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create_trees_website.py
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create_trees_website.py
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import pickle, json, os, pyconll, sys
import utils, dataloader
import argparse
import numpy as np
np.random.seed(1)
import sklearn
from sklearn.externals.six import StringIO
from sklearn.model_selection import GridSearchCV
from sklearn.tree.export import export_text
import pydotplus
from copy import deepcopy
from collections import defaultdict
def printTreeWithExamplesPDF(model, treerules, treelines, leaves, feature, leafcount, feature_names, test_samples, train_samples, dev_samples):
dot_data = StringIO()
sklearn.tree.export_graphviz(model.best_estimator_, out_file=dot_data,
feature_names=feature_names, node_ids=True
, class_names=["disagree", "agree"], proportion=False, rounded=True, filled=True,
leaves_parallel=False, impurity=False)
nodes = []
else_nodes = []
#REtrieve the egde information in nodes, and else-nodes
for tree_rule, treeline in zip(treerules.split("\n"), treelines.split("\n")):
header = ""
if feature in tree_rule:
header = feature
if "head" in tree_rule:
if "<=" in tree_rule:
nodes.append(header + " not in [head]")
else:
else_nodes.append(header + " in [head]")
elif "child" in tree_rule:
if "<=" in tree_rule:
nodes.append(header + " not in [child]")
else:
else_nodes.append(header + " in [child]")
else:
if "relation" in tree_rule:
header = "relation"
elif "head" in tree_rule:
header="head-pos"
elif "child" in tree_rule:
header="child-pos"
info = "[" + treeline.split("[")[-1]
if "<=" in tree_rule:
nodes.append(header + " in " + info.lstrip().rstrip().lower())
if "else" in treeline:
else_nodes.append(header + " in " +info.lstrip().rstrip().lower())
try:
os.mkdir(f"{folder_name}/{lang}/{feature}")
except OSError:
#print(f"Directory websiter/{lang}/{feature} already exists")
i = 0
#Traverse the tree to add the information in required format
pos_set = printPOSInfomation(feature)
filename = f"{folder_name}/{lang}/{feature}/{feature}.html"
if args.hard:
threshold = 0.9
else:
threshold = 0.01
with open(filename, 'w') as outp:
HEADER = ORIG_HEADER.replace("main.css", "../../main.css")
outp.write(HEADER + '\n')
outp.write(f'<ul class="nav"><li class="nav"><a class="active" href=\"../../index.html\">Home</a>'
f'</li><li class="nav"><a href=\"../../introduction.html\">Usage</a></li>'
f'<li class="nav"><a href="../../about.html\">About Us</a></li></ul>')
outp.write(f"<br><li><a href=\"../index.html\">Back to {language_fullname} page</a></li>\n")
outp.write(f"<h1> Token Distribution across {feature} </h1>")
outp.write(f"<p>The following histogram captures the token distribution per different part-of-speech (POS) tags.</p>")
outp.write(f"<p>Legend on the top-right shows the different values the {feature} attribute takes.<br>'NA' denotes those tokens which do not possess the {feature} attribute.</p>")
outp.write(f"<img src=\"pos.png\" alt=\"{feature}\">")
outp.write(f"<h2>Token examples for each POS:</h2>")
for pos_tag in pos_set:
outp.write(
f"<li.h><a href=\"{pos_tag}.html\"> {pos_tag}</a> </li.h>")
outp.write(f"<h2>{feature} agreement rules:</h2>\n")
outp.write(f"<p> The following decision tree visualizes the rules used for classifying presence/absence of morphological agreement between two tokens that are connected by a dependency relation denoted by <i>relation</i>. "
f"<i>head-pos</i> and <i>child-pos</i> refer to the POS tag of the head and child token respectively.</p> ")
outp.write(f"<p> Each node of the tree represents a portion of the data. <i>samples</i> denotes the number of training data points in that node. <i>value</i> is the class distribution within that node. Each edge denotes the feature used for splitting. <br>"
f"Leaf nodes contain the description of all of the features that appear in that leaf. <i>*</i> denotes that the feature can take any value.</p>")
#outp.write(f"<p> Given that all feature values for {feature} are often not equally probable, we use a threshold <i>t</i> to decide the probability of agreement vs non-chance agreement. Click on <i>p</i> to toggle between showing/hiding the tree with p-value=p</p>")
#outp.write(f"<p> We evalute the tree on test data along three metrics. <i>Unweighted Distributional Similarity</i> (UDS) measures how the class distribution over the training data matches with the test data for each leaf. <i>Weighted Distributional Similarity</i> (WDS) weighs the UDS score with percent of test data in that leaf. <i> Agreement-Only Distributional Similarity</i> (ADS) measures the UDS score for only the \"agreement\" class </p> ")
