Logistic Regression ExampleΒΆ
Comparison of scaling.
from dask_ml.datasets import make_classification
import pandas as pd
from timeit import default_timer as tic
import sklearn.linear_model
import dask_ml.linear_model
import seaborn as sns
Ns = [2500, 5000, 7500, 10000]
timings = []
for n in Ns:
X, y = make_classification(n_samples=n, n_features=1_000, random_state=n,
chunks=n // 20)
t1 = tic()
sklearn.linear_model.LogisticRegression().fit(X, y)
timings.append(('Scikit-Learn', n, tic() - t1))
t1 = tic()
dask_ml.linear_model.LogisticRegression().fit(X, y)
timings.append(('dask-ml', n, tic() - t1))
df = pd.DataFrame(timings, columns=['method', 'Number of Samples', 'Fit Time'])
sns.factorplot(x='Number of Samples', y='Fit Time', hue='method',
data=df, aspect=1.5)
Total running time of the script: ( 5 minutes 0.900 seconds)