def randproj(tx, ty, rx, ry):
print "randproj"
compressor = RandomProjection(tx[1].size)
compressor.fit(tx, y=ty)
newtx = compressor.transform(tx)
# compressor = RandomProjection(tx[1].size)
newrx = compressor.transform(rx)
em(newtx, ty, newrx, ry, add="wRPtr")
km(newtx, ty, newrx, ry, add="wRPtr")
nn(newtx, ty, newrx, ry, add="wRPtr")
print "randproj done"
def rp_analysis(self, X_train, X_test, y_train, y_test, data_set_name):
scl = RobustScaler()
X_train_scl = scl.fit_transform(X_train)
ks = []
for i in range(1000):
##
## Random Projection
##
rp = GaussianRandomProjection(n_components=X_train_scl.shape[1])
rp.fit(X_train_scl)
X_train_rp = rp.transform(X_train_scl)
ks.append(kurtosis(X_train_rp))
mean_k = np.mean(ks, 0)
##
## Plots
##
ph = plot_helper()
title = 'Kurtosis (Randomized Projection) for ' + data_set_name
name = data_set_name.lower() + '_rp_kurt'
filename = './' + self.out_dir + '/' + name + '.png'
ph.plot_simple_bar(np.arange(1, len(mean_k)+1, 1),
mean_k,
np.arange(1, len(mean_k)+1, 1).astype('str'),
'Feature Index',
'Kurtosis',
title,
filename)
def best_rp_nba(self):
dh = data_helper()
X_train, X_test, y_train, y_test = dh.get_nba_data()
scl = RobustScaler()
X_train_scl = scl.fit_transform(X_train)
X_test_scl = scl.transform(X_test)
rp = GaussianRandomProjection(n_components=X_train_scl.shape[1])
X_train_transformed = rp.fit_transform(X_train_scl, y_train)
X_test_transformed = rp.transform(X_test_scl)
## top 2
kurt = kurtosis(X_train_transformed)
i = kurt.argsort()[::-1]
X_train_transformed_sorted = X_train_transformed[:, i]
X_train_transformed = X_train_transformed_sorted[:,0:2]
kurt = kurtosis(X_test_transformed)
i = kurt.argsort()[::-1]
X_test_transformed_sorted = X_test_transformed[:, i]
X_test_transformed = X_test_transformed_sorted[:,0:2]
# save
filename = './' + self.save_dir + '/nba_rp_x_train.txt'
pd.DataFrame(X_train_transformed).to_csv(filename, header=False, index=False)
filename = './' + self.save_dir + '/nba_rp_x_test.txt'
pd.DataFrame(X_test_transformed).to_csv(filename, header=False, index=False)
filename = './' + self.save_dir + '/nba_rp_y_train.txt'
pd.DataFrame(y_train).to_csv(filename, header=False, index=False)
filename = './' + self.save_dir + '/nba_rp_y_test.txt'
pd.DataFrame(y_test).to_csv(filename, header=False, index=False)
def randproj(tx, ty, rx, ry):
compressor = RandomProjection(tx[1].size)
compressor.fit(tx, y=ty)
newtx = compressor.transform(tx)
newrx = compressor.transform(rx)
em(newtx, ty, newrx, ry, add="wRPtr", times=10)
km(newtx, ty, newrx, ry, add="wRPtr", times=10)
nn(newtx, ty, newrx, ry, add="wRPtr")
def find_best_rp(train_data, test_data, start_components, max_features):
# find random proj with lowest reconstruction error
best_c = 0
#scores = []
#best_k = 0
#best = 0
best_rs = 0
best_r = 20000
# go to max - 1 since it doesn't make sense to randomly project to the same dimension
r = range(start_components, max_features)
print(r)
# center data for reconstruction
scalar = StandardScaler(with_mean=True, with_std=False)
centered = scalar.fit_transform(test_data)
for c in r:
print('C=%d' % c)
for rs in range(1, 501):
rp = GaussianRandomProjection(n_components=c, random_state=rs).fit(train_data)
fit = rp.transform(centered)
recon = extmath.safe_sparse_dot(fit, rp.components_) + scalar.mean_
err = linalg.norm(test_data - recon)
if err < best_r:
best_r = err
best_c = c
best_rs = rs
print('best reconstruction error=%.4f' % best_r)
print('>>best rs=%d,c=%d' % (best_rs, best_c))
# for the best, track the variation
v_max = 0
errsum = 0
for rs in range(1, 501):
rp = GaussianRandomProjection(n_components=c, random_state=rs).fit(train_data)
fit = rp.transform(centered)
recon = extmath.safe_sparse_dot(fit, rp.components_) + scalar.mean_
err = linalg.norm(test_data - recon)
errsum += err
if err > v_max:
v_max = err
print('RP max:%.3f, avg:%.3f' % (v_max, errsum/500))
return best_c, best_rs
class ProjClassifier(BaseEstimator, ClassifierMixin):
def __init__(self, n_components=1, knn=100):
self.n_components = n_components
self.knn = knn
def fit(self, X, y, sample_weight=None):
self.classes_ = numpy.array([0, 1])
self.proj = GaussianRandomProjection(n_components=self.n_components)
# self.knner = KNeighborsClassifier(n_neighbors=self.knn)
self.knner = Knn1dClassifier(self.knn)
self.proj.fit(X)
X_new = self.proj.transform(X)
# TODO sample weight!!
self.knner.fit(X_new, y, sample_weight=sample_weight)
print('ok')
return self
def predict_proba(self, X):
X_new = self.proj.transform(X)
return self.knner.predict_proba(X_new)
def predict(self, X):
return numpy.argmax(self.predict_proba(X), axis=1)
#variances.
pc = pca.fit_transform(x)
plotgraph('PCA',pc)
#Nonlinear kernelPCA
from sklearn.decomposition import KernelPCA
kpca = KernelPCA(n_components = 2, kernel = 'rbf')
#kernel=linear,rbf(radial basis function),sigmoid,cosine
xkpca = kpca.fit_transform(x)
plotgraph('kernel pca',xkpca)
##Isomap
#It is a nonlinear dimensionality reduction method based on spectral theory that
#attempts to preserve geodetic distances in the lower dimension.
from sklearn.manifold import Isomap
isomap = Isomap(n_components=2)#n_jobs = 4
isomap.fit(x)
X_isomap = isomap.transform(x)
plotgraph('Isomap',X_isomap)
##Gaussian Random Projection
#data with a very large dimension (d) are projected in a two-dimensional space (kd)
# with a random matrix.
from sklearn.random_projection import GaussianRandomProjection
GRP = GaussianRandomProjection(n_components=2, random_state=20)
#random state is like seed to the function
GRP.fit(x)
X_grd = GRP.transform(x)
plotgraph('GRP',X_grd)
# In[44]:
ids = test.reset_index()['ID']
# In[45]:
from sklearn.decomposition import FactorAnalysis
from sklearn.random_projection import GaussianRandomProjection
from sklearn.random_projection import SparseRandomProjection
X_fa = fa.transform(test)
X_srp = srp.transform(test)
X_grp = grp.transform(test)
X_added = pd.concat([
pd.DataFrame(X_fa),
pd.DataFrame(X_srp),
pd.DataFrame(X_grp),
],
axis=1)
y_pred = gbm.predict(X_added)
y_pred
# In[46]:
y_pred = np.exp(y_pred) - 1
labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
plt.legend(lines, labels)
plt.title('visualization of data in 2D (rp)-> digits dataset')
plt.show()
r = np.array([7, 17, 37, 57, 77])
plt.figure()
for m in range(5):
x1 = []
# e1=[]
r1 = []
for i in range(4, 64, 10):
rp1 = GaussianRandomProjection(n_components=i, random_state=r[m])
rp1.fit(X)
X1 = rp1.transform(X)
print(X1.shape, rp1.components_.shape)
X2 = np.dot(X1, (rp1.components_))
rmse = np.sqrt(mean_squared_error(X, X2))
x1.append(i)
r1.append(rmse)
r1 = np.array(r1)
x1 = np.array(x1)
print(r1)
if m == 1:
plt.plot(x1, r1, color='r', label=1)
if m == 2:
plt.plot(x1, r1, color='g', label=2)
tsvd_results_test = tsvd.transform(test)
# PCA
pca = PCA(n_components=n_comp, random_state=420)
pca2_results_train = pca.fit_transform(train.drop(["y"], axis=1))
pca2_results_test = pca.transform(test)
# ICA
ica = FastICA(n_components=n_comp, random_state=420)
ica2_results_train = ica.fit_transform(train.drop(["y"], axis=1))
ica2_results_test = ica.transform(test)
# GRP
grp = GaussianRandomProjection(n_components=n_comp, eps=0.1, random_state=420)
grp_results_train = grp.fit_transform(train.drop(["y"], axis=1))
grp_results_test = grp.transform(test)
# SRP
srp = SparseRandomProjection(n_components=n_comp, dense_output=True, random_state=420)
srp_results_train = srp.fit_transform(train.drop(["y"], axis=1))
srp_results_test = srp.transform(test)
# Append decomposition components to datasets
for i in range(1, n_comp+1):
train['pca_' + str(i)] = pca2_results_train[:,i-1]
test['pca_' + str(i)] = pca2_results_test[:, i-1]
train['ica_' + str(i)] = ica2_results_train[:,i-1]
test['ica_' + str(i)] = ica2_results_test[:, i-1]
train['tsvd_' + str(i)] = tsvd_results_train[:,i-1]
df_ica = pd.DataFrame(ica.fit_transform(train), columns=columns)
df_test_ica = pd.DataFrame(ica.transform(test), columns=columns)
# Truncated SVD
columns = ['TSVD_{}'.format(i) for i in range(n_components)]
tsvd = TruncatedSVD(n_components=n_components, random_state=420)
df_tsvd = pd.DataFrame(tsvd.fit_transform(train), columns=columns)
df_test_tsvd = pd.DataFrame(tsvd.transform(test), columns=columns)
# GRP
columns = ['GRP_{}'.format(i) for i in range(n_components)]
grp = GaussianRandomProjection(n_components=n_components,
eps=0.1,
random_state=420)
df_grp = pd.DataFrame(grp.fit_transform(train), columns=columns)
df_test_grp = pd.DataFrame(grp.transform(test), columns=columns)
# SRP
columns = ['SRP_{}'.format(i) for i in range(n_components)]
srp = SparseRandomProjection(n_components=n_components,
dense_output=True,
random_state=420)
df_srp = pd.DataFrame(srp.fit_transform(train), columns=columns)
df_test_srp = pd.DataFrame(srp.transform(test), columns=columns)
train = pd.concat([train, df_pca, df_ica, df_tsvd, df_grp, df_srp], axis=1)
test = pd.concat(
[test, df_test_pca, df_test_ica, df_test_tsvd, df_test_grp, df_test_srp],
axis=1)
### Grid Search
start_time = time.time()
# Load the data
from income_data import X_train, X_test, y_train, y_test
# Scale the data
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
X_toTransform = X_train
# Reduce Dimensionality
projection = ProjectionAlgorithm(n_components=22)
X_transformed = projection.fit_transform(X_train)
X_testTransformed = projection.transform(X_test)
# Run em clustering with 2 clusters and plot
cluster = GaussianMixture(random_state=0, n_components=99).fit(X_transformed)
cluster_labels = cluster.predict(X_transformed)
X_transformed = np.dot(X_transformed, np.transpose(cluster.means_))
X_testTransformed = np.dot(X_testTransformed, np.transpose(cluster.means_))
# Define the classifier
nn = MLPClassifier(solver='lbfgs',
random_state=1,
alpha=0.005,
hidden_layer_sizes=3)
grid_params = {'alpha': [0.005], 'hidden_layer_sizes': [3]}
clf = GridSearchCV(nn, param_grid=grid_params, cv=3)
def DecomposedFeatures(train,
test,
val,
total,
addtrain,
addtest,
use_pca=0.0,
use_tsvd=0.0,
use_ica=0.0,
use_fa=0.0,
use_grp=0.0,
use_srp=0.0,
use_KPCA=0.0,
kernal="rbf"):
print("\nStart decomposition process...")