outp.write(f"<h2> <a id=\"show_image9\">Tree for p={threshold}</a> </h2>")
outp.write(f"<div id=\"show_image9div\" >")
outp.write(f"<p> Click on <button id=\"show_summary9\">Summary</button> to show summary of agreement rules. </p>")
editedgraph = deepcopy(dot_data.getvalue()).split("\n")
tree_dictionary, top_nodes = {}, []
leafnodes = []
leafedges = {}
leafvalues = {}
graph, collated_graph, relabeled_leaves, collate_leaves, graphLines = getTree(dot_data, editedgraph, else_nodes, feature, leaves, nodes,
tree_dictionary, top_nodes, leafnodes, leafedges, leafvalues,
threshold=threshold)
'''
WDS_9, DS_9, A_9, test_leaves_distr = utils.distributional_metric(relabeled_leaves, test_path, feature,
data_loader.feature_distribution[feature],
test_samples, threshold=threshold, hard=args.hard)
'''
automated_acc, test_leaves_distr = utils.automated_metric(relabeled_leaves, test_path, feature,
data_loader.feature_distribution[feature],
test_samples, threshold=threshold, hard=args.hard, traindata=data)
#collated_graph = addValuesEval(collated_graph, graphLines, test_leaves_distr)
with open(f'{args.seed}_{lang_full}_{feature}_{percent}_9.pkl', 'wb') as f:
pickle.dump(collate_leaves, f)
summary = getLeafInfo(collate_leaves, feature, leafvalues)
outp.write(f"<div id=\"summary9\"> {summary} </div>")
image = f"{folder_name}/" + lang_full + "/" + feature + "/" + feature + "_collate9.png"
collated_graph.write_png(image)
#collated_graph.write_pdf(f"{folder_name}/" + lang_full + "/" + feature + "/" + feature + "9.pdf")
#collated_graph.write_pdf(f'{lang_full}-{feature}.pdf')
outp.write(f"<img id=\"collate9img\" src=\"{feature}_collate9.png\" alt=\"{feature}\"> ")
outp.write(f"<h3> Examples for each leaf node: \n </h3>")
for leaf_num, _ in enumerate(collate_leaves):
outp.write(
f"<li.h><a href=\"{feature}-{leaf_num}-.html\"> Leaf-{leaf_num}</a> </li.h>")
if graph:
outp.write(f"<p> Click on <button id=\"show_collate9\">Expand</button> to expand the tree. </p>")
image = f"{folder_name}/" + lang_full + "/" + feature + "/" + feature + "9.png"
graph.write_png(image)
outp.write(f"<img id=\"my_images9\" src=\"{feature}9.png\" style=\"display:none;\">")
#outp.write(
# f"<h3> <b>Test Metrics </b> </h3> <p> Unweighted Distributional Similarity: {DS_9}")
#outp.write(f"Agreement-Only Distributional Similarity: {A_9} </p></div>")
#print("test" + percent + ", " + str(train_samples) + ", " + lang + ", " + feature + ", " + str(WDS_9) + ", " + str(DS_9) + ", " + str(A_9))
print("test" + percent + ", " + str(train_samples) + ", " + lang + ", " + feature + ", " + str( automated_acc) )
if dev_samples > 0:
'''
dev_wds, dev_ds, dev_ads, _ = utils.distributional_metric(relabeled_leaves, dev_path, feature,
data_loader.feature_distribution[feature],
dev_samples, threshold=threshold,
hard=args.hard)
'''
automated_acc, test_leaves_distr = utils.automated_metric(relabeled_leaves, dev_path, feature,
data_loader.feature_distribution[feature],
dev_samples, threshold=threshold, hard=args.hard,
traindata=data)
print("dev" + percent + ", " + str(train_samples) + ", " + lang + ", " + feature + ", " + str(automated_acc) )
if not args.inTh:
return
outp.write(f"<h2> <a id=\"show_image5\">Tree for p=0.5</a> </h2>")
outp.write(f"<div id=\"show_image5div\" style=\"display:none;\">")
outp.write(f"<p> Click on <button id=\"show_summary5\">Summary</button> to show summary of agreement rules. </p>")
editedgraph = deepcopy(dot_data.getvalue()).split("\n")
tree_dictionary, top_nodes = {}, []
leafnodes = []
leafedges = {}
leafvalues = {}
graph, collated_graph, relabeled_leaves, collate_leaves, graphLines = getTree(dot_data, editedgraph, else_nodes, feature, leaves, nodes, tree_dictionary,
top_nodes, leafnodes, leafedges, leafvalues, threshold=0.5)
WDS_5, DS_5, A_5, test_leaves_distr = utils.distributional_metric(relabeled_leaves, test_path, feature,
data_loader.feature_distribution[feature],
test_samples, threshold=0.5, hard=args.hard)
#collated_graph = addValuesEval(collated_graph, graphLines, test_leaves_distr)
with open(f'{args.seed}_{lang_full}_{feature}_{percent}_5.pkl', 'wb') as f:
pickle.dump(collate_leaves, f)
summary = getLeafInfo(collate_leaves, feature, leafvalues)
outp.write(f"<div id=\"summary5\"> {summary} </div>")
image = f"{folder_name}/" + lang_full + "/" + feature + "/" + feature + "_collate5.png"
collated_graph.write_png(image)
outp.write(f"<img id=\"collate5img\" src=\"{feature}_collate5.png\" alt=\"{feature}\"> ")
outp.write(f"<h3> Examples for each leaf node: \n </h3>")
for leaf_num, _ in enumerate(collate_leaves):
outp.write(
f"<li.h><a href=\"{feature}-{leaf_num}-5.html\"> Leaf-{leaf_num}</a> </li.h>")
if graph:
image = f"{folder_name}/" + lang_full + "/" + feature + "/" + feature + "5.png"
