def solution_similarity_stats(dataset='air/solutions_preprocessed'):
    """
    Plots histogram of solution-solution similarity distribution of a dataset.
    """
    print '> Reading data..', dataset
    corpus_path = '../data/'+dataset
    (documents, labels) = data.read_files(corpus_path)

    print '> Creating vector representations..'
    vectors = freq_representation.text_to_vector(documents, freq_representation.FrequencyMetrics.TF_IDF)

    print '> Calculating similarities..'
    distances = scipy.spatial.distance.cdist(vectors.T, vectors.T, 'cosine')
    diag = numpy.diag([2.0]*len(distances),0) # move similarities of "self" to -1
    distances = distances + diag
    similarities = 1.0 - distances
    similarities = similarities.ravel()
    similarities = [s for s in similarities if s >= 0]
    print plotter.histogram(similarities,'similarity','# matches','',bins=150)
    print
    print max(similarities)
    print min(similarities)
    print float(sum(similarities))/len(similarities)
    num = len([sim for sim in similarities if sim < 0.23])
    print 'fraction sims < .23:', float(num)/len(similarities)
Exemple #2
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def retrieval_demo():
    """Function intended to illustrate retrieval in the experimental framework.

    Intended as a basis for new experiments for those not intimately
    familiar with the code.
    """
    print 'Evaluation type: Retrieval'
    print 'Graph type:      Dependency'
    print 'Centrality:      PageRank'
    print
    print '> Reading data..'
    desc_path = '../data/air/problem_descriptions_dependencies'
    sol_path = '../data/air/solutions_preprocessed'
    problems, _ = data.read_files(desc_path)
    solutions, _ = data.read_files(sol_path)

    print '> Creating solution representations..'
    metric = freq_representation.FrequencyMetrics.TF_IDF
    sol_vectors = freq_representation.text_to_vector(solutions, metric)

    print '> Creating problem description representations..'
    dicts = []
    for i, doc in enumerate(problems):
        print '   ',str(i)+'/'+str(len(problems))
        g = graph_representation.construct_dependency_network(doc)
        d = graph_representation.graph_to_dict(g, graph.GraphMetrics.PAGERANK)
        dicts.append(d)
    desc_vectors = graph_representation.dicts_to_vectors(dicts)

    print '> Evaluating..'
    score = evaluation.evaluate_retrieval(desc_vectors, sol_vectors)
    print '    score:', score
    print
def test_retrieval(orders=[1,2,3],order_weights=[1.0,1.53,1.51]):
    """
    Test retrieval using different combinations of higher orders and weightings of these.

    The list *orders* define which higher order relations to include.
    The relative importance of the orders are defined by *order_weights*.
    """
    print '> Reading cases..'
    descriptions_path = '../data/air/problem_descriptions_preprocessed'
    description_texts, labels = data.read_files(descriptions_path)
    filenames = data.get_file_names(descriptions_path)

    solutions_path = '../data/air/solutions_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)

    print '> Creating representations..'
    rep = []
    for i, text in enumerate(description_texts):
        print '    '+str(i)+"/"+str(len(description_texts))
        g = graph_representation.construct_cooccurrence_network(text, orders=orders, order_weights=order_weights, doc_id='output/higher_order/air/'+labels[i]+'/'+filenames[i])
        d = graph_representation.graph_to_dict(g, graph.GraphMetrics.WEIGHTED_DEGREE)
        rep.append(d)
    rep = graph_representation.dicts_to_vectors(rep)

    print '> Evaluating..'
    score = evaluation.evaluate_retrieval(rep, solution_vectors)
    print 'orders:', orders
    print 'score:', score
    fname = 'output/higher_order/results/retr'
    with open(fname, 'a+') as f:
        s = reduce(lambda x,y:str(x)+str(y), orders)
        f.write(str(s)+' '+str(score)+'\n')
    return score
def retrieval_comparison_graph(dataset='air', graph_type='co-occurrence', use_icc=False):
    """
    Experiment used for comparative evaluation of different network
    representations on retrieval.

    graph_type = 'co-occurrence' | 'dependency'

