diff options
| -rwxr-xr-x | evaluate.py | 52 |
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() |