outp.write(f"<p> Click on <button id=\"show_collate5\">Expand</button> to expand the tree. </p>")
graph.write_png(image)
outp.write(f"<img id=\"my_images5\" src=\"{feature}5.png\" style=\"display:none;\" >")
graph.write_pdf(f'./{lang_full}-{feature}-{0.5}.pdf')
#outp.write(
# f"<h3> <b>Test Metrics </b> </h3> <p>Unweighted Distributional Similarity: {DS_5}")
#outp.write(f"Agreement-Only Distributional Similarity: {A_5} </p> </div>")
getLeafInfo(collate_leaves, feature, leafvalues, extension="5")
print(percent + ", " + lang + ", " + feature + ", " + str(WDS_5) + ", " + str(DS_5) + ", " + str(A_5) + "\n")
def addValuesEval(collated_graph, graphLines, test_leaves_distr):
graphforeval = []
for line in graphLines:
if 'class' in line:
leaf_num = int(line.split("Leaf-")[-1].split("\\n")[0])
info = line.split("\\l")
distr = test_leaves_distr[leaf_num]
distr = 'test-value = [' + str(distr[0]) + "," + str(distr[1]) + "]\\l"
graphforeval.append("\\l".join(info[0:-1]) + "\\l" + distr + info[-1])
else:
graphforeval.append(line)
collated_graph = pydotplus.graph_from_dot_data("\n".join(graphforeval))
return collated_graph
def getLeafInfo(collate_leaves, feature, leafvalues, extension=""):
# Add the examples for each leaf
allAgreement = {}
relationcount = defaultdict(lambda:0)
total = 0
for leaf_num, _ in enumerate(collate_leaves):
leaf_node = collate_leaves[leaf_num]
ag_examples, dis_examples, relation_dict, head_pos_dict, child_pos_dict, example, sorted_examplecount = utils.getAggreeingExamples(
leaf_node, feature, data, leafvalues[leaf_num], data_loader.train_random_samples)
with open(f"{folder_name}/{lang}/{feature}/{feature}-{leaf_num}-{extension}.html", 'w') as outp2:
HEADER = ORIG_HEADER.replace("main.css", "../../main.css")
outp2.write(HEADER + '\n')
outp2.write(f'<ul class="nav"><li class="nav"><a class="active" href=\"../../index.html\">Home</a>'
f'</li><li class="nav"><a href=\"../../introduction.html\">Usage</a></li>'
f'<li class="nav"><a href=\"../../about.html\">About Us</a></li></ul>')
outp2.write(f"<br><li><a href=\"{feature}.html\">Back to {feature} {language_fullname} page</a></li>\n")
if len(relation_dict) > 0:
utils.plot_histogram(relation_dict, color='peru', type='Relation',
file=f"./{folder_name}/{lang}/{feature}/{leaf_num}-{extension}")
if len(head_pos_dict) >0:
utils.plot_histogram(head_pos_dict, color='seagreen', type='Head-POS',
file=f"./{folder_name}/{lang}/{feature}/{leaf_num}-{extension}")
if len(child_pos_dict) > 0:
utils.plot_histogram(child_pos_dict, color='olivedrab', type='Child-POS',
file=f"./{folder_name}/{lang}/{feature}/{leaf_num}-{extension}")
outp2.write(f"<h2>Distribution of features within this leaf </h2>")
outp2.write(
f"<p style = \"float: left; font-size: 15pt; text-align: center; width: 33%; \"><img src=\"{leaf_num}-Relation.png\" alt=\"Relation\" style=\"width:100%\"></p>")
outp2.write(
f"<p style = \"float: left; font-size: 15pt; text-align: center; width: 33%; \"><img src=\"{leaf_num}-Head-POS.png\" alt=\"head-pos\" style=\"width:100%\"></p>")
outp2.write(
f"<p style = \"float: left; font-size: 15pt; text-align: center; width: 33%;\"><img src=\"{leaf_num}-Child-POS.png\" alt=\"child-pos\" style=\"width:100%\"></p><br>")
if not ag_examples:
outp2.write("\tNo agree examples found.<br>")
else:
outp2.write("<h2>Agreement Rules sorted by frequency.</h2> <ul>")
required_relation, required_head, required_child, _, _ = utils.parseLeafInformation(leaf_node[1])
for (key, val) in sorted_examplecount:
rule_template = ""
(relation, head_pos, child_pos) = key
ex = example[key]
if leaf_node[0] == "agreement":
if relation not in allAgreement:
allAgreement[relation] = {}
allAgreement[relation][(head_pos, child_pos)] = val
relationcount[relation] += val
total += val
if required_relation is not None:
if relation not in relation_map:
if relation.split("@")[0] in relation_map:
full_relation_name = relation_map[relation.split("@")[0]][0]
url = relation_map[ relation.split("@")[0]][1]
else:
full_relation_name = relation
url=f'https://universaldependencies.org/'
else:
full_relation_name = relation_map[relation][0]
url = relation_map[relation][1]
rule_template = f" When the dependent token is the "
rule_template += f"<i>{full_relation_name}</i>(<a href=\"{url}\">{relation})</a> of the head token, "
if required_head is not None:
if len(rule_template) == 0:
rule_template = f'<p> When the head token is <i>{head_pos}</i> '
else:
rule_template += f" and the head token is <i>{head_pos}</i> "
if required_child is not None:
if len(rule_template) == 0:
rule_template = f'<p> When the dependent token is <i>{head_pos}</i> '
else:
rule_template += f" and the dependent token is <i>{child_pos}</i>."