train_decomposed = []
test_decomposed = []
val_decomposed = []
if addtrain is not None:
train_decomposed = [addtrain]
val_decomposed = [addtrain]
if addtest is not None:
test_decomposed = [addtest]
if use_pca > 0.0:
print("PCA")
N_COMP = int(use_pca * train.shape[1]) + 1
pca = PCA(n_components=N_COMP,
whiten=True,
svd_solver="full",
random_state=42)
pca_results = pca.fit(total)
pca_results_train = pca.transform(train)
pca_results_test = pca.transform(test)
pca_results_val = pca.transform(val)
train_decomposed.append(pca_results_train)
test_decomposed.append(pca_results_test)
val_decomposed.append(pca_results_val)
if use_tsvd > 0.0:
print("tSVD")
N_COMP = int(use_tsvd * train.shape[1]) + 1
tsvd = TruncatedSVD(n_components=N_COMP, random_state=42)
tsvd_results = tsvd.fit(total)
tsvd_results_train = tsvd.transform(train)
tsvd_results_test = tsvd.transform(test)
tsvd_results_val = tsvd.transform(val)
train_decomposed.append(tsvd_results_train)
test_decomposed.append(tsvd_results_test)
val_decomposed.append(tsvd_results_val)
if use_ica > 0.0:
print("ICA")
N_COMP = int(use_ica * train.shape[1]) + 1
ica = FastICA(n_components=N_COMP, random_state=42)
ica_results = ica.fit(total)
ica_results_train = ica.transform(train)
ica_results_test = ica.transform(test)
ica_results_val = ica.transform(val)
train_decomposed.append(ica_results_train)
test_decomposed.append(ica_results_test)
val_decomposed.append(ica_results_val)
if use_fa > 0.0:
print("FA")
N_COMP = int(use_fa * train.shape[1]) + 1
fa = FactorAnalysis(n_components=N_COMP, random_state=42)
fa_results = fa.fit(total)
fa_results_train = fa.transform(train)
fa_results_test = fa.transform(test)
fa_results_val = fa.transform(val)
train_decomposed.append(fa_results_train)
test_decomposed.append(fa_results_test)
val_decomposed.append(fa_results_val)
if use_grp > 0.0 or use_grp < 0.0:
print("GRP")
if use_grp > 0.0:
N_COMP = int(use_grp * train.shape[1]) + 1
eps = 10
if use_grp < 0.0:
N_COMP = "auto"
eps = abs(use_grp)
grp = GaussianRandomProjection(n_components=N_COMP,
eps=eps,
random_state=42)
grp_results = grp.fit(total)
grp_results_train = grp.transform(train)
grp_results_test = grp.transform(test)
grp_results_val = grp.transform(val)
train_decomposed.append(grp_results_train)
test_decomposed.append(grp_results_test)
val_decomposed.append(grp_results_val)
if use_srp > 0.0:
print("SRP")
N_COMP = int(use_srp * train.shape[1]) + 1
srp = SparseRandomProjection(n_components=N_COMP,
dense_output=True,
random_state=42)
srp_results = srp.fit(total)
srp_results_train = srp.transform(train)
srp_results_test = srp.transform(test)
srp_results_val = pca.transform(val)
train_decomposed.append(srp_results_train)
test_decomposed.append(srp_results_test)
val_decomposed.append(srp_results_val)
if use_KPCA > 0.0:
print("KPCA")
N_COMP = int(use_KPCA * train.shape[1]) + 1
#N_COMP = None
pls = KernelPCA(n_components=N_COMP, kernel=kernal)
pls_results = pls.fit(total)
pls_results_train = pls.transform(train)
pls_results_test = pls.transform(test)
pls_results_val = pls.transform(val)
train_decomposed.append(pls_results_train)
test_decomposed.append(pls_results_test)
val_decomposed.append(pls_results_val)
gc.collect()
print("Append decomposition components together...")
train_decomposed = np.concatenate(train_decomposed, axis=1)
test_decomposed = np.concatenate(test_decomposed, axis=1)
val_decomposed = np.concatenate(val_decomposed, axis=1)
train_with_only_decomposed_features = pd.DataFrame(train_decomposed)
test_with_only_decomposed_features = pd.DataFrame(test_decomposed)
val_with_only_decomposed_features = pd.DataFrame(val_decomposed)
#for agg_col in ['sum', 'var', 'mean', 'median', 'std', 'weight_count', 'count_non_0', 'num_different', 'max', 'min']:
# train_with_only_decomposed_features[col] = train[col]
# test_with_only_decomposed_features[col] = test[col]
# Remove any NA
train_with_only_decomposed_features = train_with_only_decomposed_features.fillna(
0)
test_with_only_decomposed_features = test_with_only_decomposed_features.fillna(
0)
val_with_only_decomposed_features = val_with_only_decomposed_features.fillna(
0)
return train_with_only_decomposed_features, test_with_only_decomposed_features, val_with_only_decomposed_features
pca_results_test = pca.transform(test[flist])
print("tSVD")
tsvd = TruncatedSVD(n_components=N_COMP, random_state=random_state)
tsvd_results_train = tsvd.fit_transform(train[flist])
tsvd_results_test = tsvd.transform(test[flist])
print("ICA")
ica = FastICA(n_components=N_COMP, random_state=random_state)
ica_results_train = ica.fit_transform(train[flist])
ica_results_test = ica.transform(test[flist])
print("GRP")
grp = GaussianRandomProjection(n_components=N_COMP, eps=0.1, random_state=random_state)
grp_results_train = grp.fit_transform(train[flist])
grp_results_test = grp.transform(test[flist])
print("SRP")
srp = SparseRandomProjection(n_components=N_COMP, dense_output=True, random_state=random_state)
srp_results_train = srp.fit_transform(train[flist])
srp_results_test = srp.transform(test[flist])
print("FA")
fa = FactorAnalysis(n_components=N_COMP, random_state=random_state)
fa_results_train = fa.fit_transform(train[flist])
fa_results_test = fa.transform(test[flist])
print("Append decomposition components to datasets...")
for i in range(1, N_COMP + 1):
train['pca_' + str(i)] = pca_results_train[:, i - 1]
def run_grp(n_c, X_train, X_test, y_train, y_test):
from sklearn.random_projection import GaussianRandomProjection
grp = GaussianRandomProjection(n_components = n_c, eps = 0.1)
X_train = grp.fit_transform(X_train, y_train)
X_test = grp.transform(X_test)
return [X_train, X_test]
class PISFPA_GRP:
def __init__(self,
num_input_nodes,
num_hidden_units,
num_output_units,
activation='sigmoid',
loss='mse',
beta_init=None,
w_init=None,
bias_init=None):
self._num_input_nodes = num_input_nodes
self._num_hidden_units = num_hidden_units
self._num_output_units = num_output_units
self._activation = getActivation(activation)
self._loss = getLoss(loss)
self._beta = None
self._w = None
self.GRP = GaussianRandomProjection(n_components=num_hidden_units)
#weight_out
self._bias = np.random.uniform(-1.,
1.,
size=(self._num_hidden_units, ))
def fit(self, X, Y, itrs, lam, display_time=False):
self._w = self.GRP.fit_transform(X)
H = self._activation(self._w + self._bias)
if display_time:
start = time.time()
L = 1. / np.max(np.linalg.eigvals(np.dot(H.T, H))).real
m = H.shape[1]
n = Y.shape[1]
x0 = np.zeros((m, n))
x1 = np.zeros((m, n))
L1 = 2 * L * np.dot(H.T, H)
L2 = 2 * L * np.dot(H.T, Y)
for i in range(1, itrs + 1):
cn = ((2e-6 * i) / (2 * i + 1)) * lam * L
beta = 0.9 * i / (i + 1)
alpha = 0.9 * i / (i + 1)
y = x1 + thetan(x0, x1, i) * (x1 - x0)
z = (1 - beta) * x1 + beta * T(x1, L1, L2, cn)
Ty = T(y, L1, L2, cn)
Tz = T(z, L1, L2, cn)
x = (1 - alpha) * Ty + alpha * Tz
x0, x1 = x1, x
if display_time:
stop = time.time()
print(f'Train time: {stop-start}')
self._beta = x
def __call__(self, X):
w = self.GRP.transform(X)
H = self._activation(w + self._bias)
return H.dot(self._beta)
def evaluate(self, X, Y):
pred = self(X)
loss = self._loss(Y, pred)
acc = np.sum(
np.argmax(pred, axis=-1) == np.argmax(Y, axis=-1)) / len(Y)
return loss, acc
def applyGRP(data, nc, new_data):
grp = GaussianRandomProjection(n_components=nc, random_state=79)
grp.fit(data)
return grp.transform(new_data)
def run(_train, _test):
for c in categorical_columns:
train = _train.copy()
test = _test.copy()
enc = LabelBinarizer()
enc.fit(list(train[c].values) + list(test[c].values))
encoded = pd.DataFrame(enc.transform(list(train[c].values)))
train = pd.concat([train, encoded], axis=1)
encoded = pd.DataFrame(enc.transform(list(test[c].values)))
test = pd.concat([test, encoded], axis=1)
train.drop(c, axis=1)
test.drop(c, axis=1)
##Add decomposed components: PCA / ICA etc.
