MOA E2

E_MOA_2.py

@@ -0,0 +1,78 @@
+"""
+E2 - classification for MOA streams 
+"""
+
+import numpy as np
+from sklearn import clone
+from sklearn.model_selection import RepeatedStratifiedKFold
+from sklearn.neural_network import MLPClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from tqdm import tqdm
+from sklearn.metrics import balanced_accuracy_score
+import os
+
+np.random.seed(1233)
+
+measures = ["clustering",
+        "complexity",
+        "concept",
+        "general",
+        "info-theory",
+        "itemset",
+        "landmarking",
+        "model-based",
+        "statistical"
+        ]
+
+streams = os.listdir('data/moa')
+streams.remove('.DS_Store')
+print(streams)
+
+base_clfs = [
+    GaussianNB(),
+    KNeighborsClassifier(),
+    SVC(random_state=11313),
+    DecisionTreeClassifier(random_state=11313),
+    MLPClassifier(random_state=11313)
+]
+
+origial_datasets=len(streams)
+
+n_splits=2
+n_repeats=5
+
+clf_res = np.zeros((len(measures), origial_datasets, n_splits*n_repeats, len(base_clfs)))
+pbar = tqdm(total=len(measures)*origial_datasets*n_splits*n_repeats*len(base_clfs))
+
+for f_id in range(len(streams)):
+    for m_id, m in enumerate(measures):
+        res = np.load('results/moa_%s_%i.npy' % (m, f_id))
+
+        p = np.random.permutation(res.shape[0])
+        res = res[p]
+
+        # print(res_rep.shape) # chunks, measures + label
+        X = res[:,:-1]
+        y = res[:,-1]
+
+        X[np.isnan(X)]=1
+        X[np.isinf(X)]=1
+
+        rskf = RepeatedStratifiedKFold(n_splits=n_splits, n_repeats=n_repeats, random_state=3242)
+        for fold, (train, test) in enumerate(rskf.split(X, y)):
+
+            for base_id, base_c in enumerate(base_clfs):
+                clf = clone(base_c)
+
+                pred = clf.fit(X[train], y[train]).predict(X[test])
+                acc = balanced_accuracy_score(y[test], pred)
+
+                clf_res[m_id, f_id, fold, base_id] = acc
+                pbar.update(1)
+
+        print(m, np.mean(clf_res[m_id, f_id], axis=0))
+
+np.save('results/moa_clf.npy', clf_res)

E_MOA_2P.py

@@ -0,0 +1,57 @@
+"""
+Plot E2 - Visualize classification results for MOA streams
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+np.random.seed(1233)
+
+measures = ["clustering",
+    "complexity",
+    "concept",
+    "general",
+    "info-theory",
+    "itemset",
+    "landmarking",
+    "model-based",
+    "statistical"
+    ]
+
+streams = os.listdir('data/moa')
+streams.remove('.DS_Store')
+print(streams)
+
+base_clfs = ['GNB', 'KNN', 'SVM', 'DT', 'MLP']
+
+n_drift_types=3
+stream_reps=5
+
+res = np.load('results/moa_clf.npy') # measures, datasets, reps, folds, clfs
+
+res_mean = np.mean(res, axis=2)
+print(res_mean.shape)
+
+fig, ax = plt.subplots(4, 3, figsize=(10,20), sharex=True, sharey=True)
+ax=ax.ravel()
+plt.suptitle('MOA', fontsize=18, y=0.99)
+
+for dataset_id, dataset in enumerate(streams):
+
+    axx = ax[dataset_id]
+
+    r = res_mean[:,dataset_id]
+    axx.imshow(r, vmin=0.4, vmax=1., cmap='Blues')
+
+    for _a, __a in enumerate(measures):
+        for _b, __b in enumerate(base_clfs):
+            axx.text(_b, _a, "%.3f" % (r[_a, _b]) , va='center', ha='center', c='black' if r[_a, _b]<0.75 else 'white', fontsize=11)
+
+    axx.set_title(dataset.split('.')[0])
+    axx.set_xticks(np.arange(len(base_clfs)),base_clfs)
+    axx.set_yticks(np.arange(len(measures)),measures)
+
+
+plt.tight_layout()
+plt.savefig('foo.png')
+plt.savefig('figures/fig_clf/MOA.png')