def __do_perform(self,
custom_out=None,
main_experiment=None
): # ./output/ICA/clustering//{}', ICAExperiment
if custom_out is not None:
# if not os.path.exists(custom_out):
# os.makedirs(custom_out)
self._old_out = self._out # './output/ICA/{}'
self._out = custom_out # ./output/ICA/clustering//{}'
elif self._old_out is not None:
self._out = self._old_out
if main_experiment is not None:
self.log("Performing {} as part of {}".format(
self.experiment_name(),
main_experiment.experiment_name())) # 'clustering', 'ICA'
else:
self.log("Performing {}".format(self.experiment_name()))
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-3/blob/master/clustering.py
# %% Data for 1-3
sse = defaultdict(list)
ll = defaultdict(list)
bic = defaultdict(list)
sil = defaultdict(lambda: defaultdict(list))
sil_s = np.empty(shape=(2 * len(self._clusters) *
self._details.ds.training_x.shape[0], 4),
dtype='<U21')
acc = defaultdict(lambda: defaultdict(float))
adj_mi = defaultdict(lambda: defaultdict(float))
km = kmeans(random_state=self._details.seed)
gmm = GMM(random_state=self._details.seed)
st = clock()
j = 0
for k in self._clusters:
km.set_params(n_clusters=k)
gmm.set_params(n_components=k)
km.fit(
self._details.ds.training_x
) # cluster the ICA-transformed input features using kMeans with varying K
gmm.fit(
self._details.ds.training_x
) # cluster the ICA-transformed input features using GMM with varying k
km_labels = km.predict(
self._details.ds.training_x
) # give each ICA-transformed input feature a label
gmm_labels = gmm.predict(self._details.ds.training_x)
sil[k]['Kmeans'] = sil_score(
self._details.ds.training_x, km_labels
) # compute mean silhouette score for all ICA-transformed input features
sil[k]['GMM'] = sil_score(self._details.ds.training_x, gmm_labels)
km_sil_samples = sil_samples(
self._details.ds.training_x, km_labels
) # compute silhouette score for each ICA-transformed input feature
gmm_sil_samples = sil_samples(self._details.ds.training_x,
gmm_labels)
# There has got to be a better way to do this, but I can't brain right now
for i, x in enumerate(km_sil_samples):
sil_s[j] = [
k, 'Kmeans', round(x, 6), km_labels[i]
] # record the silhouette score x for each instance i given its label kn_labels[i] by kMeans with value k
j += 1
for i, x in enumerate(gmm_sil_samples):
sil_s[j] = [k, 'GMM', round(x, 6), gmm_labels[i]]
j += 1
sse[k] = [
km.score(self._details.ds.training_x)
] # score (opposite of the value of X on the k-Means objective (what is the objective???)
ll[k] = [gmm.score(self._details.ds.training_x)
] # per-sample average log-likelihood
bic[k] = [
gmm.bic(self._details.ds.training_x)
] # bayesian information criterion (review ???) on the input X
acc[k]['Kmeans'] = cluster_acc(
self._details.ds.training_y, km_labels
) # compute the accuracy of the clustering algorithm on the ICA-transformed data (against the original y-label) if it predicted the majority y-label for each cluster
acc[k]['GMM'] = cluster_acc(self._details.ds.training_y,
gmm_labels)
adj_mi[k]['Kmeans'] = ami(
self._details.ds.training_y, km_labels
) # compute the adjusted mutual information between the true labels and the cluster predicted labels (how well does clustering match truth)
adj_mi[k]['GMM'] = ami(self._details.ds.training_y, gmm_labels)
self.log("Cluster: {}, time: {}".format(k, clock() - st))
sse = (-pd.DataFrame(sse)).T
sse.index.name = 'k'
sse.columns = ['{} sse (left)'.format(self._details.ds_readable_name)
] # Bank sse (left)
ll = pd.DataFrame(ll).T
ll.index.name = 'k'
ll.columns = [
'{} log-likelihood'.format(self._details.ds_readable_name)
] # Bank log-likelihood
bic = pd.DataFrame(bic).T
bic.index.name = 'k'
bic.columns = ['{} BIC'.format(self._details.ds_readable_name)
] # Bank BIC
sil = pd.DataFrame(sil).T
sil_s = pd.DataFrame(sil_s, columns=['k', 'type', 'score',
'label']).set_index('k') #.T
# sil_s = sil_s.T
acc = pd.DataFrame(acc).T
adj_mi = pd.DataFrame(adj_mi).T
sil.index.name = 'k'
sil_s.index.name = 'k'
acc.index.name = 'k'
adj_mi.index.name = 'k'
# write scores to files
sse.to_csv(self._out.format('{}_sse.csv'.format(
self._details.ds_name)))
ll.to_csv(
self._out.format('{}_logliklihood.csv'.format(
self._details.ds_name)))
bic.to_csv(self._out.format('{}_bic.csv'.format(
self._details.ds_name)))
sil.to_csv(
self._out.format('{}_sil_score.csv'.format(self._details.ds_name)))
sil_s.to_csv(
self._out.format('{}_sil_samples.csv'.format(
self._details.ds_name)))
acc.to_csv(self._out.format('{}_acc.csv'.format(
self._details.ds_name)))
adj_mi.to_csv(
self._out.format('{}_adj_mi.csv'.format(self._details.ds_name)))
# %% NN fit data (2,3)
# train a NN on clustered data
grid = {
'km__n_clusters': self._clusters,
'NN__alpha': self._nn_reg,
'NN__hidden_layer_sizes': self._nn_arch
}
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=self._details.seed)
km = kmeans(random_state=self._details.seed,
n_jobs=self._details.threads)
pipe = Pipeline(
[('km', km), ('NN', mlp)], memory=experiments.pipeline_memory
) # run a NN on the clustered data (only on the cluster labels, or input features + cluster labels???)