from sklearn.decomposition import PCA, FastICA
from sklearn.decomposition import TruncatedSVD
n_comp = 12
# tSVD
tsvd = TruncatedSVD(n_components=n_comp, random_state=400)
tsvd_results_train = tsvd.fit_transform(train.drop(["y"], axis=1))
tsvd_results_test = tsvd.transform(test)
# PCA
pca = PCA(n_components=n_comp)# random_state=400)
pca2_results_train = pca.fit_transform(train.drop(["y"], axis=1))
pca2_results_test = pca.transform(test)
# ICA
ica = FastICA(n_components=n_comp)#, random_state=400)
ica2_results_train = ica.fit_transform(train.drop(["y"], axis=1))
ica2_results_test = ica.transform(test)
# GRP
grp = GaussianRandomProjection(n_components=n_comp, eps=0.007, random_state=400)
grp_results_train = grp.fit_transform(train.drop(["y"], axis=1))
grp_results_test = grp.transform(test)
# SRP
srp = SparseRandomProjection(n_components=n_comp, dense_output=True, random_state=400)
srp_results_train = srp.fit_transform(train.drop(["y"], axis=1))
srp_results_test = srp.transform(test)
# Append decomposition components to datasets
for i in range(1, n_comp + 1):
train['pca_' + str(i)] = pca2_results_train[:, i - 1]
test['pca_' + str(i)] = pca2_results_test[:, i - 1]
train['ica_' + str(i)] = ica2_results_train[:, i - 1]
test['ica_' + str(i)] = ica2_results_test[:, i - 1]
train['grp_' + str(i)] = grp_results_train[:, i - 1]
test['grp_' + str(i)] = grp_results_test[:, i - 1]
train['srp_' + str(i)] = srp_results_train[:, i - 1]
test['srp_' + str(i)] = srp_results_test[:, i - 1]
train['tsvd_' + str(i)] = tsvd_results_train[:, i - 1]
test['tsvd_' + str(i)] = tsvd_results_test[:, i - 1]
y_train = train["y"]
train = train.drop(["y"], axis=1)
print(c)
cv(train, y_train)
mse_df = pd.DataFrame(columns=[
"1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14",
"15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30"
])
transformed_df = pd.DataFrame(columns=[
"1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14",
"15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30"
])
for i in range(500):
rp = GaussianRandomProjection(n_components=28)
rp.fit(df)
transformed_instances = rp.transform(df)
inverse_components = np.linalg.pinv(rp.components_)
reconstructed_instances = utils.extmath.safe_sparse_dot(
transformed_instances, inverse_components.T)
print(reconstructed_instances)
break
#mse = ((df - reconstructed_instances) ** 2).mean()
#mse_df.loc[i] = mse
new_df = pd.DataFrame(reconstructed_instances,
columns=[
"1", "2", "3", "4", "5", "6", "7", "8", "9",
"10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20", "21", "22", "23", "24", "25",
"26", "27", "28", "29", "30"
])
transformed_df = transformed_df.append(new_df)
def select_features_GaussianRandomProjections(train_X, train_y, test_X, k):
selector = GaussianRandomProjection(n_components=k, random_state=42)
selector.fit(train_X)
train_X = selector.transform(train_X)
test_X = selector.transform(test_X)
return train_X, test_X
tit_cols = list(ten_features.columns)
print(tit_cols)
# RCA
sil_em = []
rca_models = []
em_models = []
ks = range(2, 20)
for dim in range(1, len(tit_cols)):
sil_em.append([])
rca = GaussianRandomProjection(n_components=dim)
rca.fit(X_train)
rca_models.append(rca)
rca_X_train = rca.transform(X_train)
em_models.append([])
for k in ks:
em = GaussianMixture(n_components=k)
em.fit(rca_X_train)
em_models[-1].append(em)
sil_em[-1].append(
silhouette_score(rca_X_train, em.predict(rca_X_train)))
np.savetxt('rca_sil_em_tennis', sil_em)
if False:
for j in range(len(tit_cols) - 1):
plt.plot(ks, sil_em[j], linestyle='-', marker='o', label=str(j + 1))
plt.title("RCA EM silhouette curves - Tennis")
plt.xlabel("number of clusters")
champion_games[key['champ']] [(key['patch'], region, tier)] += build['value']
items_json = []
for champ in champion_builds.keys():
# perform GaussianRandomProjection
all_builds = []
for key in champion_builds[champ]:
all_builds += champion_builds[champ][key]
grp = GaussianRandomProjection(2, random_state = 0)
grp.fit(all_builds)
for key in champion_builds[champ]:
builds = champion_builds[champ][key]
reduction = grp.transform(builds)
# get top 100 builds
zipped = zip(list(reduction), build_games[champ][key], build_objects[champ][key])
sorted_zipped = sorted(zipped, key=lambda x: x[1], reverse=True)
top_builds = sorted_zipped[0:100]
builds_json = []
for i in top_builds:
x = list(i[0])[0]
y = list(i[0])[1]
builds_json.append( {
"champ":champ,
"patch":key[0],
"region":key[1],
"tier":key[2],
# We select only sci.crypt category # Other categories include # 'sci.med', 'sci.space' ,'soc.religion.christian' cat = ['sci.crypt'] data = fetch_20newsgroups(categories=cat) # Create a term document matrix, with term frequencies as the values # from the above dataset. vectorizer = TfidfVectorizer(use_idf=False) vector = vectorizer.fit_transform(data.data) # Perform the projection. In this case we reduce the dimension to 1000 gauss_proj = GaussianRandomProjection(n_components=1000) gauss_proj.fit(vector) # Transform the original data to the new space vector_t = gauss_proj.transform(vector) # print transformed vector shape print vector.shape print vector_t.shape # To validate if the transformation has preserved the distance, we calculate the old and the new distance between the points org_dist = euclidean_distances(vector) red_dist = euclidean_distances(vector_t) diff_dist = abs(org_dist - red_dist) # We take the difference between these points and plot them # as a heatmap (only the first 100 documents). plt.figure() plt.pcolor(diff_dist[0:100, 0:100])
def main():
"""
Main method for the script.
"""
dataset = Dataset(file_path=DATASET_PATHS[CONFIG.dataset])
df = dataset.get_dataframe()
# remove columns that are constant values
metric_headers = dataset.get_metric_headers()
constant_headers = []
variable_headers = []
for header in metric_headers:
if np.unique(df[header].values).size > 1:
variable_headers.append(header)
else:
constant_headers.append(header)
metric_headers = variable_headers
dataset = Dataset(dataframe=df.drop(constant_headers, axis=1))
raw_metrics = dataset.get_metrics()
metrics = raw_metrics.T
# factor analysis
LOG.info('Starting factor analysis with %s factors...', CONFIG.num_factors)
start = time()
# model = FactorAnalysis(n_components=CONFIG.num_factors)
# factors = model.fit_transform(metrics) # num_metrics * num_factors
rng = np.random.RandomState(74)
model = GaussianRandomProjection(eps=0.999, random_state=rng)
factors = model.fit_transform(metrics)
LOG.debug('Dimension before factor analysis: %s', metrics.shape)
LOG.debug('Dimension after factor analysis: %s', factors.shape)
LOG.info('Finished factor analysis in %s seconds.', round(time() - start))
# clustering
if CONFIG.model == 'kmeans':
model = build_k_means(factors)
elif CONFIG.model == 'kmedoids':
model = build_k_medoids(factors)
else:
raise ValueError('Unrecognized model: %s', CONFIG.model)
