Note
Click here to download the full example code
Benchmark of PolynomialFeatures + partialfit of SGDClassifier (standalone)¶
This benchmark looks into a new implementation of PolynomialFeatures proposed in PR13290. It tests the following configurations:
SGD: sklearn.linear_model.SGDClassifier only
SGD-SKL: sklearn.preprocessing.PolynomialFeatures from scikit-learn (no matter what it is)
SGD-FAST: new implementation copy-pasted in the benchmark source file
SGD-SLOW: implementation of 0.20.2 copy-pasted in the benchmark source file
This script is standalone and does not require pymlbenchmark as opposed to Benchmark of PolynomialFeatures + partialfit of SGDClassifier which reuse functions implemented in pymlbenchmark.
from time import perf_counter as time
import numpy
import numpy as np
from numpy.random import rand
import matplotlib.pyplot as plt
import pandas
import sklearn
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import SGDClassifier
try:
from sklearn.utils._testing import ignore_warnings
except ImportError:
from sklearn.utils.testing import ignore_warnings
from mlinsights.mlmodel import ExtendedFeatures
Implementations to benchmark¶
def fcts_model(X, y):
model1 = SGDClassifier()
model2 = make_pipeline(PolynomialFeatures(), SGDClassifier())
model3 = make_pipeline(ExtendedFeatures(kind='poly'), SGDClassifier())
model4 = make_pipeline(ExtendedFeatures(kind='poly-slow'), SGDClassifier())
model1.fit(PolynomialFeatures().fit_transform(X), y)
model2.fit(X, y)
model3.fit(X, y)
model4.fit(X, y)
def partial_fit_model1(X, y, model=model1):
return model.partial_fit(X, y)
def partial_fit_model2(X, y, model=model2):
X2 = model.steps[0][1].transform(X)
return model.steps[1][1].partial_fit(X2, y)
def partial_fit_model3(X, y, model=model3):
X2 = model.steps[0][1].transform(X)
return model.steps[1][1].partial_fit(X2, y)
def partial_fit_model4(X, y, model=model4):
X2 = model.steps[0][1].transform(X)
return model.steps[1][1].partial_fit(X2, y)
return (partial_fit_model1, partial_fit_model2,
partial_fit_model3, partial_fit_model4)
Benchmarks¶
def build_x_y(ntrain, nfeat):
X_train = np.empty((ntrain, nfeat))
X_train[:, :] = rand(ntrain, nfeat)[:, :]
X_trainsum = X_train.sum(axis=1)
eps = rand(ntrain) - 0.5
X_trainsum_ = X_trainsum + eps
y_train = (X_trainsum_ >= X_trainsum).ravel().astype(int)
return X_train, y_train
@ignore_warnings(category=(FutureWarning, DeprecationWarning))
def bench(n_obs, n_features, repeat=1000, verbose=False):
res = []
for n in n_obs:
for nfeat in n_features:
X_train, y_train = build_x_y(1000, nfeat)
obs = dict(n_obs=n, nfeat=nfeat)
fct1, fct2, fct3, fct4 = fcts_model(X_train, y_train)
# creates different inputs to avoid caching in any ways
Xs = []
Xpolys = []
for r in range(repeat):
X, y = build_x_y(n, nfeat)
Xs.append((X, y))
Xpolys.append((PolynomialFeatures().fit_transform(X), y))
# measure fct1
r = len(Xs)
st = time()
for X, y in Xpolys:
fct1(X, y)
end = time()
obs["time_sgd"] = (end - st) / r
res.append(obs)
# measures fct2
st = time()
for X, y in Xs:
fct2(X, y)
end = time()
obs["time_pipe_skl"] = (end - st) / r
res.append(obs)
# measures fct3
st = time()
for X, y in Xs:
fct3(X, y)
end = time()
obs["time_pipe_fast"] = (end - st) / r
res.append(obs)
# measures fct4
st = time()
for X, y in Xs:
fct4(X, y)
end = time()
obs["time_pipe_slow"] = (end - st) / r
res.append(obs)
if verbose and (len(res) % 1 == 0 or n >= 10000):
print("bench", len(res), ":", obs)
return res
Plots¶
def plot_results(df, verbose=False):
nrows = max(len(set(df.nfeat)), 2)