outp2.write(f"<li>{rule_template}</li>")
utils.example_web_print(ex, outp2, data)
outp2.write(f"<br>")
outp2.write("</ul>")
if not dis_examples:
outp2.write("\tNo disagree examples found.<br>")
else:
outp2.write("\t<br><h2>Disagree Examples:</h2>")
for ex in dis_examples:
utils.example_web_print(ex, outp2, data)
outp2.write(FOOTER)
#GetSummary of the agreement rules
#Sort the agreement by relation type
if len(allAgreement) == 0:
summary = [f'<p>There is no agreement for {feature}.</p>']
return summary
sorted_relation = sorted(relationcount.items(), key=lambda kv:kv[1],reverse=True)
summary = []
summary.append(f'<ol>')
rulenum=1
headchilddict = defaultdict(set)
for (relation, val) in sorted_relation:
sorted_headchild = sorted(allAgreement[relation].items(), key=lambda kv:kv[1], reverse=True)
#Group-by head
GroupbyHead, GroupbyHeadInfo, GroupByChild, GroupByChildInfo = defaultdict(lambda :0), defaultdict(set), defaultdict(lambda:0), defaultdict(set)
child, head = False, False
if relation is None:
full_relation_name = 'anything'
else:
if relation not in relation_map:
if relation.split("@")[0] in relation_map:
full_relation_name = relation_map[relation.split("@")[0]][0]
else:
full_relation_name = relation
else:
full_relation_name = relation_map[relation][0]
for (headchild, value) in sorted_headchild:
if value *1.0/val < 0.5:
continue
(head, child) = headchild
if child is not None and head is not None:
GroupByChild[child] += value
GroupByChildInfo[child].add(head)
GroupbyHead[head] += value
GroupbyHeadInfo[head].add(child)
child, head = True, True
else:
if head is None:
head = False
else:
GroupbyHead[head] = 0
head=True
if child is None:
child = False
else:
GroupByChild[child] = 0
child=True
if not head and child:
all_childpos = ",".join(list(GroupByChild.keys()))
key=f'<i>{all_childpos}</i> tokens agree with their head'
value=f'<i>{full_relation_name}({relation})</i>'
headchilddict[key].add(value)
elif not child and head:
all_headpos = ",".join(list(GroupbyHead.keys()))
key=f'<i>{all_headpos}</i> tokens agree with their dependent tokens'
value=f'<i>{full_relation_name}({relation})</i>'
headchilddict[key].add(value)
elif not child and not head:
key = f'All tokens agree with their head tokens'
value = f'<i>{full_relation_name} ({relation})</i>'
headchilddict[key].add(value)
elif child and head:
#sort group-by-head and group-by-child and compare the highest values, whichever is higher, choose that as the condition of groupping
sort_grpchild = sorted(GroupByChild.items(), key =lambda kv:kv[1], reverse=True)
sort_grphead = sorted(GroupbyHead.items(), key = lambda kv:kv[1], reverse=True)
if sort_grpchild[0][1] > sort_grphead[0][1]: #Grp by child
for (child, _) in sort_grpchild:
headpos = ", ".join(list(GroupByChildInfo[child]))
headchilddict[f'<i>{child}</i> tokens agree when head token belongs to [<i>{headpos}</i>]'].add(f'<i>{full_relation_name}({relation})</i>')
else:
for (head, _) in sort_grphead:
childpos = ", ".join(list(GroupbyHeadInfo[head]))
headchilddict[f'<i>{head}</i> tokens agree when the dependent token belongs to [<i>{childpos}</i>]'].add(f'<i>{full_relation_name}({relation})</i>')
for rule, relations in headchilddict.items():
summary.append(f'<li> {rule} for the dependency relations: {", ".join(list(relations))} </li><br>')
rulenum+=1
summary.append(f'</ol>')
summary = "".join(summary)
return summary
def getTree(dot_data, editedgraph, else_nodes, feature, leaves, nodes, tree_dictionary, topnodes, leafnodes, leafedges, leafvalues, threshold):
i = 0
leaf_num = 0
leftstart = 0
rightstart = 0
relabeled_leaves = {}
for linenum, line in enumerate(dot_data.getvalue().split("\n")):
if "<=" in line: # If
info = line.split("<=")
info_index = info[-1].find("\\")
nodenum = line.split("[")[0]
textinfo = info[-1][info_index + 2:].split("fillcolor=")[0]
edge = info[0] + " in " + nodes[i]
values = info[-1].split("\\nclass")[0].split("value = ")[1].replace("[", "").replace("]", "").replace("\'", "").split(
",")
disagree, agree = int(values[0]), int(values[1])