# find cluster center
labels = model.labels_
# each dimension in transformed_data is the distance to the cluster
# centers.
transformed_data = model.transform(factors)
leftover_metrics = []
for i in np.unique(labels):
# index of the points for the ith cluster
cluster_member_idx = np.argwhere(labels == i).squeeze(1)
cluster_members = transformed_data[cluster_member_idx]
# find the index of the minimum-distance point to the center
closest_member = cluster_member_idx[np.argmin(cluster_members[:, i])]
leftover_metrics.append(metric_headers[closest_member])
# latency needs to be in the metrics
if 'latency' not in leftover_metrics:
leftover_metrics += ['latency']
with open(CONFIG.output_path, 'w') as file:
file.writelines('\n'.join(leftover_metrics))
from sklearn.decomposition import TruncatedSVD as TruncSVD tsvd = TruncSVD(n_components=num_components, algorithm='randomized', random_state=0) tsvd_transformed_data_train = tsvd.fit_transform(sparse_trainData) tsvd_transformed_data_valid = tsvd.transform(sparse_validData) # Perform Randomized Principal Components Analysis (PCA) from sklearn.decomposition import RandomizedPCA as RPCA rpca = RPCA(n_components=num_components) rpca_transformed_data_train = rpca.fit_transform(dense_trainData) rpca_transformed_data_valid = rpca.transform(dense_validData) # Perform Gaussian Random Projection from sklearn.random_projection import GaussianRandomProjection as GaussRan grp = GaussRan(n_components=num_components) grp_transformed_data_train = grp.fit_transform(dense_trainData) grp_transformed_data_valid = grp.transform(dense_validData) # Perform Sparse Random Projection from sklearn.random_projection import SparseRandomProjection as SparseRan srp = SparseRan(n_components=num_components, random_state=0) srp_transformed_data_train = srp.fit_transform(dense_trainData) srp_transformed_data_valid = srp.transform(dense_validData) # Perform classification using 1-Nearest Neighbor Classifier from sklearn.neighbors import KNeighborsClassifier # Create a subset grid to plot performance against numbers of components tsvd_max = tsvd_transformed_data_train.shape[1] plot_subset = [] length_of_plot_subset = len(plot_subset) if tsvd_max < 101:
rtest_data.append(all_reduced[reserve:, ])
rmethod.append("pca")
# ------------------------------------------------------------------------
ica = FastICA(max_iter=500, n_components=comps, random_state=1)
ica.fit(train_data)
all_reduced = ica.transform(train_data)
rdata.append(all_reduced[0:data_n - reserve, ])
rtest_data.append(all_reduced[reserve:, ])
rmethod.append("ica")
# ------------------------------------------------------------------------
grp = GaussianRandomProjection(n_components=comps, eps=0.1,
random_state=1) # reduce data to n components
grp.fit(train_data)
all_reduced = grp.transform(train_data)
rdata.append(all_reduced[0:data_n - reserve, ])
rtest_data.append(all_reduced[reserve:, ])
rmethod.append("grp")
# ------ build the classifier, then run the various data through the neural net -----------------------------------
print_header()
for si in range(len(solvers)):
solver = solvers[si]
cfier = MLPClassifier(validation_fraction=.30,
max_iter=max_iters,
learning_rate=opts["learn_rate"],
solver=solver)
ts_start = time()
y2 = list(dataset2["y"])
scaler = StandardScaler()
scaler.fit(x2)
x2_n = scaler.transform(x2)
#<---------------------DATASET2
#RANDOMIZED PROJECTION ITER
for name, data, y in [['student set', x1, y1],
['student set normalized', x1_n, y1],
['bank set', x2, y2], ['bank set normalized', x2_n, y2]]:
data = np.array(data)
varians = []
for i in range(200):
rp = GRP(n_components=8, random_state=None).fit(data)
newdata = rp.transform(data)
variance = np.var(newdata)
varians.append(variance)
data = newdata
percentvars = (varians / varians[0])
pyplot.plot(percentvars, linewidth=1.5, label=name)
pyplot.plot(np.tile(1, 200), 'k--', linewidth=1, label=('start variance'))
pyplot.title('Variance in RP self iteration \n (ratio to the first run)')
pyplot.xlabel('rp iterations')
pyplot.ylabel("variance ratio")
pyplot.legend()
pyplot.show()
#RANDOMIZED PROJECTION KM AND EM PERFORMANCE
"""
for name, data,y in [['student set',x1,y1],['student set normalized',x1_n,y1],['bank set',x2,y2],['bank set normalized',x2_n,y2]]:
n_components = 'auto'
eps = 0.5
random_state = 2018
# インスタンスの作成
GRP = GaussianRandomProjection(n_components=n_components, eps=eps,
random_state=random_state)
# 確認
vars(GRP)
# 学習器の作成
GRP.fit(X_train)
# 学習器の適用
X_train_GRP = GRP.transform(X_train)
# データフレームに変換
X_train_GRP = pd.DataFrame(data=X_train_GRP, index=train_index)
# プロット表示
scatterPlot(X_train_GRP, y_train, "Gaussian Random Projection")
# 2 スパースランダム射影 -------------------------------------------------------------------
# パラメータの設定
n_components = 'auto'
density = 'auto'
eps = 0.5
dense_output = False
def train_NN_RP(filename,
X_train,
X_test,
y_train,
y_test,
debug=False,
numFolds=10,
njobs=-1,
scalar=1,
make_graphs=False,
pNN={},
nolegend=False,
random_seed=1,
num_dim=4):
np.random.seed(random_seed)
algo = 'RP' + str(num_dim)
start = time.time()
rp = GRP(n_components=num_dim, random_state=random_seed)
rp.fit(X_train)
X_train = rp.transform(X_train)
X_test = rp.transform(X_test)
param_grid = [{
'hidden_layer_sizes': [(512, 512, 512, 512)],
'activation': ['relu'], # 'identity',
'solver': ['adam'],
'alpha': [0.0001, 0.001, 0.01, 0.1],
'batch_size': ['auto'],
'learning_rate_init': [0.001, 0.01],
'max_iter': [10000],
'warm_start': [True],
'early_stopping': [True],
'random_state': [1]
}]
nn_classifier = MLPClassifier()
grid_search = GridSearchCV(nn_classifier,
param_grid,
cv=numFolds,
scoring='roc_auc_ovr_weighted',
return_train_score=True,
n_jobs=njobs,
verbose=debug)
grid_search.fit(X_train, y_train)
cvres = grid_search.cv_results_
util.save_gridsearch_to_csv(cvres, algo,
filename[:-4] + '-' + str(num_dim), scalar, '')
start = time.time()
nn_classifier.fit(X_train, y_train)
print('NN Fit Time: ', time.time() - start)
start = time.time()
y_prob = nn_classifier.predict_proba(X_train)
train_score = roc_auc_score(y_train,
y_prob,
multi_class="ovr",
average="weighted")
print('NN Train Score Time: ', train_score, time.time() - start)
start = time.time()
y_prob = nn_classifier.predict_proba(X_test)
test_score = roc_auc_score(y_test,
y_prob,
multi_class="ovr",
average="weighted")
print('NN Test Score Time: ', test_score, time.time() - start)
test_class = MLPClassifier()
test_class.set_params(**pNN)
if make_graphs:
# computer Model Complexity/Validation curves
util.plot_learning_curve(nn_classifier,
algo,
filename[:-4],
X_train,
y_train,
ylim=(0.0, 1.05),
cv=10,
n_jobs=njobs,
debug=debug)
# util.compute_vc(algo, 'alpha',
# [0.0000001, 0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500,
# 1000, 5000, 10000, 100000, 1000000], X_train, y_train, X_test, y_test, nn_classifier,
# filename[:-4], test_class, pNN, log=True, njobs=njobs, debug=debug)
return time.time() - start, round(train_score, 4), round(test_score, 4)
plt.legend(loc='best')
plt.show()
#KNN baseline
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
ori_train_score = knn.score(X_train, y_train)
ori_test_score = knn.score(X_test, y_test)
#RP
train_scores=[]
test_scores=[]
for component in range(1, len(X_train[0])+1):
grp = GaussianRandomProjection(n_components=component, random_state=1)
X_train_reduced = grp.fit_transform(X_train)
X_test_reduced = grp.transform(X_test)
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train_reduced, y_train)
train_scores.append(knn.score(X_train_reduced, y_train))
test_scores.append(knn.score(X_test_reduced, y_test))
if dataset_name=='spam':
drawMultiple([train_scores, test_scores], 'KNN Accuracy over Randomized Projected Components (Spam dataset)', 'Number of Projected Components', 'Accuracy', ['projected train score','projected test score'], range(1, len(X_train[0])+1))
elif dataset_name=='letter':
drawMultiple([train_scores, test_scores], 'KNN Accuracy over Randomized Projected Components (Letter Recognition dataset)', 'Number of Projected Components', 'Accuracy', ['projected train score','projected test score'], range(1, len(X_train[0])+1))
#FA
train_scores=[]
test_scores=[]
for component in range(1, len(X_train[0])+1):
fa = FeatureAgglomeration(n_clusters=component)
test_size=0.3,
random_state=1,
stratify=yT)
datasetsRCA['Titanic'] = {
'X_train': XT_train.copy(),
'y_train': yT_train.copy(),
'X_test': XT_test.copy(),
'y_test': yT_test.copy(),
'X_full': XT,
'y_full': yT
}
rp = W4.best_estimator_.steps[0][1]
datasetsRCA['Wilt'] = {
'X_train': rp.fit_transform(XW_train),
'y_train': yW_train.copy(),
'X_test': rp.transform(XW_test),
'y_test': yW_test.copy()
}
#%% Clustering after DR
clusters = [2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 25, 30, 35, 40, 50]
scoresRCA = hlp.explore_clustering(datasetsRCA, clusters)
pd.DataFrame(scores)
hlp.plot_CE([scores], clusters, 'DR using RCA (RP)')
hlp.plot_CE([scoresRCA, scores], clusters, 'DR using RCA (RP) vs baseline')
plt.suptitle(
'ICA analysis - (solid) 4 components [Titanic] or 2 [Wilt] (dotted) 7 [Titanic] or 4 [Wilt]'
)
# Plot Silhouettes
from silhouette import plot_silhouettes
''' Decomposition '''
pca = PCA(12, random_state=0)
pca_train = pca.fit_transform(train, y_train)
pca_test = pca.transform(test)
ica = FastICA(12, random_state=0)
ica_train = ica.fit_transform(train, y_train)
ica_test = ica.transform(test)