gs, _ = self.gs_with_best_estimator(
pipe, grid, type='kmeans') # write the best NN to file
self.log("KMmeans Grid search complete")
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(
self._out.format('{}_cluster_kmeans.csv'.format(
self._details.ds_name))
) # write grid search results --> bank_cluster_kmeans.csv
grid = {
'gmm__n_components': self._clusters,
'NN__alpha': self._nn_reg,
'NN__hidden_layer_sizes': self._nn_arch
}
mlp = MLPClassifier(activation='relu',
max_iter=2000,
early_stopping=True,
random_state=self._details.seed)
gmm = CustomGMM(random_state=self._details.seed)
pipe = Pipeline([('gmm', gmm), ('NN', mlp)],
memory=experiments.pipeline_memory)
gs, _ = self.gs_with_best_estimator(
pipe, grid, type='gmm') # write the best NN to file
self.log("GMM search complete")
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(
self._out.format('{}_cluster_GMM.csv'.format(
self._details.ds_name))
) # write grid search results --> bank_cluster_GMM.csv
# %% For chart 4/5
# perform TSNE D.R on training data (why???)
self._details.ds.training_x2D = TSNE(
verbose=10, random_state=self._details.seed).fit_transform(
self._details.ds.training_x)
ds_2d = pd.DataFrame(
np.hstack((self._details.ds.training_x2D,
np.atleast_2d(self._details.ds.training_y).T)),
columns=['x', 'y', 'target']
) # prepare NN-learnable data using TSNE D.R'd input features + label
ds_2d.to_csv(
self._out.format('{}_2D.csv'.format(
self._details.ds_name))) # --> bank_2D.csv
self.log("Done")
def __do_perform(self, custom_out=None, main_experiment=None):
if custom_out is not None:
# if not os.path.exists(custom_out):
# os.makedirs(custom_out)
self._old_out = self._out
self._out = custom_out
elif self._old_out is not None:
self._out = self._old_out
if main_experiment is not None:
self.log("Performing {} as part of {}".format(self.experiment_name(), main_experiment.experiment_name()))
else:
self.log("Performing {}".format(self.experiment_name()))
# Adapted from https://github.com/JonathanTay/CS-7641-assignment-3/blob/master/clustering.py
# %% Data for 1-3
sse = defaultdict(list)
ll = defaultdict(list)
bic = defaultdict(list)
sil = defaultdict(lambda: defaultdict(list))
sil_s = np.empty(shape=(2*len(self._clusters)*self._details.ds.training_x.shape[0],4), dtype='<U21')
acc = defaultdict(lambda: defaultdict(float))
adj_mi = defaultdict(lambda: defaultdict(float))
km = kmeans(random_state=self._details.seed)
gmm = GMM(random_state=self._details.seed)
st = clock()
j = 0
for k in self._clusters:
km.set_params(n_clusters=k)
gmm.set_params(n_components=k)
km.fit(self._details.ds.training_x)
gmm.fit(self._details.ds.training_x)
km_labels = km.predict(self._details.ds.training_x)
gmm_labels = gmm.predict(self._details.ds.training_x)
sil[k]['Kmeans'] = sil_score(self._details.ds.training_x, km_labels)
sil[k]['GMM'] = sil_score(self._details.ds.training_x, gmm_labels)
km_sil_samples = sil_samples(self._details.ds.training_x, km_labels)
gmm_sil_samples = sil_samples(self._details.ds.training_x, gmm_labels)
# There has got to be a better way to do this, but I can't brain right now
for i, x in enumerate(km_sil_samples):
sil_s[j] = [k, 'Kmeans', round(x, 6), km_labels[i]]
j += 1
for i, x in enumerate(gmm_sil_samples):
sil_s[j] = [k, 'GMM', round(x, 6), gmm_labels[i]]
j += 1
sse[k] = [km.score(self._details.ds.training_x)]
ll[k] = [gmm.score(self._details.ds.training_x)]
bic[k] = [gmm.bic(self._details.ds.training_x)]
acc[k]['Kmeans'] = cluster_acc(self._details.ds.training_y, km_labels)
acc[k]['GMM'] = cluster_acc(self._details.ds.training_y, gmm_labels)
adj_mi[k]['Kmeans'] = ami(self._details.ds.training_y, km_labels)
adj_mi[k]['GMM'] = ami(self._details.ds.training_y, gmm_labels)
