def plot_IsoMap(objectives, n_neighbors, ax, name):
class_dummy = np.zeros(len(objectives))
visualizer = Manifold(manifold='isomap',
n_neighbors=n_neighbors,
classes=[name],
ax=ax)
visualizer.fit_transform(objectives, class_dummy)
visualizer.show()
def manifold_embeding(data,
name=name,
location=location,
target=target,
manifold=manifold,
n_neighbors=n_neighbors):
classes = data[target].unique()
data[target].replace(0, "0", inplace=True)
le = preprocessing.LabelEncoder()
le.fit(data[target])
y = le.transform(data[target])
data_test = data.drop([target], axis=1)
ax = plt.axes()
vizualisation = Manifold(classes=classes,
manifold=manifold,
n_neighbors=n_neighbors,
ax=ax)
vizualisation.fit_transform(data_test, y)
plot_name = f"Manifold_{manifold}_{name}.png"
vizualisation.show(outpath=os.path.join(location, plot_name))
plt.close()
def plot_MDS(objectives, ax, name):
class_dummy = np.zeros(len(objectives))
visualizer = Manifold(manifold='mds', classes=[name], ax=ax)
visualizer.fit_transform(objectives, class_dummy)
visualizer.show()
visualizer.transform(dfrad.iloc[:, :50]) # Transform the data
visualizer.show() # Finalize and render the figure
#MANIFOLD - No balanced
from yellowbrick.features import Manifold
classes = [1, 0]
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder(
) #label_encoder object knows how to understand word labels.
dfrad['activities'] = label_encoder.fit_transform(
dfrad['activities']) #Encode labels
dfrad['activities'].unique()
viz = Manifold(manifold="tsne", classes=classes) # Instantiate the visualizer
viz.fit_transform(dfrad.iloc[:, :100],
dfrad['activities']) # Fit the data to the visualizer
viz.show() # Finalize and render the figure
# =============================================================================
# #CLASS BALANCE - Balanced (DO NOT USE. Draft)
# =============================================================================
#m2_train_s_bk_bal #dataframe
#m2_test_s_bk_bal #dataframe
#ai_train_rav_bal = np.ravel(y_rus)
#ai_test_rav_bal = np.ravel(y_rus2)
y_rus_df = pd.DataFrame(y_rus)
frames_bal = [m2_train_s_bk_bal, y_rus_df]
import pandas as pd
dfrad_bal = pd.concat(frames_bal, axis=1)
dfrad_bal = dfrad.dropna()