示例#1
0
 def stage5():
     # Obtain k-NN scores & pscores, predict, and calculate F1!
     n_iterations = 20
     k = 70
     w1, w2, w3, w4 = 3.4, 0.6, 0.8, 0.2
     alpha = 0.9
     cat_pns = evaluation.CategoryPNCounter()
     for d_i, labels_i in islice(izip(v_X, v_Y), 0, n_iterations):
         scores, pscores = similarity.cossim(d_i, t_X, k, t_Y,
                                             parents_index, children_index)
         ranks = similarity.optimized_ranks(scores, pscores, label_counter,
                                            w1, w2, w3, w4)
         predicted_labels = similarity.predict(ranks, alpha)
         cat_pns.fill_pns(predicted_labels, labels_i)
     cat_pns.calculate_cat_pr()
     MaF = cat_pns.calculate_MaF()
     print "MaF:", MaF
示例#2
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	def stage5():
		# Obtain k-NN scores & pscores, predict, and calculate F1!
		n_iterations = 20
		k = 70
		w1, w2, w3, w4 = 3.4, 0.6, 0.8, 0.2
		alpha = 0.9
		cat_pns = evaluation.CategoryPNCounter()
		for d_i, labels_i in islice(izip(v_X, v_Y), 0, n_iterations):
			scores, pscores = similarity.cossim(d_i, t_X, k, t_Y, parents_index,
				children_index)
			ranks = similarity.optimized_ranks(scores, pscores, label_counter,
				w1, w2, w3, w4)
			predicted_labels = similarity.predict(ranks, alpha)
			cat_pns.fill_pns(predicted_labels, labels_i)
		cat_pns.calculate_cat_pr()
		MaF = cat_pns.calculate_MaF()
		print "MaF:", MaF
示例#3
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def onego_main():
    # Test on toyset.
    preproc.subset("../raw_data/train.csv", "../data/train.csv", 1, 200000)

    # Load toyset .csv -> X & Y
    X, Y = preproc.extract_XY("../data/train.csv")

    # Prune corpora
    label_counter = pruning.LabelCounter(Y)
    word_counter = pruning.WordCounter(X)
    label_counter.prune(no_below=2, no_above=1.0, max_n=None)
    word_counter.prune(no_below=2, no_above=0.4, max_n=None)  # assume balanced
    pruning.prune_corpora(X, Y, label_counter, word_counter)
    del word_counter  # free up memory

    # Transform X to tf-idf
    bin_word_counter = pruning.WordCounter(X, binary=True)
    similarity.transform_tfidf(X, bin_word_counter)
    del bin_word_counter  # free up memory

    # Load hierarchy (parents & children indices)
    parents_index = preproc.extract_parents(Y, "../raw_data/hierarchy.txt")
    children_index = preproc.inverse_index(parents_index)

    # CV-split X & Y (using default params)
    v_X, v_Y, t_X, t_Y = cv.prop_sample_CV(X=X, Y=Y)
    del X, Y  # free up memory

    # Obtain k-NN scores & pscores, predict, and calculate F1!
    k = 70
    w1, w2, w3, w4 = 3.4, 0.6, 0.8, 0.2
    alpha = 0.9
    cat_pns = evaluation.CategoryPNCounter()
    for d_i, labels_i in izip(v_X, v_Y):
        scores, pscores = similarity.cossim(d_i, t_X, k, t_Y, parents_index,
                                            children_index)
        ranks = similarity.optimized_ranks(scores, pscores, label_counter, w1,
                                           w2, w3, w4)
        predicted_labels = similarity.predict(ranks, alpha)
        cat_pns.fill_pns(predicted_labels, labels_i)
    cat_pns.calculate_cat_pr()
    MaF = cat_pns.calculate_MaF()

    print "MaF:", MaF
示例#4
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def onego_main():
	# Test on toyset.
	preproc.subset("../raw_data/train.csv", "../data/train.csv", 1, 200000)

	# Load toyset .csv -> X & Y
	X, Y = preproc.extract_XY("../data/train.csv")

	# Prune corpora
	label_counter = pruning.LabelCounter(Y)
	word_counter = pruning.WordCounter(X)
	label_counter.prune(no_below=2, no_above=1.0, max_n=None)
	word_counter.prune(no_below=2, no_above=0.4, max_n=None) # assume balanced
	pruning.prune_corpora(X, Y, label_counter, word_counter)
	del word_counter # free up memory

	# Transform X to tf-idf
	bin_word_counter = pruning.WordCounter(X, binary=True)
	similarity.transform_tfidf(X, bin_word_counter)
	del bin_word_counter # free up memory

	# Load hierarchy (parents & children indices)
	parents_index = preproc.extract_parents(Y, "../raw_data/hierarchy.txt")
	children_index = preproc.inverse_index(parents_index)

	# CV-split X & Y (using default params)
	v_X, v_Y, t_X, t_Y = cv.prop_sample_CV(X=X, Y=Y)
	del X, Y # free up memory

	# Obtain k-NN scores & pscores, predict, and calculate F1!
	k = 70
	w1, w2, w3, w4 = 3.4, 0.6, 0.8, 0.2
	alpha = 0.9
	cat_pns = evaluation.CategoryPNCounter()
	for d_i, labels_i in izip(v_X, v_Y):
		scores, pscores = similarity.cossim(d_i, t_X, k, t_Y, parents_index,
			children_index)
		ranks = similarity.optimized_ranks(scores, pscores, label_counter,
			w1, w2, w3, w4)
		predicted_labels = similarity.predict(ranks, alpha)
		cat_pns.fill_pns(predicted_labels, labels_i)
	cat_pns.calculate_cat_pr()
	MaF = cat_pns.calculate_MaF()

	print "MaF:", MaF