tsvd = TruncatedSVD(12, random_state=0)
tsvd_train = tsvd.fit_transform(train, y_train)
tsvd_test = tsvd.transform(test)
grp = GaussianRandomProjection(12, eps=0.1, random_state=0)
grp_train = grp.fit_transform(train, y_train)
grp_test = grp.transform(test)
srp = SparseRandomProjection(12, dense_output=True, random_state=0)
srp_train = srp.fit_transform(train, y_train)
srp_test = srp.transform(test)
for i in range(12):
train['pca_' + str(i)] = pca_train.T[i]
test['pca_' + str(i)] = pca_test.T[i]
train['ica_' + str(i)] = ica_train.T[i]
test['ica_' + str(i)] = ica_test.T[i]
train['tsvd_' + str(i)] = tsvd_train.T[i]
test['tsvd_' + str(i)] = tsvd_test.T[i]
def kMeans():
# citation: https://realpython.com/k-means-clustering-python/
digits = load_digits()
# features
digits_features = digits.data[:, 0:-1]
# label
label = digits.data[:, -1]
scaler = StandardScaler()
scaled_features = scaler.fit_transform(digits_features)
# citation: hands on machine learning
gm = GaussianMixture(covariance_type='spherical',
n_components=8,
n_init=10)
gm.fit(scaled_features)
print("GM Converged", gm.converged_)
print("GM Convergence Iterations", gm.n_iter_)
print("GM weights", gm.weights_)
gm.predict(scaled_features)
gm.predict_proba(scaled_features)
gm.score_samples(scaled_features)
aic = []
bic = []
for i in range(21):
gm = GaussianMixture(covariance_type='spherical',
n_components=20,
n_init=10)
gm.fit(scaled_features)
aic.append(gm.aic(scaled_features))
bic.append(gm.bic(scaled_features))
plt.plot(aic, label="AIC")
plt.plot(bic, label="BIC")
# plt.xticks(np.arange(min(x), max(x) + 1, 1.0))
# plt.xticks(range(1,18))
plt.xlabel("Number of Clusters")
plt.ylabel("Information Criterion")
plt.legend()
plt.show()
# x_centered = digits_features - digits_features.mean(axis=0)
# U, s, Vt = np.linalg.svd(x_centered)
# c1 = Vt.T[:, 0]
# c2 = Vt.T[:, 1]
# W2 = Vt.T[:, :2]
# X2D = x_centered.dot(W2)
# pca = PCA()
# pca.fit(scaled_features)
# cumsum = np.cumsum(pca.explained_variance_ratio_)
# d = np.argmax(cumsum >= 0.95) + 1
# pca = PCA(n_components=0.95)
# X_reduced = pca.fit_transform(scaled_features)
explained_variance = []
for i in range(63):
pca = PCA(n_components=i)
pca.fit(scaled_features)
cumsum = np.cumsum(pca.explained_variance_ratio_)
plt.plot(cumsum, label="Explained Variance Ratio")
# plt.xticks(np.arange(min(x), max(x) + 1, 1.0))
# plt.xticks(range(1,18))
plt.xlabel("Number of Dimensions")
plt.ylabel("Explained Variance Ratio")
plt.legend()
plt.show()
digits_trainingX, digits_testingX, digits_trainingY, digits_testingY = train_test_split(
digits_features, label)
# ica
# citation: https://stackoverflow.com/questions/36566844/pca-projection-and-reconstruction-in-scikit-learn
error = []
for i in range(1, 50):
pca = PCA(n_components=i)
pca.fit(digits_trainingX)
U, S, VT = np.linalg.svd(digits_trainingX - digits_trainingX.mean(0))
x_train_pca = pca.transform(digits_trainingX)
x_train_pca2 = (digits_trainingX - pca.mean_).dot(pca.components_.T)
x_projected = pca.inverse_transform(x_train_pca)
x_projected2 = x_train_pca.dot(pca.components_) + pca.mean_
loss = ((digits_trainingX - x_projected)**2).mean()
error.append(loss)
plt.clf()
plt.figure(figsize=(15, 15))
plt.title("reconstruction error")
plt.plot(error, 'r')
plt.xticks(range(len(error)), range(1, 50), rotation='vertical')
plt.xlim([-1, len(error)])
plt.show()
clf = MLPClassifier(alpha=0.001,
hidden_layer_sizes=(8, ),
random_state=1,
solver='lbfgs')
clf.fit(digits_trainingX, digits_trainingY)
y_pred = clf.predict(digits_testingX)
print("Accuracy Score Normal", accuracy_score(digits_testingY, y_pred))
k_acc = []
k_gm = []
time_arr = []
for k in range(1, 15):
kmeans = KMeans(n_clusters=k)
X_train = kmeans.fit_transform(digits_trainingX)
X_test = kmeans.transform(digits_testingX)
start_time = time.time()
clf = MLPClassifier(alpha=0.001,
hidden_layer_sizes=(8, ),
random_state=1,
solver='lbfgs')
clf.fit(X_train, digits_trainingY)
total_time = time.time() - start_time
y_pred = clf.predict(X_test)
score = accuracy_score(digits_testingY, y_pred)
k_acc.append(score)
time_arr.append(total_time)
plt.plot(k_acc, label="K-Means")
plt.plot(time_arr, label="Computation Time")
# plt.xticks(np.arange(min(x), max(x) + 1, 1.0))
# plt.xticks(range(1,18))
plt.xlabel("k # of clusters")
plt.ylabel("NN Accuracy")
plt.legend()
plt.show()
acc = []
acc_ica = []
acc_rca = []
for i in range(1, 40):
pca = PCA(n_components=i)
X_train = pca.fit_transform(digits_trainingX)
X_test = pca.transform(digits_testingX)
clf = MLPClassifier(alpha=0.001,
hidden_layer_sizes=(8, ),
random_state=1,
solver='lbfgs')
clf.fit(X_train, digits_trainingY)
y_pred = clf.predict(X_test)
score = accuracy_score(digits_testingY, y_pred)
acc.append(score)
ica = FastICA(n_components=i)
x_train_i = ica.fit_transform(digits_trainingX)
x_test_i = ica.transform(digits_testingX)
clf.fit(x_train_i, digits_trainingY)
y_pred_i = clf.predict(x_test_i)
score_i = accuracy_score(digits_testingY, y_pred_i)
acc_ica.append(score_i)
rca = GaussianRandomProjection(n_components=i)
x_train_r = rca.fit_transform(digits_trainingX)
x_test_r = rca.transform(digits_testingX)
clf.fit(x_train_r, digits_trainingY)
y_pred_r = clf.predict(x_test_r)
score_r = accuracy_score(digits_testingY, y_pred_r)
acc_rca.append(score_r)
plt.plot(acc, label="PCA")
plt.plot(acc_ica, label="ICA")
plt.plot(acc_rca, label="RCA")
# plt.xticks(np.arange(min(x), max(x) + 1, 1.0))
# plt.xticks(range(1,18))
plt.xlabel("Components")
plt.ylabel("NN Accuracy")
plt.legend()
plt.show()
# cumsum = np.cumsum(pca.explained_variance_ratio_)
# d = np.argmax(cumsum >= 0.95) + 1
# randomized projections
rnd_pca = PCA(n_components=50, svd_solver="randomized")
X_reduced_rand = rnd_pca.fit_transform(scaled_features)
# citation: https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py
# k best
scaler = MinMaxScaler()
digits_indices = np.arange(digits_features.shape[-1])
scaled_features_norm = scaler.fit_transform(scaled_features)
k_selected = SelectKBest(f_classif, k=50)
k_selected.fit(scaled_features_norm, label)
scores = -np.log10(k_selected.pvalues_)
plt.bar(digits_indices - .45,
scores,
width=.2,
label=r'Univariate score ($-Log(p_{value})$)')
plt.xlabel("Features")
plt.ylabel("F-Score")
plt.show()
gm = GaussianMixture(covariance_type='spherical',
n_components=8,
n_init=10)
gm.fit(X_reduced_inc)
print("GM Converged - PCA Inc", gm.converged_)
print("GM Convergence Iterations", gm.n_iter_)
print("GM weights", gm.weights_)
gm.predict(X_reduced_inc)
gm.predict_proba(X_reduced_inc)
gm.score_samples(X_reduced_inc)
kmeans = KMeans(init="random",
n_clusters=63,
n_init=10,
max_iter=300,
random_state=42)
kmeans.fit(scaled_features)
# the lowest SSE value
print("KMeans Inertia", kmeans.inertia_)
# final locations of the centroid
print("KMeans Cluster Centers", kmeans.cluster_centers_)
# num of iterations required to converge
print("KMeans Iterations Required To Converge", kmeans.n_iter_)
# labels
print("KMeans Labels", kmeans.labels_[:5])
kmeans_kwargs = {
"init": "random",
"n_init": 10,
"max_iter": 300,
"random_state": 42,
}
sse = []
for k in range(1, 63):
kmeans = KMeans(n_clusters=k, **kmeans_kwargs)
kmeans.fit(scaled_features)
sse.append(kmeans.inertia_)
kl = KneeLocator(range(1, 63), sse, curve="convex", direction="decreasing")
# optimal k (number of clusters) for this dataset
print("Elbow", kl.elbow)
clf = MLPClassifier(alpha=0.001,
hidden_layer_sizes=(8, ),
random_state=1,
solver='lbfgs')
clf.fit(digits_trainingX, digits_trainingY)
y_pred = clf.predict(digits_testingX)
model = KMeans(n_clusters=5)
kmeans.fit(scaled_features)
labels = kmeans.fit_predict(digits_testingX)
print("Accuracy Score Normal", accuracy_score(digits_testingY, y_pred))
print("Accuracy Score K-Means", accuracy_score(digits_testingY, labels))
elbow_visualizer = KElbowVisualizer(model, k=(2, 63))
elbow_visualizer.fit(digits_features)
elbow_visualizer.show()
silhouette_visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
silhouette_visualizer.fit(digits_features)
silhouette_visualizer.show()
ic_visualizer = InterclusterDistance(model)
ic_visualizer.fit(digits_features)
ic_visualizer.show()
# gmm = GaussianMixture(n_components=7).fit(digits_features)
# labels = gmm.predict(digits_features)
# plt.scatter(digits_features[:, 0], digits_features[:, 1], c=labels, s=40, cmap='viridis')
# plt.show()
# digits_features_pd = pd.DataFrame(data=digits_features[1:, 1:],
# index=digits_features[1:,0],
# columns=digits_features[0,1:])
# pd.plotting.scatter_matrix(digits_features_pd)
# probs = GaussianMixture.predict_proba(digits_features)
# print(probs[:5].round(3))
kmeans = KMeans(init="random",
n_clusters=18,
n_init=10,
max_iter=300,
random_state=42)
kmeans.fit(X_reduced_inc)
# the lowest SSE value
print("KMeans Inertia", kmeans.inertia_)
# final locations of the centroid
print("KMeans Cluster Centers", kmeans.cluster_centers_)
# num of iterations required to converge
print("KMeans Iterations Required To Converge", kmeans.n_iter_)
# labels
print("KMeans Labels", kmeans.labels_[:5])
kmeans_kwargs = {
"init": "random",
"n_init": 10,
"max_iter": 300,
"random_state": 42,
}
sse = []
for k in range(1, 18):
kmeans = KMeans(n_clusters=k, **kmeans_kwargs)
kmeans.fit(scaled_features)
sse.append(kmeans.inertia_)
kl = KneeLocator(range(1, 18), sse, curve="convex", direction="decreasing")
# optimal k (number of clusters) for this dataset
print("Elbow", kl.elbow)
