def projectEnsemble(form_values, X, labels, control_points, dataset_name):
#Normalize weights
weight1 = float(form_values['LAMP_weight']) / (
float(form_values['LAMP_weight']) + float(form_values['LSP_weight']) +
float(form_values['PLMP_weight']))
weight2 = float(form_values['LSP_weight']) / (
float(form_values['LAMP_weight']) + float(form_values['LSP_weight']) +
float(form_values['PLMP_weight']))
weight3 = float(form_values['PLMP_weight']) / (
float(form_values['LAMP_weight']) + float(form_values['LSP_weight']) +
float(form_values['PLMP_weight']))
form_values['LAMP_weight'] = weight1
form_values['LSP_weight'] = weight2
form_values['PLMP_weight'] = weight3
projection_json = {}
ensemble = np.zeros((X.shape[0], 2))
# X = min_max_scaler.fit_transform(X) # min_max
if float(form_values['LAMP_weight']) != 0.0:
lamp_proj = Lamp(Xdata=X,
control_points=control_points[:, :2],
label=False)
x_projected = lamp_proj.fit()
# x_projected = min_max_scaler.fit_transform(x_projected) # min_max
np.savetxt("../../datasets/" + dataset_name + '/' + dataset_name +
"_projected_LAMP.csv",
x_projected,
delimiter=",")
projection_json['LAMP'] = {
'x': x_projected[:, 0].tolist(),
'y': x_projected[:, 1].tolist(),
'labels': labels.reshape(-1).tolist()
}
ensemble += x_projected * float(form_values['LAMP_weight'])
if float(form_values['LSP_weight']) != 0.0:
os.system("./runLSP.m " + dataset_name)
x_projected = pd.read_csv("../../datasets/" + dataset_name + '/' +
dataset_name + '_projected_octave.csv',
header=None).values
# x_projected = min_max_scaler.fit_transform(x_projected) # min_max
np.savetxt("../../datasets/" + dataset_name + '/' + dataset_name +
"_projected_LSP.csv",
x_projected,
delimiter=",")
projection_json['LSP'] = {
'x': x_projected[:, 0].tolist(),
'y': x_projected[:, 1].tolist(),
'labels': labels.reshape(-1).tolist()
}
ensemble += x_projected * float(form_values['LSP_weight'])
if float(form_values['PLMP_weight']) != 0.0:
plmp = PLMP(X, labels.reshape(-1),
control_points[:, 2].reshape(-1).astype(int),
control_points[:, :2])
plmp.project()
x_projected = plmp.get_projection()
# x_projected = min_max_scaler.fit_transform(x_projected) # min_max
np.savetxt("../../datasets/" + dataset_name + '/' + dataset_name +
"_projected_PLMP.csv",
x_projected,
delimiter=",")
projection_json['PLMP'] = {
'x': x_projected[:, 0].tolist(),
'y': x_projected[:, 1].tolist(),
'labels': labels.reshape(-1).tolist()
}
ensemble += x_projected * float(form_values['PLMP_weight'])
projection_json['Ensemble'] = {
'x': ensemble[:, 0].tolist(),
'y': ensemble[:, 1].tolist(),
'labels': labels.reshape(-1).tolist()
}
# print("evaluating metrics...")
metrics = getMetricsForAllProjections(X, projection_json, labels, 8)
# print("evaluating metrics finished")
data = {
'projections': projection_json,
'metrics': metrics
} #{'NP': [], 'T': [], 'NH': []}
return data
from scipy.stats.mstats import zscore import matplotlib.pyplot as plt from lamp import Lamp iris = datasets.load_iris() x = iris.data y = iris.target # randomly chosing the control points sample_size = 15 samples = np.random.randint(0, high=x.shape[0], size=(sample_size, )) ##### projecting control points with MDS ##### ctp_mds = MDS(n_components=2) ctp_samples = ctp_mds.fit_transform(x[samples] - np.average(x[samples])) # including ids of control points as the last column of the projected control points ctp_samples = np.hstack((ctp_samples, samples.reshape(sample_size, 1))) # including labels as the last column data = np.hstack((x, y.reshape(y.shape[0], 1))) ##### using Lamp lamp_proj = Lamp(Xdata=data, control_points=ctp_samples, label=True) data_proj = lamp_proj.fit() plt.scatter(data_proj[:, 0], data_proj[:, 1], c=y) plt.scatter(ctp_samples[:, 0], ctp_samples[:, 1], c='r', s=2) plt.show()
######## Generating the Control Points #############
##### projecting control points with MDS #####
ctp_mds = MDS(n_components=2)
ctp_samples = ctp_mds.fit_transform(x[samples] - np.average(x[samples]))
# including ids of control points as the last column of the projected control points
ctp_samples = np.hstack((ctp_samples, samples.reshape(sample_size, 1)))
######## Performing the projections in two stages #############
##### Instantiating Lamp
lamp_proj = Lamp(Xdata=x[:num_points], control_points=ctp_samples, label=True)
##### projecting first half of the data
data_proj = lamp_proj.fit()
plt.title('First Half')
plt.scatter(data_proj[:, 0], data_proj[:, 1], c=data_proj[:, -1])
plt.scatter(ctp_samples[:, 0], ctp_samples[:, 1], c='r', s=2)
##### projecting second half of the data
data_proj = lamp_proj.fit(Xdata=x[num_points:])
plt.title('Second Half')
plt.scatter(data_proj[:, 0],
data_proj[:, 1],
marker='o',
edgecolors='r',
c=data_proj[:, -1])
plt.show()
