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Adding greater visibility of plots, as well as scree plots for greater comparison ability #25

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4441426
Update __init__.py
patjiang Aug 24, 2024
a83b520
Update __init__.py
patjiang Aug 24, 2024
054cb12
Update __init__.py
patjiang Aug 24, 2024
5ee7bbd
Update __init__.py
patjiang Aug 25, 2024
5641b50
Update __init__.py
patjiang Aug 25, 2024
ea8d4a7
Update __init__.py
patjiang Aug 29, 2024
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61 changes: 54 additions & 7 deletions contrastive/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,8 +24,13 @@ def get_data(self):
return self.data
def get_bg(self):
return self.bg
def get_bg_cov(self):
return self.bg_cov
def get_fg(self):
return self.fg
def get_fg_cov(self):
return self.fg_cov

def get_active_labels(self):
return self.active_labels
def get_pca_directions(self):
Expand All @@ -41,20 +46,28 @@ def standardize_array(self, array):
return np.nan_to_num(standardized_array)

#stores
def __init__(self, n_components=2, standardize=True, verbose=False):
def __init__(self, n_components=2, standardize=True, verbose=False, show_scree = True):
self.standardize = standardize
self.n_components = n_components
self.verbose = verbose
self.fitted = False
self.show = show_scree
self.rawvar = 0
self.pervar = 0
self.pcs = []

self.rawvars = []
self.pervars = []
self.full_pc = []

"""
Finds the covariance matrices of the foreground and background datasets,
and then transforms the foreground dataset based on the principal contrastive components

Parameters: see self.fit() and self.transform() for parameter description
"""
def fit_transform(self, foreground, background, plot=False, gui=False, alpha_selection='auto', n_alphas=40, max_log_alpha=3, n_alphas_to_return=4, active_labels = None, colors=None, legend=None, alpha_value=None, return_alphas=False):
self.fit(foreground, background)
def fit_transform(self, foreground, background, preprocess_with_pca_dim=None, plot=False, gui=False, alpha_selection='auto', n_alphas=40, max_log_alpha=3, n_alphas_to_return=4, active_labels = None, colors=None, legend=None, alpha_value=None, return_alphas=False):
self.fit(foreground, background, preprocess_with_pca_dim)
return self.transform(dataset=self.fg, alpha_selection=alpha_selection, n_alphas=n_alphas, max_log_alpha=max_log_alpha, n_alphas_to_return=n_alphas_to_return, plot=plot, gui=gui, active_labels=active_labels, colors=colors, legend=legend, alpha_value=alpha_value, return_alphas=return_alphas)

"""
Expand Down Expand Up @@ -215,15 +228,36 @@ def update(value):
raise ImportError("Something wrong while loading matplotlib.pyplot! You probably don't have plotting libraries installed.")
if (alpha_selection=='auto'):
transformed_data, best_alphas = self.automated_cpca(dataset, n_alphas_to_return, n_alphas, max_log_alpha)
plt.figure(figsize=[14,3])
if self.show:
plt.figure(figsize=[8,14])
else:
plt.figure(figsize=[14,3])

for j, fg in enumerate(transformed_data):
plt.subplot(1,4,j+1)
if self.show:
plt.subplot(4,2,2*(j+1) - 1)
else:
plt.subplot(1,4,j+1)
for i, l in enumerate(np.sort(np.unique(self.active_labels))):
idx = np.where(self.active_labels==l)
plt.scatter(fg[idx,0],fg[idx,1], color=self.colors[i%len(self.colors)], alpha=0.6, label='Class '+str(i))
plt.xlabel(f"PC1: %var = {round(self.pervars[j][0], 2)}, raw = {round(self.rawvars[j][0], 2)}")
plt.ylabel(f"PC2: %var = {round(self.pervars[j][1], 2)}, raw = {round(self.rawvars[j][1], 2)}")
plt.title('Alpha='+str(np.round(best_alphas[j],2)))
if len(np.unique(self.active_labels))>1:
plt.legend()
if len(np.unique(self.active_labels))>1:
plt.legend()

if self.show:
plt.subplot(4,2,2*(j+1))
plt.plot(np.arange(1,(len(self.full_pc[j]) + 1)), self.full_pc[j], 'o-c', label = 'screeplot')
plt.xlabel("Index of Principal Component")
plt.ylabel("Raw Explained Variance")
plt.title('Scree Plot of Eigenvalues')




plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0.4, hspace=0.4)
plt.show()
elif (alpha_selection=='manual'):
fg = self.cpca_alpha(dataset, alpha_value)
Expand All @@ -232,6 +266,8 @@ def update(value):
idx = np.where(self.active_labels==l)
plt.scatter(fg[idx,0],fg[idx,1], color=self.colors[i%len(self.colors)], alpha=0.6, label='Class '+str(i))
plt.title('Alpha=' + str(alpha_value))
plt.xlabel(f"PC1: %var = {round(self.pervar[0], 2)}, raw = {round(self.rawvar[0], 2)}")
plt.ylabel(f"PC2: %var = {round(self.pervar[1], 2)}, raw = {round(self.rawvar[1], 2)}")
plt.legend()
plt.show()

Expand Down Expand Up @@ -265,9 +301,14 @@ def automated_cpca(self, dataset, n_alphas_to_return, n_alphas, max_log_alpha):
best_alphas, all_alphas, _, _ = self.find_spectral_alphas(n_alphas, max_log_alpha, n_alphas_to_return)
best_alphas = np.concatenate(([0], best_alphas)) #one of the alphas is always alpha=0
data_to_plot = []
raw = []
per = []
for alpha in best_alphas:
transformed_dataset = self.cpca_alpha(dataset=dataset, alpha=alpha)
data_to_plot.append(transformed_dataset)
self.rawvars.append(self.rawvar)
self.pervars.append(self.pervar)
self.full_pc.append(self.pcs)
return data_to_plot, best_alphas

"""
Expand All @@ -281,6 +322,9 @@ def all_cpca(self, dataset, n_alphas, max_log_alpha):
for alpha in alphas:
transformed_dataset = self.cpca_alpha(dataset=dataset, alpha=alpha)
data_to_plot.append(transformed_dataset)
self.rawvars.append(self.rawvar)
self.pervars.append(self.pervar)
self.full_pc.append(self.pcs)
return data_to_plot, alphas

"""
Expand All @@ -293,6 +337,9 @@ def cpca_alpha(self, dataset, alpha=1):
w, v = LA.eig(sigma)
eig_idx = np.argpartition(w, -n_components)[-n_components:]
eig_idx = eig_idx[np.argsort(-w[eig_idx])]
self.pcs = -1*np.sort(-w)
self.rawvar = w[eig_idx]
self.pervar = [i/np.sum(w) for i in self.rawvar]
v_top = v[:,eig_idx]
reduced_dataset = dataset.dot(v_top)
reduced_dataset[:,0] = reduced_dataset[:,0]*np.sign(reduced_dataset[0,0])
Expand Down