def plot_single_projection(holder,
labels,
class_name='Antioxidants',
fp_name='fps_e3fp_1024bit',
standardize=True,
preprocess_lda='PCA'):
'''
holder should be a dictionary with df's as values and fp-filenames as keys
labels should be a mapping of DrugCombID: ATC_class
'''
from mlxtend.preprocessing import standardize as st
from sklearn.preprocessing import LabelEncoder
from sklearn.cluster import KMeans
from mlxtend.feature_extraction import LinearDiscriminantAnalysis #in sklearn LDA i'd need to add a dummy class if i want to have 2 components after trasnformation
from scipy.spatial.distance import pdist
df_cluster = holder[fp_name].copy()
df_cluster = df_cluster.loc[df_cluster.index.isin(labels.keys())]
df_cluster = df_cluster[~df_cluster.index.duplicated(keep='last')]
if standardize:
classes = df_cluster.index.copy()
df_cluster.reset_index(inplace=True, drop=True)
df_cluster = st(df_cluster)
else:
classes = df_cluster.index.copy()
df_cluster[
'classes'] = classes # our classes are mapped to index in labels dictionary
df_cluster['classes'] = df_cluster['classes'].map(labels)
df_cluster.loc[df_cluster.classes != class_name,
'classes'] = 'not ' + 'class_name'
#dummy = [0]*(df_cluster.shape[1]-1) + ['dummy']
#df_cluster.loc[df_cluster.shape[0]] = dummy
# change labels from str to int
enc = LabelEncoder()
real_classes = df_cluster.loc[:, 'classes']
df_cluster.loc[:, 'classes'] = enc.fit_transform(df_cluster['classes'])
classes = df_cluster.pop('classes')
if preprocess_lda == 'PLS':
from sklearn.cross_decomposition import PLSRegression
pls = PLSRegression(n_components=10, scale=False)
temp = pls.fit_transform(df_cluster.values, classes.values)[0]
elif preprocess_lda == 'PCA':
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, svd_solver='full', whiten=False)
temp = pca.fit_transform(df_cluster.values)
elif preprocess_lda == 'kernelPCA':
from sklearn.decomposition import KernelPCA
pca = KernelPCA(kernel="rbf", gamma=5)
temp = pca.fit_transform(df_cluster.values)
elif preprocess_lda == 'NONE':
temp = df_cluster.values
elif preprocess_lda == 'NCA':
from sklearn.neighbors import NeighborhoodComponentsAnalysis
nca = NeighborhoodComponentsAnalysis()
temp = nca.fit_transform(df_cluster.values, classes.values)
#lda = LinearDiscriminantAnalysis(solver='eigen', shrinkage='auto')
#lda.fit(temp, classes.values)
#temp1 = lda.transform(temp)
lda = LinearDiscriminantAnalysis(n_discriminants=2)
lda.fit(temp, classes.values)
temp = lda.transform(temp)
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore',
'Casting complex values to real discards the imaginary part')
temp = temp.astype(np.float) # in case of complex numbers///
df = pd.DataFrame(index=df_cluster.index, columns=[0, 1], data=temp)
df['classes'] = real_classes
km = KMeans(init='k-means++', n_clusters=1, n_init=10)
km.fit(df.loc[df.classes != class_name, [0, 1]])
km1 = KMeans(init='k-means++', n_clusters=1, n_init=10)
km1.fit(df.loc[df.classes == class_name, [0, 1]])
d = pdist([km.cluster_centers_[0], km1.cluster_centers_[0]])
d = str(round(d[0], 3))
fig, ax = plt.subplots(figsize=(6, 6))
ax.scatter(df.loc[df.classes != class_name, 0],
df.loc[df.classes != class_name, 1],
marker=',',
color='grey')
ax.scatter(df.loc[df.classes == class_name, 0],
df.loc[df.classes == class_name, 1],
marker=',',
color='orange')
ax.scatter(km.cluster_centers_[:, 0],
km.cluster_centers_[:, 1],
marker='X',
color='green',
linewidths=30)
ax.scatter(km1.cluster_centers_[:, 0],
km1.cluster_centers_[:, 1],
marker='X',
color='red',
linewidths=30)
fig.suptitle(class_name + ' ' + d)
return fig
def LDA:
lda = LinearDiscriminantAnalysis(n_discriminants=2)
lda.fit(X_train, y_train)
X_lda = lda.transform(X)
