Add random forest feature generation

1 files changed, 39 insertions, 13 deletions

diff --git a/evaluate.py b/evaluate.py
index 9b26920..9e3a613 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -4,38 +4,64 @@
 
 DATA_FILE = 'data.npz'
 CLUSTER_CNT = 256
+KMEANS = False
+
+if KMEANS:
+    N_ESTIMATORS = 10
+else:
+    N_ESTIMATORS = 1
 
 import numpy as np
 import matplotlib.pyplot as plt
+import scikitplot as skplt
 
 from sklearn.cluster import KMeans
 from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomTreesEmbedding
 
 data = np.load(DATA_FILE)
 
 train = data['desc_tr']
-train_part = data['desc_sel'].T[0:1000]
+test = data['desc_te']
+train_part = data['desc_sel'].T
+
+if (KMEANS):
+    print("Computing KMeans with", train_part.shape[0], "keywords")
+    kmeans = KMeans(n_clusters=CLUSTER_CNT, n_init=N_ESTIMATORS, random_state=0).fit(train_part)
+else:
+    trees = RandomTreesEmbedding(max_leaf_nodes=256, n_estimators=N_ESTIMATORS, random_state=0).fit(train_part)
 
-print("Computing KMeans with", train_part.shape[0], "keywords")
-kmeans = KMeans(n_clusters=CLUSTER_CNT, random_state=0).fit(train_part)
 
 print("Generating histograms")
-histogram = np.zeros((train.shape[0], train.shape[1],CLUSTER_CNT))
 
-for i in range(train.shape[0]):
-    for j in range(train.shape[1]):
-        histogram[i][j] = np.bincount(kmeans.predict(train[i][j].T),minlength=CLUSTER_CNT)
+def make_histogram(data):
+    histogram = np.zeros((data.shape[0], data.shape[1],CLUSTER_CNT*N_ESTIMATORS)) 
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            print(data[i][j].shape)
+            if (KMEANS):
+                histogram[i][j] = np.bincount(kmeans.predict(data[i][j].T),minlength=CLUSTER_CNT)
+            else:
+                leaves = trees.apply(data[i][j].T)
+                leaves = np.apply_along_axis(np.bincount, axis=0, arr=leaves, minlength=CLUSTER_CNT)
+                print(leaves.shape)
+                histogram[i][j] = leaves.reshape(CLUSTER_CNT*N_ESTIMATORS)
+    return histogram
+
+hist_train = make_histogram(train)
+hist_test = make_histogram(test)
 
-print("Keywords shape", histogram.shape, "\n")
+print("Keywords shape", hist_train.shape, "\n")
 print("Planting trees...")
 clf = RandomForestClassifier()
 clf.fit(
-        histogram.reshape((histogram.shape[0]*histogram.shape[1], histogram.shape[2])),
-        np.repeat(np.arange(histogram.shape[0]), histogram.shape[1]))
+        hist_train.reshape((hist_train.shape[0]*hist_train.shape[1], hist_train.shape[2])),
+        np.repeat(np.arange(hist_train.shape[0]), hist_train.shape[1]))
 
 print("Random forests created")
 
-print(clf.score(
-        histogram.reshape((histogram.shape[0]*histogram.shape[1], histogram.shape[2])),
-        np.repeat(np.arange(histogram.shape[0]), histogram.shape[1])))
+test_pred = clf.predict(hist_test.reshape((hist_test.shape[0]*hist_test.shape[1], hist_test.shape[2])))
+test_label = np.repeat(np.arange(hist_test.shape[0]), hist_test.shape[1])
 
+skplt.metrics.plot_confusion_matrix(test_pred, test_label, normalize=True)
+plt.show()