model = KMeans()
elbow_visualizer = KElbowVisualizer(model, k=(2, 18))
elbow_visualizer.fit(X_reduced_inc)
elbow_visualizer.show()
silhouette_visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
silhouette_visualizer.fit(X_reduced_inc)
silhouette_visualizer.show()
ic_visualizer = InterclusterDistance(model)
ic_visualizer.fit(X_reduced_inc)
ic_visualizer.show()
def nn2(xs, ys, xs_test, ys_test, n_components, clf_constructor):
ks = [0 for _ in range(10)]
cataccs = [0 for _ in range(10)]
ys = [to_categorical(ys[0]), to_categorical(ys[1])]
ys_test = [to_categorical(ys_test[0]), to_categorical(ys_test[1])]
for i in range(2):
shape = np.shape(xs[i])[1]
n_components[i] = shape
model = utils.create_adult_model(
shape, 2) if i == 0 else utils.create_wine_model(shape, 5)
model.fit(xs[i][:10000],
ys[i][:10000],
batch_size=50,
epochs=10,
verbose=False)
cataccs[i] = model.evaluate(xs_test[i], ys_test[i],
verbose=False)[1] * 100
for k in range(2, 11):
try:
clf = clf_constructor(n_clusters=k)
except:
clf = clf_constructor(n_components=k)
for i in range(2):
pca = PCA(n_components=n_components[2 + i])
transformed = pca.fit_transform(xs[i])
transformed_test = pca.transform(xs_test[i])
predict = to_categorical(clf.fit_predict(transformed[:10000]))
predict_test = to_categorical(clf.predict(
transformed_test[:10000]))
input_dims = [n_components[2 + i], k]
model = utils.create_mi_adult_model(
input_dims, 2) if i == 0 else utils.create_mi_wine_model(
input_dims, 5)
model.fit([transformed[:10000], predict],
ys[i][:10000],
batch_size=50,
epochs=10,
verbose=False)
catacc = model.evaluate([transformed_test, predict_test],
ys_test[i],
verbose=False)[1] * 100
if catacc > cataccs[2 + i]:
ks[2 + i] = k
cataccs[2 + i] = catacc
ica = FastICA(n_components=n_components[4 + i])
transformed = ica.fit_transform(xs[i])
transformed_test = ica.transform(xs_test[i])
predict = to_categorical(clf.fit_predict(transformed[:10000]))
predict_test = to_categorical(clf.predict(
transformed_test[:10000]))
input_dims = [n_components[4 + i], k]
model = utils.create_mi_adult_model(
input_dims, 2) if i == 0 else utils.create_mi_wine_model(
input_dims, 5)
model.fit([transformed[:10000], predict],
ys[i][:10000],
batch_size=50,
epochs=10,
verbose=False)
catacc = model.evaluate([transformed_test, predict_test],
ys_test[i],
verbose=False)[1] * 100
if catacc > cataccs[4 + i]:
ks[4 + i] = k
cataccs[4 + i] = catacc
if i == 1:
rp = GaussianRandomProjection(eps=0.95)
transformed = rp.fit_transform(xs[i])
transformed_test = rp.transform(xs_test[i])
predict = to_categorical(clf.fit_predict(transformed[:10000]))
predict_test = to_categorical(
clf.predict(transformed_test[:10000]))
input_dims = [np.shape(transformed)[1], k]
model = utils.create_mi_wine_model(input_dims, 5)
model.fit([transformed[:10000], predict],
ys[i][:10000],
batch_size=50,
epochs=10,
verbose=False)
catacc = model.evaluate([transformed_test, predict_test],
ys_test[i],
verbose=False)[1] * 100
if catacc > cataccs[6 + i]:
ks[6 + i] = k
cataccs[6 + i] = catacc
encoder, vae = utils.create_vae(
np.shape(xs[i])[1], n_components[8 + i])
vae.fit(xs[i], batch_size=50, epochs=10, verbose=False)
transformed = encoder.predict(xs[i], verbose=False)
transformed_test = encoder.predict(xs_test[i], verbose=False)
predict = to_categorical(clf.fit_predict(transformed[:10000]))
predict_test = to_categorical(clf.predict(
transformed_test[:10000]))
input_dims = [n_components[8 + i], k]
model = utils.create_mi_adult_model(
input_dims, 2) if i == 0 else utils.create_mi_wine_model(
input_dims, 5)
model.fit([transformed[:10000], predict],
ys[i][:10000],
batch_size=50,
epochs=10,
verbose=False)
catacc = model.evaluate([transformed_test, predict_test],
ys_test[i],
verbose=False)[1] * 100
if catacc > cataccs[8 + i]:
ks[8 + i] = k
cataccs[8 + i] = catacc
plot.style.use('seaborn-darkgrid')
plot.title(f'Influence of feature transformation on the NN accuracy')
color = []
for _ in range(5):
color.append('tab:blue')
color.append('tab:orange')
x = []
count = 1
for _ in range(5):
x.append(count)
count += 0.5
x.append(count)
count += 1
plot.bar(x, cataccs, color=color, width=0.75)
x = []
count = 1.25
for _ in range(5):
x.append(count)
count += 1.5
plot.xticks(x, ['None', 'PCA', 'ICA', 'RP', 'VAE'])
plot.xlabel('Feature transformation method')
plot.ylabel('Categorical accuracy (%)')
plot.show()
def gen_feature(train, test):
train = pd.DataFrame(train)
test = pd.DataFrame(test)
n_comp = 15
drop_list = []
test_drop_list = []
print(train.drop(drop_list, axis=1).shape, test.drop(test_drop_list, axis=1).shape)
print('tSVD', datetime.now() - start)
# tSVD
tsvd = TruncatedSVD(n_components=n_comp)
tsvd_results_train = tsvd.fit_transform(train.drop(drop_list, axis=1))
tsvd_results_test = tsvd.transform(test.drop(test_drop_list, axis=1))
print('PCA', datetime.now() - start)
# PCA
pca = PCA(n_components=n_comp)
pca2_results_train = pca.fit_transform(train.drop(drop_list, axis=1))
pca2_results_test = pca.transform(test.drop(test_drop_list, axis=1))
print('ICA', datetime.now() - start)
# ICA
ica = FastICA(n_components=n_comp, max_iter=10000)
ica2_results_train = ica.fit_transform(train.drop(drop_list, axis=1))
ica2_results_test = ica.transform(test.drop(test_drop_list, axis=1))
print('GRP', datetime.now() - start)
# GRP
grp = GaussianRandomProjection(n_components=n_comp, eps=0.1)
grp_results_train = grp.fit_transform(train.drop(drop_list, axis=1))
grp_results_test = grp.transform(test.drop(test_drop_list, axis=1))
print('SRP', datetime.now() - start)
# SRP
srp = SparseRandomProjection(n_components=n_comp, dense_output=True)
srp_results_train = srp.fit_transform(train.drop(drop_list, axis=1))
srp_results_test = srp.transform(test.drop(test_drop_list, axis=1))
# MCA
# res_mca = MCA(train, ncp=n_comp, graph = FALSE)
# save columns list before adding the decomposition components
usable_columns = list(set(train.columns) - set(drop_list))
# Append decomposition components to datasets
for i in range(1, n_comp + 1):
train['pca_' + str(i)] = pca2_results_train[:, i - 1]
test['pca_' + str(i)] = pca2_results_test[:, i - 1]
train['ica_' + str(i)] = ica2_results_train[:, i - 1]
test['ica_' + str(i)] = ica2_results_test[:, i - 1]
train['tsvd_' + str(i)] = tsvd_results_train[:, i - 1]
test['tsvd_' + str(i)] = tsvd_results_test[:, i - 1]
train['grp_' + str(i)] = grp_results_train[:, i - 1]
test['grp_' + str(i)] = grp_results_test[:, i - 1]
train['srp_' + str(i)] = srp_results_train[:, i - 1]
test['srp_' + str(i)] = srp_results_test[:, i - 1]
return train, test
def perform_feature_engineering(train, test, config):
for c in train.columns:
if (len(train[c].value_counts()) == 2):
if (train[c].mean() < config['SparseThreshold']):
del train[c]
del test[c]
col = list(test.columns)
if config['ID'] != True:
col.remove('ID')
# tSVD
if (config['tSVD'] == True):
tsvd = TruncatedSVD(n_components=config['n_comp'])
tsvd_results_train = tsvd.fit_transform(train[col])
tsvd_results_test = tsvd.transform(test[col])
for i in range(1, config['n_comp'] + 1):
train['tsvd_' + str(i)] = tsvd_results_train[:, i - 1]
test['tsvd_' + str(i)] = tsvd_results_test[:, i - 1]
# PCA
if (config['PCA'] == True):
pca = PCA(n_components=config['n_comp'])
pca2_results_train = pca.fit_transform(train[col])
pca2_results_test = pca.transform(test[col])
for i in range(1, config['n_comp'] + 1):
train['pca_' + str(i)] = pca2_results_train[:, i - 1]
test['pca_' + str(i)] = pca2_results_test[:, i - 1]
# ICA
if (config['ICA'] == True):
ica = FastICA(n_components=config['n_comp'])
ica2_results_train = ica.fit_transform(train[col])
ica2_results_test = ica.transform(test[col])
for i in range(1, config['n_comp'] + 1):
train['ica_' + str(i)] = ica2_results_train[:, i - 1]
test['ica_' + str(i)] = ica2_results_test[:, i - 1]
# GRP
if (config['GRP'] == True):
grp = GaussianRandomProjection(n_components=config['n_comp'], eps=0.1)
grp_results_train = grp.fit_transform(train[col])
grp_results_test = grp.transform(test[col])
for i in range(1, config['n_comp'] + 1):
train['grp_' + str(i)] = grp_results_train[:, i - 1]
test['grp_' + str(i)] = grp_results_test[:, i - 1]
# SRP
if (config['SRP'] == True):
srp = SparseRandomProjection(n_components=config['n_comp'],
dense_output=True,
random_state=420)
srp_results_train = srp.fit_transform(train[col])
srp_results_test = srp.transform(test[col])
for i in range(1, config['n_comp'] + 1):
train['srp_' + str(i)] = srp_results_train[:, i - 1]
test['srp_' + str(i)] = srp_results_test[:, i - 1]
if config['magic'] == True:
magic_mat = train[['ID', 'X0', 'y']]
magic_mat = magic_mat.groupby(['X0'])['y'].mean()
magic_mat = pd.DataFrame({
'X0': magic_mat.index,
'magic': list(magic_mat)
})
mean_magic = magic_mat['magic'].mean()
train = train.merge(magic_mat, on='X0', how='left')
test = test.merge(magic_mat, on='X0', how='left')
test['magic'] = test['magic'].fillna(mean_magic)
return train, test
def gen_features(train, val, test):
train = pd.DataFrame(train)
val = pd.DataFrame(val)
test = pd.DataFrame(test)