color = utils.colorRetrival(agree, disagree, data_loader.feature_distribution[feature], threshold, args.hard)
editedgraph[linenum] = line.split("[")[0] + "[label=\"node - " + nodenum + "\\n" + textinfo.replace("\\n","\\l").replace("class = agree", "").replace("class = disagree", "") + 'fillcolor=\"{0}\"] ;'.format(color)
tree_dictionary[int(nodenum)] = {"children": [], "edge": nodes[i], "info": editedgraph[linenum]}
i += 1
topnodes.append(int(nodenum))
elif "->" in line: # Edge
lefttext = "[labeldistance={0},labelangle=50, headlabel=\"{1}\",labelfontsize=10];"
righttext = "[labeldistance={0},labelangle=-50, headlabel=\" {1}\", labelfontsize=10];"
info = line.replace('\'', '').replace(";", "").split("->")
leftnode, rightnode = int(info[0]), int(info[-1].split("[")[0])
if rightnode - leftnode == 1:
edge = nodes[leftstart]
input = edge.split("[")[-1].replace("]", "").split(",")
edge = edge.split("[")[0] + utils.printMultipleLines(input, t=7)
leftstart += 1
newtext = line.split(str(rightnode))[0] + " " + str(rightnode) + " " + lefttext.format(3.5, edge)
else:
edge = else_nodes[rightstart]
input = edge.split("[")[-1].replace("]", "").split(",")
edge = edge.split("[")[0] + utils.printMultipleLines(input, t=7)
rightstart += 1
newtext = line.split(str(rightnode))[0] + " " + str(rightnode) + " " + righttext.format(3.5, edge)
editedgraph[linenum] = newtext
tree_dictionary[leftnode]["children"].append(rightnode)
tree_dictionary[rightnode]["top"] = leftnode
leafedges[rightnode] = edge
elif ">" in line: # Else
info = line.split(">")
info_index = info[-1].find("\\")
nodenum = line.split("[")[0]
textinfo = info[-1][info_index + 2:].split("fillcolor=")[0]
edge = info[0] + " in " + nodes[i]
values = info[-1].split("\\nclass")[0].split("value = ")[1].replace("[", "").replace("]", "").replace("\'","").split(
",")
disagree, agree = int(values[0]), int(values[1])
color = utils.colorRetrival(agree, disagree, data_loader.feature_distribution[feature], threshold, args.hard)
editedgraph[linenum] = line.split("[")[0] + "[label=\"node - " + nodenum + "\\n" + textinfo.replace("\\n",
"\\l").replace(
"class = agree", "").replace("class = disagree", "") + 'fillcolor=\"{0}\"] ;'.format(color)
tree_dictionary[int(nodenum)] = {"children": [], "edge": nodes[i], "info": editedgraph[linenum]}
i += 1
topnodes.append(int(nodenum))
else: # Leaf
if "class" in line:
info = line.split("label=\"")
info[-1] = "\\n".join(info[-1].split("\\n")[1:])
leafvalues[leaf_num] = info[-1].split("\\n")[1].split("value = ")[1].replace("[", "").replace("]",
"").replace(
"\'", "").split(",")
disagree, agree = int(leafvalues[leaf_num][0]), int(leafvalues[leaf_num][1])
t = agree * 1.0 / (disagree + agree)
agreement = "chance-agreement\\n"
if utils.isAgreement(data_loader.feature_distribution[feature], agree, disagree, threshold, args.hard):# t >= threshold:
agreement = "agreement\\n"
color = utils.colorRetrival(agree, disagree, data_loader.feature_distribution[feature], threshold, args.hard)
text_position = info[-1].split("agree")
classinfo = text_position[0].replace("dis", "") + agreement + "\",fillcolor=\"{0}\"] ;".format(color)
nodenum = line.split("[")[0]
data_info = ""
(leaf_node_class, leaf_node_data) = leaves[leaf_num]
relabeled_leaves[leaf_num] = (leaf_node_data, agree, disagree)
if leaf_node_data["head_feature"] != None:
data_info = leaf_node_data["head_feature"] + "\\n\\n"
if leaf_node_data["child_feature"] != None:
data_info += leaf_node_data["child_feature"] + "\\n\\n"
if leaf_node_data["relation"] == None:
data_info += "relation = *" + "\\l\\l"
else:
class_relations = data_loader.class_relations[leaf_node_class]
input = set(
leaf_node_data["relation"].replace("\'", "").replace("[", "").replace("]", "").split(","))
extra = input - class_relations
actual = input - extra
leaf_node_data["relation"] = ",".join(list(actual))
data_info += "relation = " + utils.printMultipleLines(actual) + "\\l"
if leaf_node_data["head"] == None:
data_info += "head-pos = *" + "\\l\\l"
else:
class_pos = data_loader.class_headpos[leaf_node_class]
