WIP isolate convergence warning in LogReg

.gitignore

@@ -14,6 +14,8 @@ celer.egg-info
 # build
 build
 
+# virtual environment
+venv
 
 # cache
 .pytest_cache

celer/homotopy.py

@@ -311,6 +311,7 @@ def celer_path(X, y, pb, eps=1e-3, n_alphas=100, alphas=None,
             n_iters[t] = len(sol[2])
 
     results = alphas, coefs, dual_gaps
+    # results = alphas, coefs, sol[2]  # for isolate_conv_warn
     if return_thetas:
         results += (thetas,)
     if return_n_iter:

celer/tests/conv_warning/inspect_conv_warn.py

@@ -0,0 +1,25 @@
+"""For debbuging purposes."""
+
+import numpy as np
+from numpy.linalg import norm
+from sklearn.utils.estimator_checks import check_estimator
+
+from celer.dropin_sklearn import LogisticRegression
+from celer.utils.testing import build_dataset
+
+
+np.random.seed(1409)
+X, y = build_dataset(
+    n_samples=30, n_features=60, sparse_X=True)
+y = np.sign(y)
+alpha_max = norm(X.T.dot(y), ord=np.inf) / 2
+C = 20. / alpha_max
+
+tol = 1e-4
+clf1 = LogisticRegression(C=C, tol=tol, verbose=0)
+
+generator = check_estimator(clf1, generate_only=True)
+generator = list(generator)
+
+for i, (estimator, check_estimator) in enumerate(generator[37:]):
+    check_estimator(estimator)

celer/tests/conv_warning/isolate_conv_warn.py

@@ -0,0 +1,66 @@
+import numpy as np
+from numpy.linalg import norm
+from sklearn.preprocessing import LabelEncoder
+import matplotlib.pyplot as plt
+
+from celer.dropin_sklearn import LogisticRegression
+from celer.tests.conv_warning.logs.dumped_data import DICT_DATA
+
+import pickle
+
+
+# to be excuted only once
+# before, uncomment results in homotopy.py
+def simulate_LogReg_mul_alphas():
+    check_props = DICT_DATA['check_fit_check_is_fitted']
+    enc = LabelEncoder()
+
+    X = check_props['X']
+    y = check_props['y']
+    y_ind = enc.fit_transform(y)
+
+    C = check_props['C']
+    alpha_max = norm(X.T.dot(y_ind), ord=np.inf)
+    C_max = 1 / alpha_max
+    tol = 1e-14
+
+    arr_C = np.linspace(C_max, C, num=5)[1:]  # skip first value
+
+    dict_gaps = {}
+    for current_C in arr_C:
+        clf = LogisticRegression(C=C, tol=tol, max_iter=100, verbose=0)
+        _, gaps = clf.path(
+            X, 2 * y_ind - 1, np.array([current_C]), solver="celer-pn")
+
+        current_alpha = 1 / current_C
+        plot_name = f'{current_alpha/alpha_max:.2e}'
+        dict_gaps[plot_name] = gaps
+    # save logs
+    filename = 'celer/tests/conv_warning/logs/gaps_for_mul_alphas.pkl'
+    with open(filename, 'wb') as f:
+        pickle.dump(dict_gaps, f)
+
+
+# # simulate
+# simulate_LogReg_mul_alphas()
+
+# load
+filename = 'celer/tests/conv_warning/logs/gaps_for_mul_alphas.pkl'
+with open(filename, 'rb') as f:
+    dict_gaps = pickle.load(f)
+
+# plot
+fig, ax = plt.subplots()
+
+for plot_name, gaps in dict_gaps.items():
+    ax.semilogy(gaps, label=plot_name, marker='.')
+
+# set layout
+plt.title("LogReg for different alpha")
+ax.set_xlabel("iterations")
+ax.set_ylabel("dual gap")
+
+plt.grid()
+plt.legend(title="Fraction of alpha_max")
+
+plt.show()