# cat_cols = ['city', 'bd', 'gender', 'registered_via', 'registration_init_year',
# 'registration_init_month', 'registration_init_date', 'payment_method_id', 'payment_plan_days',
# 'plan_list_price', 'actual_amount_paid', 'is_auto_renew', 'is_cancel',
# 'transaction_date_year', 'transaction_date_month', 'transaction_date_date',
# 'membership_expire_date_year',
# 'membership_expire_date_month', 'membership_expire_date_date', 'membership_transaction_gap',
# 'cancel_times',
# 'auto_renew_count', 'plan_net_worth', 'user_date_year', 'user_date_month',
# 'user_date_date']
# con_cols = [x for x in train.columns if x not in cat_cols and x not in ['msno', 'is_churn']]
# train[cat_cols] = train[cat_cols].astype('object')
# test[cat_cols] = test[cat_cols].astype('object')
# val[cat_cols] = val[cat_cols].astype('object')
#
# for col in cat_cols:
# train[col].fillna(value=train[col].mode()[0], inplace=True)
# test[col].fillna(value=test[col].mode()[0], inplace=True)
# val[col].fillna(value=val[col].mode()[0], inplace=True)
# for col in con_cols:
# train[col].fillna(value=train[col].mean(), inplace=True)
# test[col].fillna(value=test[col].mean(), inplace=True)
# val[col].fillna(value=val[col].mean(), inplace=True)
#
# for c in train.columns:
# if train[c].dtype == 'object':
# lbl = LabelEncoder()
# lbl.fit(list(train[c].values) + list(test[c].values))
# train[c] = lbl.transform(list(train[c].values))
# test[c] = lbl.transform(list(test[c].values))
n_comp = 15
drop_list = []
test_drop_list = []
print(train.drop(drop_list, axis=1).shape, test.drop(test_drop_list, axis=1).shape)
print('tSVD', datetime.now() - start)
# tSVD
tsvd = TruncatedSVD(n_components=n_comp)
tsvd_results_train = tsvd.fit_transform(train.drop(drop_list, axis=1))
tsvd_results_val= tsvd.transform(val.drop(test_drop_list, axis=1))
tsvd_results_test = tsvd.transform(test.drop(test_drop_list, axis=1))
print('PCA', datetime.now() - start)
# PCA
pca = PCA(n_components=n_comp)
pca2_results_train = pca.fit_transform(train.drop(drop_list, axis=1))
pca2_results_val = pca.transform(val.drop(test_drop_list, axis=1))
pca2_results_test = pca.transform(test.drop(test_drop_list, axis=1))
print('ICA', datetime.now() - start)
# ICA
ica = FastICA(n_components=n_comp, max_iter=10000)
ica2_results_train = ica.fit_transform(train.drop(drop_list, axis=1))
ica2_results_val = ica.transform(val.drop(test_drop_list, axis=1))
ica2_results_test = ica.transform(test.drop(test_drop_list, axis=1))
print('GRP', datetime.now() - start)
# GRP
grp = GaussianRandomProjection(n_components=n_comp, eps=0.1)
grp_results_train = grp.fit_transform(train.drop(drop_list, axis=1))
grp_results_val = grp.transform(val.drop(test_drop_list, axis=1))
grp_results_test = grp.transform(test.drop(test_drop_list, axis=1))
print('SRP', datetime.now() - start)
# SRP
srp = SparseRandomProjection(n_components=n_comp, dense_output=True)
srp_results_train = srp.fit_transform(train.drop(drop_list, axis=1))
srp_results_val = srp.transform(val.drop(test_drop_list, axis=1))
srp_results_test = srp.transform(test.drop(test_drop_list, axis=1))
# MCA
# res_mca = MCA(train, ncp=n_comp, graph = FALSE)
# save columns list before adding the decomposition components
usable_columns = list(set(train.columns) - set(drop_list))
# Append decomposition components to datasets
for i in range(1, n_comp + 1):
train['pca_' + str(i)] = pca2_results_train[:, i - 1]
val['pca_' + str(i)] = pca2_results_val[:, i - 1]
test['pca_' + str(i)] = pca2_results_test[:, i - 1]
train['ica_' + str(i)] = ica2_results_train[:, i - 1]
val['ica_' + str(i)] = ica2_results_val[:, i - 1]
test['ica_' + str(i)] = ica2_results_test[:, i - 1]
train['tsvd_' + str(i)] = tsvd_results_train[:, i - 1]
val['tsvd_' + str(i)] = tsvd_results_val[:, i - 1]
test['tsvd_' + str(i)] = tsvd_results_test[:, i - 1]
train['grp_' + str(i)] = grp_results_train[:, i - 1]
val['grp_' + str(i)] = grp_results_val[:, i - 1]
test['grp_' + str(i)] = grp_results_test[:, i - 1]
train['srp_' + str(i)] = srp_results_train[:, i - 1]
val['srp_' + str(i)] = srp_results_val[:, i - 1]
test['srp_' + str(i)] = srp_results_test[:, i - 1]
return train, val, test
def run_rp(dataset, min_components, max_components):
X, y = load_dataset(dataset)
data = X
n_samples, n_features = data.shape
n_labels = len(np.unique(y))
labels = y
results = []
for n_components in range(min_components, max_components):
print('n_components: ', n_components)
for max_iters in [10, 40, 100, 500]:
scores = []
times = []
components = []
kurtoses = []
losses = []
for iters in range(0, max_iters):
data = X.copy()
rp = GaussianRandomProjection(n_components=n_components)
t0 = time()
rp.fit(X)
times.append(time() - t0)
components.append(rp.n_components_)
kurtoses.append(kurtosis(rp.components_, axis=None))
matrix = rp.components_
new_data = rp.transform(data)
new_data_inv = np.dot(new_data, matrix)
loss = metrics.mean_squared_error(data, new_data_inv)
losses.append(loss)
scores.append(n_components)
scores.append(max_iters)
scores.append(np.mean(np.array(times)))
scores.append(np.mean(np.array(components)))
scores.append(np.mean(np.array(kurtoses)))
scores.append(np.std(np.array(kurtoses)))
scores.append(np.mean(np.array(losses)))
scores.append(np.std(np.array(losses)))
results.append(scores)
# N-Components vs Loss
plot_results(np.array(results),
trends_index=1,
x_axis_index=0,
x_axis_label='K-Components',
y_axis_index=[6],
y_axis_label='Reconstruction Error',
title=dataset.title() + ': RP',
filename='-'.join(['rp', dataset, 'loss']))
# N-Components vs Loss (STD)
plot_results(np.array(results),
trends_index=1,
x_axis_index=0,
x_axis_label='K-Components',
y_axis_index=[7],
y_axis_label='Reconstruction Error (STD)',
title=dataset.title() + ': RP',
filename='-'.join(['rp', dataset, 'lossstd']))
# N-Components vs Kurtosis
plot_results(np.array(results),
trends_index=1,
x_axis_index=0,
x_axis_label='K-Components',
y_axis_index=[4],
y_axis_label='Kurtosis',
title=dataset.title() + ': RP',
filename='-'.join(['rp', dataset, 'kurtosis']))
# N-Components vs Kurtosis (STD)
plot_results(np.array(results),
trends_index=1,
x_axis_index=0,
x_axis_label='K-Components',
y_axis_index=[5],
y_axis_label='Kurtosis (STD)',
title=dataset.title() + ': RP',
filename='-'.join(['rp', dataset, 'kurtstd']))
# N-Components vs Components
plot_results(np.array(results),
trends_index=1,
x_axis_index=0,
x_axis_label='K-Components',
y_axis_index=[3],
y_axis_label='Components',
title=dataset.title() + ': RP',
filename='-'.join(['rp', dataset, 'comp']))
# N-Components vs Time
plot_results(np.array(results),
trends_index=1,
x_axis_index=0,
x_axis_label='K-Components',
y_axis_index=[2],
y_axis_label='Time',
title=dataset.title() + ': RP',
filename='-'.join(['rp', dataset, 'time']))
results = np.array(results)
np.savetxt('output-csv/' + ('-'.join([dataset, 'rp.csv'])),
results,
delimiter=",",
fmt="%s")
tsvd_results_test = tsvd.transform(testingSet)
# PCA
pca = PCA(n_components=n_comp, random_state=42)
pca2_results_train = pca.fit_transform(trainingSet.drop(["y"], axis=1))
pca2_results_test = pca.transform(testingSet)
# ICA
ica = FastICA(n_components=n_comp, random_state=42)
ica2_results_train = ica.fit_transform(trainingSet.drop(["y"], axis=1))
ica2_results_test = ica.transform(testingSet)
# GRP
grp = GaussianRandomProjection(n_components=n_comp, eps=0.1, random_state=420)
grp_results_train = grp.fit_transform(trainingSet.drop(["y"], axis=1))
grp_results_test = grp.transform(testingSet)
# SRP
srp = SparseRandomProjection(n_components=n_comp,
dense_output=True,
random_state=420)
srp_results_train = srp.fit_transform(trainingSet.drop(["y"], axis=1))
srp_results_test = srp.transform(testingSet)
# Append decomposition components to datasets
for i in range(1, n_comp + 1):
trainingSet['pca_' + str(i)] = pca2_results_train[:, i - 1]
testingSet['pca_' + str(i)] = pca2_results_test[:, i - 1]
trainingSet['ica_' + str(i)] = ica2_results_train[:, i - 1]
testingSet['ica_' + str(i)] = ica2_results_test[:, i - 1]
# Selec tonly sci.crypt category # Other categories include # sci.med sci.space soc.religion.christian cat = ['sci.crypt'] data = fetch_20newsgroups(categories=cat) # Creat a term document matrix with term frequencies as the values frmo the # above dataset vectorizer = TfidfVectorizer(use_idf=False) vector = vectorizer.fit_transform(data.data) # Perform the projection. In this case we reduce the dimension to 1000 gauss_proj = GaussianRandomProjection(n_components=1000) gauss_proj.fit(vector) # Transform the original data to the new space vector_t = gauss_proj.transform(vector) # Print transformed vector shape print vector.shape print vector_t.shape # To validate if the transformation has preserved the distance, we calculate the # old and the new distance between the points org_dist = euclidean_distances(vector) red_dist = euclidean_distances(vector_t) diff_dist = abs(org_dist - red_dist) # We take the difference between these points and plot them as a heatmap (only # the first 1000 documents). plt.figure() plt.pcolor(diff_dist[0:100, 0:100])
def scatterPlot(xDF, yDF, algoName):
tempDF = pd.DataFrame(data=xDF.loc[:, 0:1], index=xDF.index)
tempDF = pd.concat((tempDF, yDF), axis=1, join="inner")
tempDF.columns = ["First Vector", "Second Vector", "Label"]
sns.lmplot(x="First Vector", y="Second Vector", hue="Label", \
data=tempDF, fit_reg=False)
ax = plt.gca()
ax.set_title("Separation of Observations using " + algoName)
#----------------------------------------------------------------------------------------------------
# Gaussian Random Projection
from sklearn.random_projection import GaussianRandomProjection
n_components = 'auto'
eps = 0.5
random_state = 2018
#eps:n_componentsが'auto'に設定されている場合に、Johnson-Lindenstrauss lemmaに従った埋め込みの品質を制御するためのパラメータ.
