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bench_plot_incremental_pca.py
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bench_plot_incremental_pca.py
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"""
========================
IncrementalPCA benchmark
========================
Benchmarks for IncrementalPCA
"""
import numpy as np
import gc
from time import time
from collections import defaultdict
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import IncrementalPCA, RandomizedPCA, PCA
def plot_results(X, y, label):
plt.plot(X, y, label=label, marker='o')
def benchmark(estimator, data):
gc.collect()
print("Benching %s" % estimator)
t0 = time()
estimator.fit(data)
training_time = time() - t0
data_t = estimator.transform(data)
data_r = estimator.inverse_transform(data_t)
reconstruction_error = np.mean(np.abs(data - data_r))
return {'time': training_time, 'error': reconstruction_error}
def plot_feature_times(all_times, batch_size, all_components, data):
plt.figure()
plot_results(all_components, all_times['pca'], label="PCA")
plot_results(all_components, all_times['ipca'],
label="IncrementalPCA, bsize=%i" % batch_size)
plot_results(all_components, all_times['rpca'], label="RandomizedPCA")
plt.legend(loc="upper left")
plt.suptitle("Algorithm runtime vs. n_components\n \
LFW, size %i x %i" % data.shape)
plt.xlabel("Number of components (out of max %i)" % data.shape[1])
plt.ylabel("Time (seconds)")
def plot_feature_errors(all_errors, batch_size, all_components, data):
plt.figure()
plot_results(all_components, all_errors['pca'], label="PCA")
plot_results(all_components, all_errors['ipca'],
label="IncrementalPCA, bsize=%i" % batch_size)
plot_results(all_components, all_errors['rpca'], label="RandomizedPCA")
plt.legend(loc="lower left")
plt.suptitle("Algorithm error vs. n_components\n"
"LFW, size %i x %i" % data.shape)
plt.xlabel("Number of components (out of max %i)" % data.shape[1])
plt.ylabel("Mean absolute error")
def plot_batch_times(all_times, n_features, all_batch_sizes, data):
plt.figure()
plot_results(all_batch_sizes, all_times['pca'], label="PCA")
plot_results(all_batch_sizes, all_times['rpca'], label="RandomizedPCA")
plot_results(all_batch_sizes, all_times['ipca'], label="IncrementalPCA")
plt.legend(loc="lower left")
plt.suptitle("Algorithm runtime vs. batch_size for n_components %i\n \
LFW, size %i x %i" % (
n_features, data.shape[0], data.shape[1]))
plt.xlabel("Batch size")
plt.ylabel("Time (seconds)")
def plot_batch_errors(all_errors, n_features, all_batch_sizes, data):
plt.figure()
plot_results(all_batch_sizes, all_errors['pca'], label="PCA")
plot_results(all_batch_sizes, all_errors['ipca'], label="IncrementalPCA")
plt.legend(loc="lower left")
plt.suptitle("Algorithm error vs. batch_size for n_components %i\n \
LFW, size %i x %i" % (
n_features, data.shape[0], data.shape[1]))
plt.xlabel("Batch size")
plt.ylabel("Mean absolute error")
def fixed_batch_size_comparison(data):
all_features = [i.astype(int) for i in np.linspace(data.shape[1] // 10,
data.shape[1], num=5)]
batch_size = 1000
# Compare runtimes and error for fixed batch size
all_times = defaultdict(list)
all_errors = defaultdict(list)
for n_components in all_features:
pca = PCA(n_components=n_components)
rpca = RandomizedPCA(n_components=n_components, random_state=1999)
ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)
results_dict = {k: benchmark(est, data) for k, est in [('pca', pca),
('ipca', ipca),
('rpca', rpca)]}
for k in sorted(results_dict.keys()):
all_times[k].append(results_dict[k]['time'])
all_errors[k].append(results_dict[k]['error'])
plot_feature_times(all_times, batch_size, all_features, data)
plot_feature_errors(all_errors, batch_size, all_features, data)
def variable_batch_size_comparison(data):
batch_sizes = [i.astype(int) for i in np.linspace(data.shape[0] // 10,
data.shape[0], num=10)]
for n_components in [i.astype(int) for i in
np.linspace(data.shape[1] // 10,
data.shape[1], num=4)]:
all_times = defaultdict(list)
all_errors = defaultdict(list)
pca = PCA(n_components=n_components)
rpca = RandomizedPCA(n_components=n_components, random_state=1999)
results_dict = {k: benchmark(est, data) for k, est in [('pca', pca),
('rpca', rpca)]}
# Create flat baselines to compare the variation over batch size
all_times['pca'].extend([results_dict['pca']['time']] *
len(batch_sizes))
all_errors['pca'].extend([results_dict['pca']['error']] *
len(batch_sizes))
all_times['rpca'].extend([results_dict['rpca']['time']] *
len(batch_sizes))
all_errors['rpca'].extend([results_dict['rpca']['error']] *
len(batch_sizes))
for batch_size in batch_sizes:
ipca = IncrementalPCA(n_components=n_components,
batch_size=batch_size)
results_dict = {k: benchmark(est, data) for k, est in [('ipca',
ipca)]}
all_times['ipca'].append(results_dict['ipca']['time'])
all_errors['ipca'].append(results_dict['ipca']['error'])
plot_batch_times(all_times, n_components, batch_sizes, data)
# RandomizedPCA error is always worse (approx 100x) than other PCA
# tests
plot_batch_errors(all_errors, n_components, batch_sizes, data)
faces = fetch_lfw_people(resize=.2, min_faces_per_person=5)
# limit dataset to 5000 people (don't care who they are!)
X = faces.data[:5000]
n_samples, h, w = faces.images.shape
n_features = X.shape[1]
X -= X.mean(axis=0)
X /= X.std(axis=0)
fixed_batch_size_comparison(X)
variable_batch_size_comparison(X)
plt.show()