input = set(
leaf_node_data["head"].replace("\'", "").replace("[", "").replace("]", "").split(","))
extra = input - class_pos
actual = input - extra
leaf_node_data["head"] = ",".join(list(actual))
data_info += "head-pos = " + utils.printMultipleLines(actual) + "\\l"
if leaf_node_data["child"] == None:
data_info += "child-pos = *" + "\\l\\l"
else:
class_pos = data_loader.class_childpos[leaf_node_class]
input = set(
leaf_node_data["child"].replace("\'", "").replace("[", "").replace("]", "").split(","))
extra = input - class_pos
actual = input - extra
leaf_node_data["child"] = ",".join(list(actual))
data_info += "child-pos = " + utils.printMultipleLines(actual) + "\\l"
textinfo = info[0] + "label=" + "\"Leaf- " + str(
leaf_num) + "\\n" + data_info.lower() + classinfo.replace("\\n", "\\l")
editedgraph[linenum] = textinfo
tree_dictionary[int(nodenum)] = {"children": [], "edge": data_info.lower(),
"info": editedgraph[linenum]}
leaf_num += 1
leafnodes.append(int(nodenum))
if args.prune:
editedgraph, tree_dictionary, leafnodes, topleafnodes, removednodes, topnodes = utils.pruneTree(editedgraph, tree_dictionary, topnodes, leafnodes, leafedges, feature)
#Original decision tree
graph = pydotplus.graph_from_dot_data(editedgraph)
#Collated tree with leaves with same labels are merged
collatedGraph,leaves, relabeled_leaves = utils.collateTree(data_loader.feature_distribution[feature], leafedges, editedgraph, topleafnodes, tree_dictionary, leaves, threshold, topnodes, removednodes, args.hard)
collated_graph = pydotplus.graph_from_dot_data("\n".join(collatedGraph))
new_graph = collatedGraph
else:
graph = pydotplus.graph_from_dot_data("\n".join(editedgraph))
new_graph=editedgraph
return graph, collated_graph, relabeled_leaves, leaves, new_graph
def printPOSInfomation(feature):
pos_set = data_loader.getHistogram(folder_name, lang, train_path, feature)
for pos, pos_dict in data_loader.feature_forms_num.items():
feature_values = pos_dict.keys()
filename = f"{folder_name}/{lang}/{feature}/{pos}.html"
with open(filename, 'w') as outp:
HEADER = ORIG_HEADER.replace("main.css", "../../main.css")
outp.write(HEADER + '\n')
outp.write(f'<ul class="nav"><li class="nav"><a class="active" href=\"../../index.html\">Home</a>'
f'</li><li class="nav"><a href=\"../../introduction.html\">Usage</a></li>'
f'<li class="nav"><a href=\"../../about.html\">About Us</a></li></ul>')
outp.write(f"<br> <a href=\"{feature}.html\">Back to {feature} information</a><br>")
outp.write(f"<h1> Examples of word types for each {feature} value : </h1>")
outp.write(f"<p> The word types shown below are ordered by token frequency in the treebank.")
outp.write(f'<table><col><colgroup span=\"{len(feature_values)}\"></colgroup><tr><th rowspan=\"2\" style=\"text-align:center\">Lemma</th><th rowspan=\"2\" style=\"text-align:center\"> Morphosyntactic <br> Attributes</th><th colspan=\"{len(feature_values)} scope=\"colgroup\" \" style=\"text-align:center\">{feature}</th></tr><tr>')
for feat in feature_values:
outp.write(f'<th scope=\"col\"> {feat} </th>')
outp.write('</tr>')
#Sort the tokens within a pos using lemma
sorted_lemma_dict = sorted(data_loader.lemma_freq[pos].items(), key=lambda kv: kv[1], reverse=True)[:30]
for (lemma, _) in sorted_lemma_dict:
for inflection in data_loader.lemma_inflection[pos][lemma].keys():
outp.write(f'<tr><td> {lemma} </td>')
outp.write(f'<td> {inflection} </td>')
inflection_feature_value = data_loader.lemma_inflection[pos][lemma][inflection]
for feat in feature_values:
if feat in inflection_feature_value:
outp.write(f'<td> {inflection_feature_value[feat]} </td>')
else:
outp.write(f'<td> - </td> ')
outp.write('</tr>')
outp.write('</table>')
outp.write(FOOTER)
return pos_set
def train(feature):
x_train, x_test, y_train, y_test = train_features[feature] , test_features[feature], \
train_output_labels[feature], test_output_labels[feature]
if dev_path:
x_dev, y_dev = dev_features[feature], dev_output_labels[feature]
x = np.concatenate([x_train, x_dev])
y = np.concatenate([y_train, y_dev])