    `icc` determines whether to use _inverse corpus centrality_ in the vector representations.
    """
    def make_dicts(docs, icc=None):
        rep = []
        for i, doc in enumerate(docs):
            if i%100==0: print '    graph',str(i)+'/'+str(len(docs))
            g = gfuns[graph_type](doc)
            d = graph_representation.graph_to_dict(g, metrics[graph_type], icc)
            rep.append(d)
        return rep

    postfix = {'co-occurrence':'_text', 'dependency':'_dependencies'}
    gfuns = {'co-occurrence':graph_representation.construct_cooccurrence_network,
                'dependency':graph_representation.construct_dependency_network}
    metrics = {'co-occurrence':graph.GraphMetrics.WEIGHTED_DEGREE,
                'dependency':graph.GraphMetrics.EIGENVECTOR}

    print '--', graph_type
    print '> Reading data..', dataset
    path = '../data/'+dataset+'/problem_descriptions'+postfix[graph_type]
    docs, labels = data.read_files(path)

    print '> Creating solution representations..'
    solutions_path = '../data/'+dataset+'/solutions_preprocessed'
    solutions_texts, labels = data.read_files(solutions_path)
    solutions_rep = freq_representation.text_to_vector(solutions_texts, freq_representation.FrequencyMetrics.TF_IDF)

    icc = None
    if use_icc:
        print '> Calculating ICC..'
        m = metrics[graph_type].split()[0]
        print graph_type
        if graph_type == 'co-occurrence':
            p = 'output/centralities/co-occurrence/'+dataset+'/problem_descriptions/window/'+m+'.cent'
        elif graph_type == 'dependency':
            p = 'output/centralities/dependency/'+dataset+'/problem_descriptions/'+m+'.cent'
        print '    fetching', p
        icc = data.pickle_from_file(p)
        print '    icc:', type(icc)

    print '> Creating problem description representations..'
    dicts = make_dicts(docs, icc)
    descriptions_rep = graph_representation.dicts_to_vectors(dicts)#, remove_stop_words=True)

    print '> Evaluating..'
    results = evaluation.evaluate_retrieval(descriptions_rep, solutions_rep)
    print results
    s = 'retrieval comparison '
    if use_icc: s += 'USING TC-ICC'
    s += '\nrepresentation: '+graph_type+'\nresult: '+str(results)+'\n\n\n'
    data.write_to_file(s, 'output/comparison/retrieval')
    return results
def edge_direction_evaluation(direction):
    """
    Evaluate impact of using different edge directions on dependency networks.

    Values for *direction*: ``forward``, ``backward``, and ``undirected``.
    """
    results = {'_edge-direction':direction}

    print '------ CLASSIFICATION EVALUATION --------'

    print '> Reading cases..'
    descriptions_path = '../data/tasa/TASA900_dependencies'
    texts, labels = data.read_files(descriptions_path)

    print '> Creating representations..'
    rep = []
    for i, text in enumerate(texts):
        if i%100==0: print '   ',str(i)+'/'+str(len(texts))
        g = graph_representation.construct_dependency_network(text, direction=direction)
        metric  = graph.GraphMetrics.CLOSENESS
        d = graph_representation.graph_to_dict(g, metric)
        rep.append(d)
        g = None # just to make sure..
    rep = graph_representation.dicts_to_vectors(rep)

    print '> Evaluating..'
    score = evaluation.evaluate_classification(rep, labels)
    print '   score:', score
    results['classification'] = score

    print '------ RETRIEVAL EVALUATION --------'
    print '> Reading cases..'
    descriptions_path = '../data/air/problem_descriptions_dependencies'
    description_texts, labels = data.read_files(descriptions_path)
    solutions_path = '../data/air/solutions_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)

    print '> Creating representations..'
    rep = []
    for i, text in enumerate(description_texts):
        if i%100==0: print '   ',str(i)+'/'+str(len(description_texts))
        g = graph_representation.construct_dependency_network(text, direction=direction)
        metric = graph.GraphMetrics.EIGENVECTOR
        d = graph_representation.graph_to_dict(g, metric)
        rep.append(d)
        g = None # just to make sure..
    rep = graph_representation.dicts_to_vectors(rep)

