NN__hidden_layer_sizes': nn_arch
}
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=5)
km = kmeans(random_state=5)
pipe = Pipeline([('km', km), ('NN', mlp)])
gs = GridSearchCV(pipe, grid, verbose=10, cv=5)
gs.fit(character_X, character_Y)
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(out + 'character_cluster_Kmeans.csv')
grid = {
'gmm__n_components': clusters,
'NN__alpha': nn_reg,
'NN__hidden_layer_sizes': nn_arch
}
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=5)
gmm = myGMM(random_state=5)
pipe = Pipeline([('gmm', gmm), ('NN', mlp)])
gs = GridSearchCV(pipe, grid, verbose=10, cv=5)
gs.fit(character_X, character_Y)
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(out + 'character_cluster_GMM.csv')
# %% For chart 4/5
madelonX2D = TSNE(verbose=10, random_state=5).fit_transform(madelon_X)
character_X2D = TSNE(verbose=10, random_state=5).fit_transform(character_X)
madelon2D = pd.DataFrame(np.hstack(
(madelonX2D, np.atleast_2d(madelon_Y).T)),
columns=['x', 'y', 'target'])
character2D = pd.DataFrame(np.hstack(
(character_X2D, np.atleast_2d(character_Y).T)),
columns=['x', 'y', 'target'])
madelon2D.to_csv(out + 'madelon2D.csv')
character2D.to_csv(out + 'character2D.csv')