test_fold = np.concatenate([
# The training data.
np.full(x_train.shape[0], -1, dtype=np.int8),
# The development data.
np.zeros(x_dev.shape[0], dtype=np.int8)
])
cv = sklearn.model_selection.PredefinedSplit(test_fold)
else:
x,y = x_train, y_train
cv = None
# Create lists of parameter for Decision Tree Classifier
criterion = ['gini', 'entropy']
parameters = {'criterion':criterion, 'max_depth':np.arange(6, 15), 'min_impurity_decrease':[1e-3]}
decision_tree = sklearn.tree.DecisionTreeClassifier()
model = GridSearchCV( decision_tree , parameters, cv=cv)
model.fit(x, y)
trainleave_id = model.best_estimator_.apply(x)
uniqueleaves = set(trainleave_id)
uniqueleaves = sorted(uniqueleaves)
leafcount = {}
for i, leaf in enumerate(uniqueleaves):
leafcount[i] = round(np.count_nonzero(trainleave_id == leaf) * 100 / len(trainleave_id), 2)
feature_names = []
for i in range(len(data_loader.pos_dictionary)):
feature_names.append("head@" + data_loader.pos_id2tag[i])
for i in range(len(data_loader.pos_dictionary)):
feature_names.append("child@" + data_loader.pos_id2tag[i])
for i in range(len(data_loader.relation_dictionary)):
feature_names.append("relation@" + data_loader.relation_id2tag[i])
feature_names.append(feature + "@child")
feature_names.append(feature + "@head")
tree_rules = export_text(model.best_estimator_, feature_names= feature_names, max_depth=model.best_params_["max_depth"])
treelines = utils.printTreeForBinaryFeatures(tree_rules, data_loader.pos_id2tag, data_loader.relation_id2tag, data_loader.used_relations, data_loader.used_head_pos, data_loader.used_child_pos, feature)
leaves = utils.constructTree(treelines, feature)
assert len(leaves) == len(leafcount)
dev_samples = len(x_dev) if dev_path else 0
printTreeWithExamplesPDF(model, tree_rules, treelines, leaves, feature, leafcount, feature_names, len(y_test), len(x_train), dev_samples)
with open(f"{folder_name}/{lang}/index.html", 'a') as outp:
outp.write(
f"<li>{feature} agreement:. <a href=\"{feature}/{feature}.html\">Examples</a></li>\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--file", type=str, default="./decision_tree_files.txt")
parser.add_argument("--features", type=str, default="Gender+Person+Number+Tense+Mood+Case", nargs='+')
parser.add_argument("--prune", action="store_true", default=True)
parser.add_argument("--binary", action="store_true", default=True)
parser.add_argument("--debug_folder", type=str, default="./")
parser.add_argument("--percent", type=float, default=1.0)
parser.add_argument("--relation_map", type=str, default="./relation_map")
parser.add_argument("--inTh", action="store_true", default=False)
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--simulate", action="store_true", default=False)
parser.add_argument("--hard", action="store_true", default=False)
parser.add_argument("--folder_name", type=str, default='./website/')
args = parser.parse_args()
folder_name = f'{args.folder_name}'
with open(f"{folder_name}/header.html") as inp:
ORIG_HEADER = inp.readlines()
ORIG_HEADER = ''.join(ORIG_HEADER)
with open(f"{folder_name}/footer.html") as inp:
FOOTER = inp.readlines()
FOOTER = ''.join(FOOTER)
with open(args.file, "r") as inp:
files = []
for file in inp.readlines():
if file.startswith("#"):
continue
files.append(file)
d = {}
relation_map = {}
with open(args.relation_map, "r") as inp:
for line in inp.readlines():
relation_map[line.split(";")[0]] = (line.split(";")[1].lstrip().rstrip(), line.split(";")[-1].lstrip().rstrip())
with open(f"{folder_name}/index.html", 'w') as op:
op.write(ORIG_HEADER + '\n')
op.write(f'<ul class="nav"><li class="nav"><a class="active" href=\"index.html\">Home</a>'
f'</li><li class="nav"><a href=\"introduction.html\">Usage</a></li>'
f'<li class="nav"><a href=\"about.html\">About Us</a></li></ul>')
op.write(f'<h2>LASE: Language Structure Explorer</h2>')
op.write(f'<h3> Most of the world\'s languages have an adherence to grammars — sets of morpho-syntactic rules specifying how to create sentences in the language. '
f'Hence, an important step in the understanding and documentation of languages is the creation of a grammar sketch, a concise and human-readabled escription of the unique characteristics of that particular language. </h3>')
op.write(f'<h3> LASE is a tool for exploring language structure and provides an automated framework for '
f'extracting a first-pass grammatical specification from raw text in a concise, human-and machine-readable format.'