    print '> Evaluating..'
    score = evaluation.evaluate_retrieval(rep, solution_vectors)
    print '   score:', score
    results['retrieval'] = score

    data.pickle_to_file(results, 'output/dependencies/stop_words_retr_'+direction)

    pp.pprint(results)
    return results
def do_retrieval_experiments(descriptions='air/problem_descriptions',
                                solutions='air/solutions',
                                graph_types=['co-occurrence','dependency','random'],
                                use_frequency=True):
    """
    Experiment used for comparative evaluation of different network
    representations on the retrieval task.

    Toggle comparison with frequency-based methods using *use_frequency*.
    """
    results = {'_solutions':solutions,
                '_descriptions':descriptions,
                '_evaluation':'retrieval'}

    print '> Evaluation type: retrieval'
    print '> Reading cases..'
    descriptions_path = '../data/'+descriptions
    descriptiondata = data.read_data(descriptions_path, graph_types)

    solutions_path = '../data/'+solutions+'_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)

    print '> Evaluating..'
    for gtype in graph_types:
        print '   ',gtype
        docs, labels = descriptiondata[gtype]
        graphs = graph_representation.create_graphs(docs, gtype)
        results[gtype] = {}
        for metric in graph_representation.get_metrics():
            print '    -', metric
            vectors = graph_representation.graphs_to_vectors(graphs, metric)
            results[gtype][metric] = evaluation.evaluate_retrieval(vectors, solution_vectors)
    if use_frequency:
        print '    frequency'
        results['freq'] = {}
        for metric in freq_representation.get_metrics():
            print '    -', metric
            docs, labels = data.read_files(descriptions_path+'_preprocessed')
            vectors = freq_representation.text_to_vector(docs, metric)
            results['freq'][metric] = evaluation.evaluate_retrieval(vectors, solution_vectors)

    print
    pp.pprint(results)
    return results
def evaluate_tc_icc_retrieval():
    graph_metrics = graph_representation.get_metrics(True, exclude_flow=True)

    print '> Reading cases..'
    corpus = 'air/problem_descriptions'
    context = 'window'
    solutions_path  = '../data/air/solutions_preprocessed'
    path            = '../data/air/problem_descriptions_preprocessed'
    description_texts, labels = data.read_files(path)

    rep = {}
    icc = {}
    print '> Calculating ICCs..'
    for metric in graph_metrics:
        print '   ', metric
        rep[metric] = []
        centralities = retrieve_centralities(corpus, context, metric)
        if centralities:
            icc[metric] = graph_representation.calculate_icc_dict(centralities)
        else:
            icc[metric] = None

    print '> Creating solution representations..'
    solutions_texts, labels = data.read_files(solutions_path)
    solutions_rep = freq_representation.text_to_vector(solutions_texts, freq_representation.FrequencyMetrics.TF_IDF)

    print '> Creating problem description representations..'
    for i, text in enumerate(description_texts):
        if i%1==0: print '    document',str(i)+'/'+str(len(description_texts))
        g = graph_representation.construct_cooccurrence_network(text, already_preprocessed=True, context='window')
        for metric in graph_metrics:
            if not icc[metric]: continue
            #~ print '   ',metric
            d = graph_representation.graph_to_dict(g, metric, icc[metric])
            rep[metric].append(d)
        g = None # just to make sure..

    print '> Creating vector representations..'
    for metric in graph_metrics:
        if not icc[metric]: continue
        rep[metric] = graph_representation.dicts_to_vectors(rep[metric])

    print '> Evaluating..'
    results = {}
    for metric in graph_metrics:
        if not icc[metric]:
            results[metric] = None
            continue
        vectors = rep[metric]
        score = evaluation.evaluate_retrieval(vectors, solutions_rep)
        print '   ', metric, score
        results[metric] = score

    pp.pprint(results)
    data.pickle_to_file(results, 'output/tc_icc/cooccurrence/'+corpus+'/retrieval.res')
    return results
def centrality_weights_retrieval(weighted=True):
    """
    Evaluate whether edge weights are beneficial to the depdendency
    network represenation for the retrieval task.
    """
    results = {'_is_weighted':weighted, '_evaluation':'retrieval'}
    graph_metrics = graph_representation.get_metrics(weighted)