self.log("Cluster: {}, time: {}".format(k, clock() - st))
sse = (-pd.DataFrame(sse)).T
sse.index.name = 'k'
sse.columns = ['{} sse (left)'.format(self._details.ds_readable_name)]
ll = pd.DataFrame(ll).T
ll.index.name = 'k'
ll.columns = ['{} log-likelihood'.format(self._details.ds_readable_name)]
bic = pd.DataFrame(bic).T
bic.index.name = 'k'
bic.columns = ['{} BIC'.format(self._details.ds_readable_name)]
sil = pd.DataFrame(sil).T
sil_s = pd.DataFrame(sil_s, columns=['k', 'type', 'score', 'label']).set_index('k') #.T
# sil_s = sil_s.T
acc = pd.DataFrame(acc).T
adj_mi = pd.DataFrame(adj_mi).T
sil.index.name = 'k'
sil_s.index.name = 'k'
acc.index.name = 'k'
adj_mi.index.name = 'k'
sse.to_csv(self._out.format('{}_sse.csv'.format(self._details.ds_name)))
ll.to_csv(self._out.format('{}_logliklihood.csv'.format(self._details.ds_name)))
bic.to_csv(self._out.format('{}_bic.csv'.format(self._details.ds_name)))
sil.to_csv(self._out.format('{}_sil_score.csv'.format(self._details.ds_name)))
sil_s.to_csv(self._out.format('{}_sil_samples.csv'.format(self._details.ds_name)))
acc.to_csv(self._out.format('{}_acc.csv'.format(self._details.ds_name)))
adj_mi.to_csv(self._out.format('{}_adj_mi.csv'.format(self._details.ds_name)))
# %% NN fit data (2,3)
grid = {'km__n_clusters': self._clusters, 'NN__alpha': self._nn_reg, 'NN__hidden_layer_sizes': self._nn_arch}
mlp = MLPClassifier(activation='relu', max_iter=2000, early_stopping=True, random_state=self._details.seed)
km = kmeans(random_state=self._details.seed, n_jobs=self._details.threads)
pipe = Pipeline([('km', km), ('NN', mlp)], memory=experiments.pipeline_memory)
gs, _ = self.gs_with_best_estimator(pipe, grid, type='kmeans')
self.log("KMmeans Grid search complete")
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(self._out.format('{}_cluster_kmeans.csv'.format(self._details.ds_name)))
grid = {'gmm__n_components': self._clusters, 'NN__alpha': self._nn_reg, 'NN__hidden_layer_sizes': self._nn_arch}
mlp = MLPClassifier(activation='relu', max_iter=2000, early_stopping=True, random_state=self._details.seed)
gmm = CustomGMM(random_state=self._details.seed)
pipe = Pipeline([('gmm', gmm), ('NN', mlp)], memory=experiments.pipeline_memory)
gs, _ = self.gs_with_best_estimator(pipe, grid, type='gmm')
self.log("GMM search complete")
tmp = pd.DataFrame(gs.cv_results_)
tmp.to_csv(self._out.format('{}_cluster_GMM.csv'.format(self._details.ds_name)))
# %% For chart 4/5
self._details.ds.training_x2D = TSNE(verbose=10, random_state=self._details.seed).fit_transform(
self._details.ds.training_x
)
ds_2d = pd.DataFrame(np.hstack((self._details.ds.training_x2D, np.atleast_2d(self._details.ds.training_y).T)),
columns=['x', 'y', 'target'])
ds_2d.to_csv(self._out.format('{}_2D.csv'.format(self._details.ds_name)))
self.log("Done")
data_st = clock()
# fit the credit data
km.fit(dataX)
km_labels = km.predict(dataX)
gmm.fit(dataX)
gmm_labels = gmm.predict(dataX)
# save the labels
labels[k]['Kmeans'] = km_labels
labels[k]['GMM'] = gmm_labels
sil[k]['Kmeans'] = sil_score(dataX, km_labels)
sil[k]['GMM'] = sil_score(dataX, gmm_labels)
km_sil_samples = sil_samples(dataX, km_labels)
gmm_sil_samples = sil_samples(dataX, gmm_labels)
for i, x in enumerate(km_sil_samples):
sil_samp[j] = [k, 'Kmeans', round(x, 6), km_labels[i]]
j += 1
for i, x in enumerate(gmm_sil_samples):
sil_samp[j] = [k, 'GMM', round(x, 6), gmm_labels[i]]
j += 1
sse[k] = km.score(dataX)
ll[k] = gmm.score(dataX)
bic[k] = gmm.bic(dataX)
acc[k]['Kmeans'] = cluster_acc(dataY,km.predict(dataX))
acc[k]['GMM'] = cluster_acc(dataY,gmm.predict(dataX))
adj_mi[k]['Kmeans'] = ami(dataY,km.predict(dataX))
adj_mi[k]['GMM'] = ami(dataY,gmm.predict(dataX))