# 値が小さいほど埋め込みが良くなり，ターゲット射影空間の次元数（n_components）が多くなる
GRP = GaussianRandomProjection(n_components=n_components, eps=eps, \
random_state=random_state)
X_train_GRP = GRP.fit_transform(X_train)
X_train_GRP = pd.DataFrame(data=X_train_GRP, index=train_index)
X_validation_GRP = GRP.transform(X_validation)
X_validation_GRP = pd.DataFrame(data=X_validation_GRP, index=validation_index)
scatterPlot(X_train_GRP, y_train, "Gaussian Random Projection")
plt.show()
tsvd_results_test = tsvd.transform(test)
# PCA
pca = PCA(n_components=n_comp, random_state=420)
pca2_results_train = pca.fit_transform(train.drop(["y"], axis=1))
pca2_results_test = pca.transform(test)
# ICA
ica = FastICA(n_components=n_comp, random_state=420)
ica2_results_train = ica.fit_transform(train.drop(["y"], axis=1))
ica2_results_test = ica.transform(test)
# GRP
grp = GaussianRandomProjection(n_components=n_comp, eps=0.1, random_state=420)
grp_results_train = grp.fit_transform(train.drop(["y"], axis=1))
grp_results_test = grp.transform(test)
# SRP
srp = SparseRandomProjection(n_components=n_comp, dense_output=True, random_state=420)
srp_results_train = srp.fit_transform(train.drop(["y"], axis=1))
srp_results_test = srp.transform(test)
# Append decomposition components to datasets
for i in range(1, n_comp+1):
train['pca_' + str(i)] = pca2_results_train[:,i-1]
test['pca_' + str(i)] = pca2_results_test[:, i-1]
train['ica_' + str(i)] = ica2_results_train[:,i-1]
test['ica_' + str(i)] = ica2_results_test[:, i-1]
train['tsvd_' + str(i)] = tsvd_results_train[:,i-1]
train_decomposed.append(fa_results_train)
test_decomposed.append(fa_results_test)
val_decomposed.append(fa_results_val)
if use_grp>0.0 or use_grp<0.0:
print("GRP")
if use_grp>0.0:
N_COMP = int(use_grp * train.shape[1]) +1
eps=10
if use_grp<0.0:
N_COMP = "auto"
eps=abs(use_grp)
grp = GaussianRandomProjection(n_components = N_COMP, eps=eps, random_state=42)
grp_results = grp.fit(total)
grp_results_train = grp.transform(train)
grp_results_test = grp.transform(test)
grp_results_val = grp.transform(val)
train_decomposed.append(grp_results_train)
test_decomposed.append(grp_results_test)
val_decomposed.append(grp_results_val)
if use_srp>0.0:
print("SRP")
N_COMP = int(use_srp * train.shape[1]) +1
srp = SparseRandomProjection(n_components = N_COMP, dense_output=True, random_state=42)
srp_results = srp.fit(total)
srp_results_train = srp.transform(train)
srp_results_test = srp.transform(test)
ap_region_data = { k:v for (k,v) in region_data.items() if ap_champs[k[2]]}
ap_tier_data = { k:v for (k,v) in tier_data.items() if ap_champs[k[2]]}
ap_cross_data = { k:v for (k,v) in cross_data.items() if ap_champs[k[3]]}
ap_patch_data = { k:v for (k,v) in patch_data.items() if ap_champs[k[1]]}
all_ap_data = ap_region_data.values() + (ap_tier_data.values()) + ap_cross_data.values() + ap_patch_data.values()
#print(ap_champs)
#pca = PCA(n_components=2)
#reduction = pca.fit_transform(ap_champs.values())
#print(pca.explained_variance_ratio_)
grp = GaussianRandomProjection(2, random_state = 0)
grp.fit(all_ap_data)
region_reduction = grp.transform(ap_region_data.values())
tier_reduction = grp.transform(ap_tier_data.values())
cross_reduction = grp.transform(ap_cross_data.values())
patch_reduction = grp.transform(ap_patch_data.values())
region_json_data = []
for i in range(0,len(ap_region_data.keys())):
key = ap_region_data.keys()[i]
data = list(region_reduction[i])
num_games = region_games[key]
region_json_data.append( {
"patch":key[0],
"region":key[1],
#"tier":key[2],
"champion":key[2],
"coordinate":{
class RCAReducer():
def __init__(self, dataset, dataset_name, num_components=10):
self.dataset = dataset
self.dataset_name = dataset_name
self.labels = dataset.target
self.scaler = MinMaxScaler()
self.data = self.scaler.fit_transform(dataset.data)
self.n_samples, self.n_features = self.data.shape
self.reducer = GaussianRandomProjection(n_components=num_components)
def reduce(self):
self.reducer.fit(self.data)
self.reduced = self.scaler.fit_transform(self.reducer.transform(self.data))
return self.reduced
def benchmark(self, estimator, name, data):
t0 = time()
sample_size = 300
labels = self.labels
estimator.fit(data)
print('% 9s %.2fs %i %.3f %.3f %.3f %.3f %.3f %.3f'
% (name, (time() - t0), estimator.inertia_,
metrics.homogeneity_score(labels, estimator.labels_),
metrics.completeness_score(labels, estimator.labels_),
metrics.v_measure_score(labels, estimator.labels_),
metrics.adjusted_rand_score(labels, estimator.labels_),
metrics.adjusted_mutual_info_score(labels, estimator.labels_),
metrics.silhouette_score(data, estimator.labels_,
metric='euclidean',
sample_size=sample_size)))
def display_reduced_digits(self):
sys.stdout = open('out/RCAReduceDigitsOutput.txt', 'w')
print("RCA Reduction of %s:\n" % self.dataset_name)
print(40 * '-')
print("Length of 1 input vector before reduction: %d \n" % len(self.data.tolist()[0]))
print("Length of 1 input vector after reduction: %d \n" % len(self.reduced.tolist()[0]))
print("\nProjection axes:\n")
for i,axis in enumerate(self.reducer.components_.tolist()):
print("Axis %d:\n" % i, axis)
self.compute_plane_variance()
def compute_plane_variance(self):
points_along_dimension = self.reduced.T
for i,points in enumerate(points_along_dimension):
print("\nVariance of dimension %d:" % i)
print(np.var(points), "\n")
def display_reduced_iris(self):
sys.stdout = open('out/RCAReduceIrisOutput.txt', 'w')
print("RCA Reduction of %s:\n" % self.dataset_name)
print(40 * '-')
print("Length of 1 input vector before reduction: %d \n" % len(self.data.tolist()[0]))
print("Length of 1 input vector after reduction: %d \n" % len(self.reduced.tolist()[0]))
print("\nProjection axes:\n")
for i,axis in enumerate(self.reducer.components_.tolist()):
print("Axis %d:\n" % i, axis)
self.compute_plane_variance()
def reduce_crossvalidation_set(self, X_train, X_test):
self.reducer.fit(X_train)
reduced_X_train = self.scaler.transform(X_train)
reduced_X_test = self.scaler.transform(X_test)
return reduced_X_train, reduced_X_test