    print '> Reading cases..'
    descriptions_path = '../data/air/problem_descriptions_dependencies'
    description_texts, labels = data.read_files(descriptions_path)

    solutions_path = '../data/air/solutions_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)

    rep = {}
    for metric in graph_metrics:
        rep[metric] = []

    print '> Creating graph representations..'
    for i, text in enumerate(description_texts):
        if i%10==0: print '   ',str(i)+'/'+str(len(description_texts))
        g = graph_representation.construct_dependency_network(text, weighted=weighted)
        for metric in graph_metrics:
            d = graph_representation.graph_to_dict(g, metric)
            rep[metric].append(d)
        g = None # just to make sure..
        if i%100==0:
            if weighted:
                postfix = '_weighted'
            else:
                postfix = '_unweighted'
            data.pickle_to_file(rep, 'output/dependencies/exp1_retr_tmp_'+str(i)+'_'+postfix)

    print '> Creating vector representations..'
    for metric in graph_metrics:
        rep[metric] = graph_representation.dicts_to_vectors(rep[metric])

    print '> Evaluating..'
    for metric in graph_metrics:
        vectors = rep[metric]
        score = evaluation.evaluate_retrieval(vectors, solution_vectors)
        print '   ', metric, score
        results[metric] = score

    if weighted:
        postfix = '_weighted'
    else:
        postfix = '_unweighted'
    data.pickle_to_file(results, 'output/dependencies/exp1_retr'+postfix)

    pp.pprint(results)
    return results
def retrieval_comparison_freq(dataset='mir'):
    print '> Reading data..', dataset
    path = '../data/'+dataset+'/problem_descriptions_preprocessed'
    docs, _ = data.read_files(path)

    print '> Creating solution representations..'
    solutions_path = '../data/'+dataset+'/solutions_preprocessed'
    solutions_docs, _ = data.read_files(solutions_path)
    solutions_rep = freq_representation.text_to_vector(solutions_docs, freq_representation.FrequencyMetrics.TF_IDF)

    print '> Evaluating..'
    results = {}
    for metric in freq_representation.get_metrics():
        print '   ', metric,
        descriptions_rep = freq_representation.text_to_vector(docs, metric)
        score = evaluation.evaluate_retrieval(descriptions_rep, solutions_rep)
        results[metric] = score
        print score
    pp.pprint(results)
    s = 'retrieval comparison \nrepresentation: frequency\ndataset:'+dataset+' \nresult:\n'+str(results)+'\n\n\n'
    data.write_to_file(s, 'output/comparison/retrieval')
    return results
def do_context_size_evaluation_retrieval():
    """
    Experiment evaluating performance of different context sizes for
    co-occurrence networks in the retrieval task.
    """
    results = {}
    graph_metrics = graph_representation.get_metrics()
    for metric in graph_metrics:
        results[metric] = []

    print '> Reading cases..'
    descriptions_path = '../data/air/problem_descriptions_preprocessed'
    description_texts, labels = data.read_files(descriptions_path)

    solutions_path = '../data/air/solutions_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)

    for window_size in range(1,11)+[20,40,80]:
        print '-- window size:',window_size

        rep = {}
        for metric in graph_metrics:
            rep[metric] = []
        print '> Creating representations..'

        # creating graphs and finding centralities
        for i, text in enumerate(description_texts):
            if i%10==0: print i
            g = graph_representation.construct_cooccurrence_network(text, window_size=window_size, already_preprocessed=True)
            for metric in graph_metrics:
                d = graph_representation.graph_to_dict(g, metric)
                rep[metric].append(d)
            g = None # just to make sure..