ncols = max(1, 2)
fig, ax = plt.subplots(nrows, ncols,
figsize=(nrows * 4, ncols * 4))
colors = "gbry"
row = 0
for nfeat in sorted(set(df.nfeat)):
pos = 0
for _ in range(1):
a = ax[row, pos]
if row == ax.shape[0] - 1:
a.set_xlabel("N observations", fontsize='x-small')
if pos == 0:
a.set_ylabel("Time (s) nfeat={}".format(nfeat),
fontsize='x-small')
subset = df[df.nfeat == nfeat]
if subset.shape[0] == 0:
continue
subset = subset.sort_values("n_obs")
if verbose:
print(subset)
label = "SGD"
subset.plot(x="n_obs", y="time_sgd", label=label, ax=a,
logx=True, logy=True, c=colors[0], style='--')
label = "SGD-SKL"
subset.plot(x="n_obs", y="time_pipe_skl", label=label, ax=a,
logx=True, logy=True, c=colors[1], style='--')
label = "SGD-FAST"
subset.plot(x="n_obs", y="time_pipe_fast", label=label, ax=a,
logx=True, logy=True, c=colors[2])
label = "SGD-SLOW"
subset.plot(x="n_obs", y="time_pipe_slow", label=label, ax=a,
logx=True, logy=True, c=colors[3])
a.legend(loc=0, fontsize='x-small')
if row == 0:
a.set_title("--", fontsize='x-small')
pos += 1
row += 1
plt.suptitle("Benchmark for Polynomial with SGDClassifier", fontsize=16)
Final function for the benchmark¶
def run_bench(repeat=100, verbose=False):
n_obs = [10, 100, 1000]
n_features = [5, 10, 50]
with sklearn.config_context(assume_finite=True):
start = time()
results = bench(n_obs, n_features, repeat=repeat, verbose=verbose)
end = time()
results_df = pandas.DataFrame(results)
print("Total time = %0.3f sec\n" % (end - start))
# plot the results
plot_results(results_df, verbose=verbose)
return results_df
Run the benchmark¶
print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
df = run_bench(verbose=True)
print(df)
plt.show()
Out:
numpy: 1.22.4
scikit-learn: 1.1.1
bench 4 : {'n_obs': 10, 'nfeat': 5, 'time_sgd': 0.0007252640900333063, 'time_pipe_skl': 0.001093377219985996, 'time_pipe_fast': 0.0009373169100217638, 'time_pipe_slow': 0.0017718485799923655}
bench 8 : {'n_obs': 10, 'nfeat': 10, 'time_sgd': 0.0007297577400095179, 'time_pipe_skl': 0.00122914702998969, 'time_pipe_fast': 0.0010638399200252025, 'time_pipe_slow': 0.00372598060999735}
bench 12 : {'n_obs': 10, 'nfeat': 50, 'time_sgd': 0.0008929046699995524, 'time_pipe_skl': 0.0025937436800086287, 'time_pipe_fast': 0.0024046017100044993, 'time_pipe_slow': 0.05859663781997369}
bench 16 : {'n_obs': 100, 'nfeat': 5, 'time_sgd': 0.0007852243700108375, 'time_pipe_skl': 0.0012213449099726858, 'time_pipe_fast': 0.0010509258499951102, 'time_pipe_slow': 0.0018742052400193643}
bench 20 : {'n_obs': 100, 'nfeat': 10, 'time_sgd': 0.0008440911700017751, 'time_pipe_skl': 0.0015123411300010047, 'time_pipe_fast': 0.001332182180012751, 'time_pipe_slow': 0.003990463199988881}
bench 24 : {'n_obs': 100, 'nfeat': 50, 'time_sgd': 0.001950420950015541, 'time_pipe_skl': 0.004869367709979997, 'time_pipe_fast': 0.004639451559996815, 'time_pipe_slow': 0.0649360409899964}
bench 28 : {'n_obs': 1000, 'nfeat': 5, 'time_sgd': 0.0014554550100001507, 'time_pipe_skl': 0.0022745840499919724, 'time_pipe_fast': 0.002048752049995528, 'time_pipe_slow': 0.002798120689985808}
bench 32 : {'n_obs': 1000, 'nfeat': 10, 'time_sgd': 0.001964197810011683, 'time_pipe_skl': 0.0036576432999936514, 'time_pipe_fast': 0.003444180079968646, 'time_pipe_slow': 0.006088137340011599}
bench 36 : {'n_obs': 1000, 'nfeat': 50, 'time_sgd': 0.010638297920013428, 'time_pipe_skl': 0.03148720231998595, 'time_pipe_fast': 0.0351094048899904, 'time_pipe_slow': 0.09803675962000852}
Total time = 47.249 sec
n_obs nfeat time_sgd time_pipe_skl time_pipe_fast time_pipe_slow
