[MRG] Speedup

appveyor.yml

@@ -28,8 +28,6 @@ environment:
       PYTHON_ARCH: "64"
       MINICONDA: "C:\\Miniconda35-x64"
 
-
-
 install:
   # Miniconda is pre-installed in the worker build
   - "SET PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"

benchmarks/bench_20newsgroups.py

@@ -0,0 +1,92 @@
+# Benchmark polynomial classifiers on bag-of-words text classification
+# Inspired from: https://github.com/scikit-learn/scikit-learn/blob/master
+#                /benchmarks/bench_20newsgroups.py
+
+from time import time
+
+import numpy as np
+import scipy.sparse as sp
+
+from sklearn.base import clone
+from sklearn.metrics import accuracy_score, f1_score
+from sklearn.datasets import fetch_20newsgroups_vectorized
+
+from polylearn import (FactorizationMachineClassifier,
+                       PolynomialNetworkClassifier)
+
+
+estimators = {
+    'fm-2': FactorizationMachineClassifier(n_components=30,
+                                           fit_linear=False,
+                                           fit_lower=None,
+                                           degree=2,
+                                           random_state=0,
+                                           max_iter=10),
+
+    'polynet-2': PolynomialNetworkClassifier(n_components=15, degree=2,
+                                             fit_lower=None,
+                                             max_iter=10,
+                                             random_state=0)
+}
+
+estimators['fm-3'] = clone(estimators['fm-2']).set_params(degree=3)
+estimators['polynet-3'] = (clone(estimators['polynet-2'])
+                           .set_params(degree=3, n_components=10))
+
+if __name__ == '__main__':
+    data_train = fetch_20newsgroups_vectorized(subset="train")
+    data_test = fetch_20newsgroups_vectorized(subset="test")
+    X_train = sp.csc_matrix(data_train.data)
+    X_test = sp.csc_matrix(data_test.data)
+
+    y_train = data_train.target == 0  # atheism vs rest
+    y_test = data_test.target == 0
+
+    print("20 newsgroups")
+    print("=============")
+    print("X_train.shape = {0}".format(X_train.shape))
+    print("X_train.format = {0}".format(X_train.format))
+    print("X_train.dtype = {0}".format(X_train.dtype))
+    print("X_train density = {0}"
+          "".format(X_train.nnz / np.product(X_train.shape)))
+    print("y_train {0}".format(y_train.shape))
+    print("X_test {0}".format(X_test.shape))
+    print("X_test.format = {0}".format(X_test.format))
+    print("X_test.dtype = {0}".format(X_test.dtype))
+    print("y_test {0}".format(y_test.shape))
+    print()
+
+    print("Classifier Training")
+    print("===================")
+    f1, accuracy, train_time, test_time = {}, {}, {}, {}
+
+    for name, clf in sorted(estimators.items()):
+        print("Training %s ... " % name, end="")
+        t0 = time()
+        clf.fit(X_train, y_train)
+        train_time[name] = time() - t0
+        t0 = time()
+        y_pred = clf.predict(X_test)
+        test_time[name] = time() - t0
+        accuracy[name] = accuracy_score(y_test, y_pred)
+        f1[name] = f1_score(y_test, y_pred)
+        print("done")
+
+    print("Classification performance:")
+    print("===========================")
+    print()
+    print("%s %s %s %s %s" % ("Classifier".ljust(16),
+                              "train".rjust(10),
+                              "test".rjust(10),
+                              "f1".rjust(10),
+                              "accuracy".rjust(10)))
+    print("-" * (16 + 4 * 11))
+    for name in sorted(f1, key=f1.get):
+        print("%s %s %s %s %s" % (
+            name.ljust(16),
+            ("%.4fs" % train_time[name]).rjust(10),
+            ("%.4fs" % test_time[name]).rjust(10),
+            ("%.4f" % f1[name]).rjust(10),
+            ("%.4f" % accuracy[name]).rjust(10)))
+
+    print()