        # creating representation vectors
        for metric in graph_metrics:
            rep[metric] = graph_representation.dicts_to_vectors(rep[metric])

        print '> Evaluating..'
        for metric in graph_metrics:
            vectors = rep[metric]
            score = evaluation.evaluate_retrieval(vectors, solution_vectors)
            print '   ', metric, score
            results[metric].append(score)

        data.pickle_to_file(results, 'output/retr_context_'+str(window_size))

    pp.pprint(results)
    return results
Exemple #11
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def freq_classification(dataset='tasa/TASA900'):
    results = {'_dataset':dataset,
                '_evaluation':'classification'}
    corpus_path = '../data/'+dataset
    results['results'] = {}
    for metric in freq_representation.get_metrics():
        print metric
        documents, labels = data.read_files(corpus_path+'_preprocessed')
        vectors = freq_representation.text_to_vector(documents, metric)
        r = evaluation.evaluate_classification(vectors, labels, mode='cross-validation')
        results['results'][metric] = r
        print '   ', r
        print
    pp.pprint(results)
    return results
Exemple #12
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def do_classification_experiments(dataset='tasa/TASA900',
                                    graph_types = ['co-occurrence','dependency','random'],
                                    use_frequency = True):
    """
    Experiment used for comparative evaluation of different network
    representations on classification.

    Toggle comparison with frequency-based methods using *use_frequency*.
    """
    results = {'_dataset':dataset,
                '_evaluation':'classification'}
    print '> Evaluation type: classification'
    print '> Reading data..', dataset
    corpus_path = '../data/'+dataset
    docdata = data.read_data(corpus_path, graph_types)

    print '> Evaluating..'
    for gtype in graph_types:
        print '   ',gtype
        documents, labels = docdata[gtype]
        graphs = graph_representation.create_graphs(documents, gtype)
        results[gtype] = {}
        for metric in graph_representation.get_metrics():
            print '    -', metric
            vectors = graph_representation.graphs_to_vectors(graphs, metric)
            results[gtype][metric] = evaluation.evaluate_classification(vectors, labels)
    if use_frequency:
        print '    frequency'
        results['freq'] = {}
        for metric in freq_representation.get_metrics():
            print '    -', metric
            documents, labels = data.read_files(corpus_path+'_preprocessed')
            vectors = freq_representation.text_to_vector(documents, metric)
            results['freq'][metric] = evaluation.evaluate_classification(vectors, labels)

    print
    pp.pprint(results)
    return results
def stop_word_evaluation(rem_stop_words):
    """
    Experiment for determining what effect removing stop words have on
    dependency networks.
    """
    results = {'_removing stop-words':rem_stop_words}

    print '------ CLASSIFICATION EVALUATION --------'

    print '> Reading cases..'
    descriptions_path = '../data/tasa/TASA900_dependencies'
    texts, labels = data.read_files(descriptions_path)

    print '> Creating representations..'
    rep = []
    total_nodes = 0
    for i, text in enumerate(texts):
        if i%100==0: print '   ',str(i)+'/'+str(len(texts))
        g = graph_representation.construct_dependency_network(text, remove_stop_words=rem_stop_words)
        total_nodes += len(g.nodes())
        metric  = graph.GraphMetrics.CLOSENESS
        d = graph_representation.graph_to_dict(g, metric)
        rep.append(d)
        g = None # just to make sure..
    rep = graph_representation.dicts_to_vectors(rep)

    print '> Evaluating..'
    score = evaluation.evaluate_classification(rep, labels)
    print '   score:', score
    print '(the networks had a total of',total_nodes,'nodes)'
    results['classification'] = score

    print '------ RETRIEVAL EVALUATION --------'
    print '> Reading cases..'
    descriptions_path = '../data/air/problem_descriptions_dependencies'
    description_texts, labels = data.read_files(descriptions_path)
    solutions_path = '../data/air/solutions_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)

    print '> Creating representations..'
    rep = []
    total_nodes = 0
    for i, text in enumerate(description_texts):
        if i%100==0: print '   ',str(i)+'/'+str(len(description_texts))
        g = graph_representation.construct_dependency_network(text, remove_stop_words=rem_stop_words)
        total_nodes += len(g.nodes())
        metric = graph.GraphMetrics.EIGENVECTOR
        d = graph_representation.graph_to_dict(g, metric)
        rep.append(d)
        g = None # just to make sure..
    rep = graph_representation.dicts_to_vectors(rep)

    print '> Evaluating..'
    score = evaluation.evaluate_retrieval(rep, solution_vectors)
    print '   score:', score
    print '(the networks had a total of',total_nodes,'nodes)'
    results['retrieval'] = score

    if rem_stop_words:
        postfix = '_without'
    else:
        postfix = '_with'
    data.pickle_to_file(results, 'output/dependencies/stop_words_retr'+postfix)

    pp.pprint(results)
    return results
def evaluate_dep_type_sets():
    """
    Evaluation of various sets of dependency relations.