0 10 5 0.000725 0.001093 0.000937 0.001772
1 10 5 0.000725 0.001093 0.000937 0.001772
2 10 5 0.000725 0.001093 0.000937 0.001772
3 10 5 0.000725 0.001093 0.000937 0.001772
12 100 5 0.000785 0.001221 0.001051 0.001874
13 100 5 0.000785 0.001221 0.001051 0.001874
14 100 5 0.000785 0.001221 0.001051 0.001874
15 100 5 0.000785 0.001221 0.001051 0.001874
24 1000 5 0.001455 0.002275 0.002049 0.002798
25 1000 5 0.001455 0.002275 0.002049 0.002798
26 1000 5 0.001455 0.002275 0.002049 0.002798
27 1000 5 0.001455 0.002275 0.002049 0.002798
n_obs nfeat time_sgd time_pipe_skl time_pipe_fast time_pipe_slow
4 10 10 0.000730 0.001229 0.001064 0.003726
5 10 10 0.000730 0.001229 0.001064 0.003726
6 10 10 0.000730 0.001229 0.001064 0.003726
7 10 10 0.000730 0.001229 0.001064 0.003726
16 100 10 0.000844 0.001512 0.001332 0.003990
17 100 10 0.000844 0.001512 0.001332 0.003990
18 100 10 0.000844 0.001512 0.001332 0.003990
19 100 10 0.000844 0.001512 0.001332 0.003990
28 1000 10 0.001964 0.003658 0.003444 0.006088
29 1000 10 0.001964 0.003658 0.003444 0.006088
30 1000 10 0.001964 0.003658 0.003444 0.006088
31 1000 10 0.001964 0.003658 0.003444 0.006088
n_obs nfeat time_sgd time_pipe_skl time_pipe_fast time_pipe_slow
8 10 50 0.000893 0.002594 0.002405 0.058597
9 10 50 0.000893 0.002594 0.002405 0.058597
10 10 50 0.000893 0.002594 0.002405 0.058597
11 10 50 0.000893 0.002594 0.002405 0.058597
20 100 50 0.001950 0.004869 0.004639 0.064936
21 100 50 0.001950 0.004869 0.004639 0.064936
22 100 50 0.001950 0.004869 0.004639 0.064936
23 100 50 0.001950 0.004869 0.004639 0.064936
32 1000 50 0.010638 0.031487 0.035109 0.098037
33 1000 50 0.010638 0.031487 0.035109 0.098037
34 1000 50 0.010638 0.031487 0.035109 0.098037
35 1000 50 0.010638 0.031487 0.035109 0.098037
n_obs nfeat time_sgd time_pipe_skl time_pipe_fast time_pipe_slow
0 10 5 0.000725 0.001093 0.000937 0.001772
1 10 5 0.000725 0.001093 0.000937 0.001772
2 10 5 0.000725 0.001093 0.000937 0.001772
3 10 5 0.000725 0.001093 0.000937 0.001772
4 10 10 0.000730 0.001229 0.001064 0.003726
5 10 10 0.000730 0.001229 0.001064 0.003726
6 10 10 0.000730 0.001229 0.001064 0.003726
7 10 10 0.000730 0.001229 0.001064 0.003726
8 10 50 0.000893 0.002594 0.002405 0.058597
9 10 50 0.000893 0.002594 0.002405 0.058597
10 10 50 0.000893 0.002594 0.002405 0.058597
11 10 50 0.000893 0.002594 0.002405 0.058597
12 100 5 0.000785 0.001221 0.001051 0.001874
13 100 5 0.000785 0.001221 0.001051 0.001874
14 100 5 0.000785 0.001221 0.001051 0.001874
15 100 5 0.000785 0.001221 0.001051 0.001874
16 100 10 0.000844 0.001512 0.001332 0.003990
17 100 10 0.000844 0.001512 0.001332 0.003990
18 100 10 0.000844 0.001512 0.001332 0.003990
19 100 10 0.000844 0.001512 0.001332 0.003990
20 100 50 0.001950 0.004869 0.004639 0.064936
21 100 50 0.001950 0.004869 0.004639 0.064936
22 100 50 0.001950 0.004869 0.004639 0.064936
23 100 50 0.001950 0.004869 0.004639 0.064936
24 1000 5 0.001455 0.002275 0.002049 0.002798
25 1000 5 0.001455 0.002275 0.002049 0.002798
26 1000 5 0.001455 0.002275 0.002049 0.002798
27 1000 5 0.001455 0.002275 0.002049 0.002798
28 1000 10 0.001964 0.003658 0.003444 0.006088
29 1000 10 0.001964 0.003658 0.003444 0.006088
30 1000 10 0.001964 0.003658 0.003444 0.006088
31 1000 10 0.001964 0.003658 0.003444 0.006088
32 1000 50 0.010638 0.031487 0.035109 0.098037
33 1000 50 0.010638 0.031487 0.035109 0.098037
34 1000 50 0.010638 0.031487 0.035109 0.098037
35 1000 50 0.010638 0.031487 0.035109 0.098037
Total running time of the script: ( 0 minutes 50.693 seconds)