celer/tests/conv_warning/logs/dumped_data.py

@@ -0,0 +1,204 @@
+import numpy as np
+
+
+DICT_DATA = {}
+
+# check_fit_idempotent
+tol = 1e-4
+C = 5.3648013574915625
+X = np.array([[99.30543214,  99.85036546],
+              [98.82687659, 101.94362119],
+              [99.260437, 101.5430146],
+              [99.5444675, 100.01747916],
+              [99.49034782,  99.5619257],
+              [99.73199663, 100.8024564],
+              [100.3130677,  99.14590426],
+              [101.18802979, 100.31694261],
+              [98.96575716, 100.68159452],
+              [101.8831507,  98.65224094],
+              [99.59682305, 101.22244507],
+              [99.32566734, 100.03183056],
+              [100.04575852,  99.81281615],
+              [101.48825219, 101.89588918],
+              [100.15494743, 100.37816252],
+              [98.95144703,  98.57998206],
+              [101.86755799,  99.02272212],
+              [100.72909056, 100.12898291],
+              [99.65208785, 100.15634897],
+              [99.97181777, 100.42833187],
+              [99.36567791,  99.63725883],
+              [99.89678115, 100.4105985],
+              [100.46566244,  98.46375631],
+              [99.25524518,  99.17356146],
+              [100.92085882, 100.31872765],
+              [100.12691209, 100.40198936],
+              [101.53277921, 101.46935877],
+              [101.12663592,  98.92006849],
+              [99.61267318,  99.69769725],
+              [100.17742614,  99.59821906],
+              [98.74720464, 100.77749036],
+              [99.09270164, 100.0519454],
+              [99.13877431, 101.91006495],
+              [97.44701018, 100.6536186],
+              [99.10453344, 100.3869025],
+              [99.12920285,  99.42115034],
+              [99.90154748,  99.33652171],
+              [98.99978465,  98.4552289],
+              [98.85253135,  99.56217996],
+              [100.20827498, 100.97663904],
+              [99.96071718,  98.8319065],
+              [101.49407907,  99.79484174],
+              [99.50196755, 101.92953205],
+              [100.40234164,  99.31518991],
+              [98.29372981, 101.9507754],
+              [100.1666735, 100.63503144],
+              [99.32753955,  99.64044684],
+              [100.14404357, 101.45427351],
+              [99.3563816,  97.77659685],
+              [98.50874241, 100.4393917],
+              [99.36415392, 100.67643329],
+              [102.38314477, 100.94447949],
+              [100.44386323, 100.33367433],
+              [100.94942081, 100.08755124],
+              [100.85683061,  99.34897441],
+              [99.58638102,  99.25254519],
+              [99.08717777, 101.11701629],
+              [98.729515, 100.96939671],
+              [99.48919486,  98.81936782],
+              [100.57659082,  99.79170124],
+              [100.06651722, 100.3024719],
+              [98.36980165, 100.46278226],
+              [100.37642553,  98.90059921],
+              [98.83485016, 100.90082649],
+              [98.70714309, 100.26705087],
+              [100.76103773, 100.12167502],
+              [100.39600671,  98.90693849],
+              [101.13940068,  98.76517418],
+              [98.89561666, 100.05216508],
+              [98.38610215,  99.78725972],
+              [99.68844747, 100.05616534],
+              [101.76405235, 100.40015721],
+              [100.97873798, 102.2408932],
+              [100.94725197,  99.84498991],
+              [102.26975462,  98.54563433],
+              [100.95008842,  99.84864279],
+              [101.17877957,  99.82007516],
+              [100.52327666,  99.82845367],
+              [100.77179055, 100.82350415],
+              [98.68409259,  99.5384154]])
+y = np.array([1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,
+              1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0,
+              1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0,
+              0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0])
+
+DICT_DATA['check_fit_idempotent'] = {'C': C, 'X': X, 'y': y, 'tol': tol}
+
+# check_fit_check_is_fitted
+C = 5.3648013574915625
+X = np.array([[100.49671415,  99.8617357],
+              [100.64768854, 101.52302986],
+              [99.76584663,  99.76586304],
+              [101.57921282, 100.76743473],
+              [99.53052561, 100.54256004],
+              [99.53658231,  99.53427025],
+              [100.24196227,  98.08671976],
+              [98.27508217,  99.43771247],