    Each set is excluded from the representation, and the performance recorded.
    The best strategy is to exclude those dependencies which removal lead to the
    greatest imporovement for the representation.
    """
    strategies = {
            'defensive': ['agent', 'advcl', 'parataxis'],
            'aggressive': ['agent', 'advcl', 'parataxis', 'dep', 'aux', 'ccomp', 'xcomp', 'dobj', 'pobj', 'nsubj', 'nsubjpass', 'cc', 'abbrev', 'purpcl', 'predet', 'preconj', 'advmod', 'neg', 'rcmod', 'tmod', 'poss', 'prepc'],
            'compromise_1': ['agent', 'advcl', 'parataxis', 'aux', 'xcomp', 'pobj', 'nsubjpass', 'cc', 'abbrev', 'purpcl', 'predet', 'neg', 'tmod', 'poss', 'prepc'],
            'compromise_2': ['agent', 'advcl', 'parataxis', 'aux', 'xcomp', 'pobj', 'nsubjpass', 'cc', 'abbrev', 'purpcl', 'predet', 'neg', 'tmod', 'poss', 'prepc', 'attr', 'csubj', 'csubjpass', 'number', 'possessive', 'punct', 'ref']
        }
    results = {'classification':{}, 'retrieval':{}}

    print '------ CLASSIFICATION EVALUATION --------'
    print '> Reading cases..'
    descriptions_path = '../data/tasa/TASA900_dependencies'
    texts, labels = data.read_files(descriptions_path)
    print '> Creating representations..'
    rep = {}
    for strategy in strategies:
        rep[strategy] = []
    metric  = graph.GraphMetrics.CLOSENESS
    for i, text in enumerate(texts):
        if i%10==0: print '   ',str(i)+'/'+str(len(texts))
        for strategy in strategies:
            g = graph_representation.construct_dependency_network(text, exclude=strategies[strategy])
            d = graph_representation.graph_to_dict(g, metric)
            rep[strategy].append(d)
            g = None # just to make sure. I don't trust this damn garbage collector...
    for strategy in strategies:
        rep[strategy] = graph_representation.dicts_to_vectors(rep[strategy])
    print '> Evaluating..'
    for strategy in strategies:
        score = evaluation.evaluate_classification(rep[strategy], labels)
        print '  ', strategy, score
        results['classification'][strategy] = score

    data.pickle_to_file(results, 'output/dependencies/types_set_eval_tmp')

    print '------ RETRIEVAL EVALUATION --------'
    print '> Reading cases..'
    descriptions_path = '../data/air/problem_descriptions_dependencies'
    description_texts, labels = data.read_files(descriptions_path)
    solutions_path = '../data/air/solutions_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)
    print '> Creating representations..'
    rep = {}
    for strategy in strategies:
        rep[strategy] = []
    metric = graph.GraphMetrics.EIGENVECTOR
    for i, text in enumerate(description_texts):
        if i%1==0: print '   ',str(i)+'/'+str(len(description_texts))
        full_graph = graph_representation.construct_dependency_network(text)
        for strategy in strategies:
            g = graph_representation.construct_dependency_network(text, exclude=strategies[strategy])
            d = graph_representation.graph_to_dict(g, metric)
            rep[strategy].append(d)
            g = None # just to make sure..
        full_graph = None
        #~ if i%100==0: data.pickle_to_file(rep, 'output/dependencies/types_eval_rep_'+str(i))
    for strategy in strategies:
        rep[strategy] = graph_representation.dicts_to_vectors(rep[strategy])
    print '> Evaluating..'
    for strategy in strategies:
        score = evaluation.evaluate_retrieval(rep[strategy], solution_vectors)
        print '  ', strategy, score
        results['retrieval'][strategy] = score