f'</h3>')
op.write("<h3> We apply our framework to all languages of the <a href=\"https://universaldependencies.org/\"> Universal Dependencies project </a>. </h3><h3> Here are the languages (and treebanks) we currently support.</h3><br><ul>")
op.write(f'<h3> Linguistic analysis based on automatically parsed syntactic analysis </h3>')
op.write(f'<table><tr><th>ISO</th><th>Language</th><th>Treebank</th><th>Linguistic Analysis </th></tr>')
fnum = 0
args.features = args.features.split("+")
while fnum < len(files):
treebank = files[fnum].strip()
fnum += 1
#print("Processing treebank ", treebank)
train_path, dev_path, test_path = None, None, None
for [path, dir, inputfiles] in os.walk(treebank):
for file in inputfiles:
if "-train.conllu" in file:
train_path = treebank + "/" + file
if args.simulate: #For simulated low-resource training
percent = treebank.split("-")[-2] + "-" + treebank.split("-")[-1]
lang = train_path.strip().split('/')[-1].split("_")[0]
lang += "-" + percent
else:
percent = 'all'
lang = train_path.strip().split('/')[-1].split("-")[0]
if "dev.conllu" in file:
dev_path = treebank + "/" + file
if "test.conllu" in file:
test_path = treebank + "/" + file
if train_path is None:
continue
language_fullname = "_".join(os.path.basename(treebank).split("_")[1:])
lang_full = lang
f = train_path.strip()
i = 0
with open(f"{folder_name}/index.html", 'a') as op:
lang_id = lang.split("_")[0]
language_name = language_fullname.split("-")[0]
treebank_name = language_fullname.split("-")[1]
op.write(f'<tr><td>{lang_id}</td> '
f'<td>{language_name}</td> '
f'<td> {treebank_name} </td>'
f' <td> <li> <a href=\"{lang}/index.html\">Agreement</a></li> </td>\n')
try:
os.mkdir(f"{folder_name}/{lang}")
except OSError:
i =0
with open(f"{folder_name}/{lang}/index.html", 'w') as outp:
HEADER = ORIG_HEADER.replace("main.css", "../main.css")
outp.write(HEADER + "\n")
outp.write(f'<ul class="nav"><li class="nav"><a class="active" href=\"../index.html\">Home</a>'
f'</li><li class="nav"><a href=\"../introduction.html\">Usage</a></li>'
f'<li class="nav"><a href=\"../about.html\">About Us</a></li></ul>')
outp.write(f"<br><a href=\"../index.html\">Back to language list</a><br>")
outp.write( f"<h1> {language_fullname} </h1> <br>\n")
outp.write(f'<h3> We present a framework that automatically creates a first-pass specification of morphological agreement rules for various morphological features (Gender, Number, Person, Tense, Mood and Case.) from a raw text corpus for the language in question.</h3>')
outp.write("<h3> We parsed the <a href=\"https://universaldependencies.org/udw18/PDFs/33_Paper.pdf\">Surface-Syntactic Universal Dependencies</a> (SUD) data in order to extract these rules.</h3>\n")
outp.write(f"<br><strong>{language_fullname}</strong> exhibits the following agreement:<br><ul>")
#Decision Tree code
data = pyconll.load_from_file(f"{f}")
data_loader = dataloader.DataLoader(args, relation_map)
# Creating the vocabulary
inputFiles = [train_path, dev_path, test_path]
data_loader.readData(inputFiles)
# creating the features for training
train_features, train_output_labels = data_loader.getBinaryFeatures(train_path,type="train", p=args.percent, shuffle=True)
if dev_path:
dev_features, dev_output_labels = data_loader.getBinaryFeatures(dev_path, type="dev", p=1.0, shuffle=False)
test_features, test_output_labels = data_loader.getBinaryFeatures(test_path, type="test", p=1.0, shuffle=False)
for feature in args.features:
if feature in test_features and feature in train_features:
try:
train(feature)
except:
print("error processing ", feature, lang)
with open(f"{folder_name}/{lang}/index.html", 'a') as outp:
outp.write("</ul><br><br><br>\n" + FOOTER+"\n")
with open(f"{folder_name}/index.html", 'a') as outp:
outp.write(f'</table>')
outp.write("</ul><br><br><br>\n" + FOOTER+"\n")