+              [98.98716888, 100.31424733],
+              [99.09197592,  98.5876963],
+              [101.46564877,  99.7742237],
+              [100.0675282,  98.57525181],
+              [99.45561728, 100.11092259],
+              [98.84900642, 100.37569802],
+              [99.39936131,  99.70830625],
+              [99.39829339, 101.85227818],
+              [99.98650278,  98.94228907],
+              [100.82254491,  98.77915635],
+              [100.2088636,  98.04032988],
+              [98.67181395, 100.19686124],
+              [100.73846658, 100.17136828],
+              [99.88435172,  99.6988963],
+              [98.52147801,  99.28015579],
+              [99.53936123, 101.05712223],
+              [100.34361829,  98.23695984],
+              [100.32408397,  99.61491772],
+              [99.323078, 100.61167629],
+              [101.03099952, 100.93128012],
+              [99.16078248,  99.69078762],
+              [100.33126343, 100.97554513],
+              [99.52082576,  99.81434102],
+              [98.89366503,  98.80379338],
+              [100.81252582, 101.35624003],
+              [99.92798988, 101.0035329],
+              [100.36163603,  99.35488025],
+              [100.36139561, 101.53803657],
+              [99.96417396, 101.56464366],
+              [97.3802549, 100.8219025],
+              [100.08704707,  99.70099265],
+              [100.09176078,  98.01243109],
+              [99.78032811, 100.35711257],
+              [101.47789404,  99.48172978],
+              [99.1915064,  99.49824296],
+              [100.91540212, 100.32875111],
+              [99.4702398, 100.51326743],
+              [100.09707755, 100.96864499],
+              [99.29794691,  99.67233785],
+              [99.60789185,  98.53648505],
+              [100.29612028, 100.26105527],
+              [100.00511346,  99.76541287],
+              [98.58462926,  99.57935468],
+              [99.65728548,  99.19772273],
+              [99.83871429, 100.40405086],
+              [101.8861859, 100.17457781],
+              [100.25755039,  99.92555408],
+              [98.08122878,  99.97348612],
+              [100.06023021, 102.46324211],
+              [99.80763904, 100.30154734],
+              [99.96528823,  98.83132196],
+              [101.14282281, 100.75193303],
+              [100.79103195,  99.09061255],
+              [101.40279431,  98.59814894],
+              [100.58685709, 102.19045563],
+              [99.00946367,  99.43370227],
+              [100.09965137,  99.49652435],
+              [98.44933657, 100.06856297],
+              [98.93769629, 100.47359243],
+              [99.08057577, 101.54993441],
+              [99.21674671,  99.67793848],
+              [100.81351722,  98.76913568],
+              [100.22745993, 101.30714275],
+              [98.39251677, 100.18463386],
+              [100.25988279, 100.78182287],
+              [98.76304929,  98.67954339],
+              [100.52194157, 100.29698467],
+              [100.25049285, 100.34644821],
+              [99.31997528, 100.2322537],
+              [100.29307247,  99.28564858],
+              [101.86577451, 100.47383292],
+              [98.8086965, 100.65655361],
+              [99.02531833, 100.7870846],
+              [101.15859558,  99.17931768],
+              [100.96337613, 100.41278093],
+              [100.82206016, 101.89679298],
+              [99.75461188,  99.24626384],
+              [99.11048557,  99.18418972],
+              [99.92289829, 100.34115197],
+              [100.2766908, 100.82718325],
+              [100.01300189, 101.45353408],
+              [99.73534317, 102.72016917],
+              [100.62566735,  99.14284244],
+              [98.9291075, 100.48247242],
+              [99.77653721, 100.71400049],
+              [100.47323762,  99.92717109],
+              [99.15320628,  98.48515278],
+              [99.55348505, 100.85639879],
+              [100.21409374,  98.75426122],
+              [100.17318093, 100.38531738],
+              [99.11614256, 100.15372511],
+              [100.05820872,  98.8570297]])
+y = np.array([1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
+              0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,
+              0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1,
+              1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0,
+              0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1])
+
+DICT_DATA['check_fit_check_is_fitted'] = {'C': C, 'X': X, 'y': y, 'tol': tol}