    pp.pprint(results)
    data.pickle_to_file(results, 'output/dependencies/types_set_eval')

    return results
def evaluate_dep_types():
    """
    Leave-one-out evaluation of the various dependency types from the stanford parser.
    """
    exclude_list = ['dep', 'aux', 'auxpass', 'cop', 'agent', 'acomp',
                    'attr', 'ccomp', 'xcomp', 'complm', 'dobj', 'iobj',
                    'pobj', 'mark', 'rel', 'nsubj', 'nsubjpass', 'csubj',
                    'csubjpass', 'cc', 'conj', 'expl', 'abbrev', 'amod',
                    'appos', 'advcl', 'purpcl', 'det', 'predet', 'preconj',
                    'infmod', 'mwe', 'partmod', 'advmod', 'neg', 'rcmod',
                    'quantmod', 'tmod', 'nn', 'npadvmod', 'num', 'number',
                    'prep', 'poss', 'possessive', 'prt', 'parataxis',
                    'punct', 'ref', 'xsubj', 'pcomp', 'prepc']
    results = {'classification':[], 'retrieval':[]}

    print '------ CLASSIFICATION EVALUATION --------'
    print '> Reading cases..'
    descriptions_path = '../data/tasa/TASA900_dependencies'
    texts, labels = data.read_files(descriptions_path)
    print '> Creating representations..'
    rep = {}
    for exclude_label in exclude_list:
        rep[exclude_label] = []
    metric  = graph.GraphMetrics.CLOSENESS
    for i, text in enumerate(texts):
        if i%10==0: print '   ',str(i)+'/'+str(len(texts))
        full_graph = graph_representation.construct_dependency_network(text)
        for exclude_label in exclude_list:
            g = graph.reduce_edge_set(full_graph, exclude_label)
            d = graph_representation.graph_to_dict(g, metric)
            rep[exclude_label].append(d)
            g = None # just to make sure..
        full_graph = None
    for exclude_label in exclude_list:
        rep[exclude_label] = graph_representation.dicts_to_vectors(rep[exclude_label])
    print '> Evaluating..'
    for exclude_label in exclude_list:
        score = evaluation.evaluate_classification(rep[exclude_label], labels)
        print '  ', exclude_label, score
        results['classification'].append(score)

    data.pickle_to_file(results, 'output/dependencies/types_eval_tmp')

    print '------ RETRIEVAL EVALUATION --------'
    print '> Reading cases..'
    descriptions_path = '../data/air/problem_descriptions_dependencies'
    description_texts, labels = data.read_files(descriptions_path)
    solutions_path = '../data/air/solutions_preprocessed'
    solution_texts, labels = data.read_files(solutions_path)
    solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)
    print '> Creating representations..'
    rep = {}
    for exclude_label in exclude_list:
        rep[exclude_label] = []
    metric = graph.GraphMetrics.EIGENVECTOR
    for i, text in enumerate(description_texts):
        if i%1==0: print '   ',str(i)+'/'+str(len(description_texts))
        full_graph = graph_representation.construct_dependency_network(text)
        for exclude_label in exclude_list:
            g = graph.reduce_edge_set(full_graph, exclude_label)
            d = graph_representation.graph_to_dict(g, metric)
            rep[exclude_label].append(d)
            g = None # just to make sure..
        full_graph = None
        #~ if i%100==0: data.pickle_to_file(rep, 'output/dependencies/types_eval_rep_'+str(i))
    for exclude_label in exclude_list:
        rep[exclude_label] = graph_representation.dicts_to_vectors(rep[exclude_label])
    print '> Evaluating..'
    for exclude_label in exclude_list:
        score = evaluation.evaluate_retrieval(rep[exclude_label], solution_vectors)
        print '  ', exclude_label, score
        results['retrieval'].append(score)

    pp.pprint(results)
    data.pickle_to_file(results, 'output/dependencies/types_eval')

    return results