refactoring everything
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8399552c8d
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@ -8,50 +8,28 @@ from quapy.protocol import AbstractProtocol
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import json
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def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
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# print('reading', path)
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if verbose:
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print(f'loading {path}...', end='')
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df = pd.read_csv(path, sep='\t')
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if verbose:
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print('[done]')
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X = df['text'].values
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y = df['continent'].values
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def load_sample(path, class_name, max_lines=-1):
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"""
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Loads a sample json as a dataframe and returns text and labels for
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the given class_name
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if parse_columns:
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rank = df['rank'].values
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scores = df['score'].values
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rank = rank[y != 'Antarctica']
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scores = scores[y != 'Antarctica']
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X = X[y!='Antarctica']
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y = y[y!='Antarctica']
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if parse_columns:
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order = np.argsort(rank)
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X = X[order]
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y = y[order]
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rank = rank[order]
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scores = scores[order]
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if max_lines is not None:
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X = X[:max_lines]
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y = y[:max_lines]
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return X, y
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def load_json_sample(path, class_name, max_lines=-1):
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obj = json.load(open(path, 'rt'))
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keys = [f'{id}' for id in range(len(obj['text'].keys()))]
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text = [obj['text'][id] for id in keys]
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#print(list(obj.keys()))
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#import sys; sys.exit(0)
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classes = [obj[class_name][id] for id in keys]
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:param path: path to a json file
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:param class_name: string representing the target class
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:param max_lines: if provided and > 0 then returns only the
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first requested number of instances
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:return: texts and labels for class_name
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"""
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df = pd.read_json(path)
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text = df.text.values
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try:
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labels = df[class_name].values
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except KeyError as e:
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print(f'error in {path}; key {class_name} not found')
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raise e
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if max_lines is not None and max_lines>0:
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text = text[:max_lines]
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classes = classes[:max_lines]
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return text, classes
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labels = labels[:max_lines]
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return text, labels
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class TextRankings:
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@ -75,49 +53,81 @@ class TextRankings:
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return texts, labels
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def get_query_id_from_path(path, prefix='training', posfix='200SPLIT'):
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qid = path
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qid = qid[:qid.index(posfix)]
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qid = qid[qid.index(prefix)+len(prefix):]
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return qid
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def filter_by_classes(X, y, classes):
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idx = np.isin(y, classes)
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return X[idx], y[idx]
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class RetrievedSamples(AbstractProtocol):
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def __init__(self, path_dir: str, load_fn, vectorizer, max_train_lines=None, max_test_lines=None, classes=None, class_name=None):
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self.path_dir = path_dir
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def __init__(self,
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class_home: str,
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test_rankings_path: str,
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load_fn,
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vectorizer,
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class_name,
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max_train_lines=None,
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max_test_lines=None,
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classes=None
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):
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self.class_home = class_home
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self.test_rankings_df = pd.read_json(test_rankings_path)
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self.load_fn = load_fn
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self.vectorizer = vectorizer
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self.class_name = class_name
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self.max_train_lines = max_train_lines
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self.max_test_lines = max_test_lines
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self.classes=classes
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assert class_name is not None, 'class name should be specified'
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self.class_name = class_name
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self.text_samples = TextRankings(join(self.path_dir, 'testRankingsRetrieval.json'), class_name=class_name)
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def __call__(self):
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for file in glob(join(self.path_dir, 'training*SPLIT.json')):
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for file in self._list_queries():
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X, y = self.load_fn(file, class_name=self.class_name, max_lines=self.max_train_lines)
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X = self.vectorizer.transform(X)
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texts, y = self.load_fn(file, class_name=self.class_name, max_lines=self.max_train_lines)
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texts, y = filter_by_classes(texts, y, self.classes)
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X = self.vectorizer.transform(texts)
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train_sample = LabelledCollection(X, y, classes=self.classes)
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query_id = get_query_id_from_path(file)
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X, y = self.text_samples.get_sample_Xy(query_id, max_lines=self.max_test_lines)
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query_id = self._get_query_id_from_path(file)
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texts, y = self._get_test_sample(query_id, max_lines=self.max_test_lines)
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texts, y = filter_by_classes(texts, y, self.classes)
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X = self.vectorizer.transform(texts)
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# if len(X)!=qp.environ['SAMPLE_SIZE']:
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# print(f'[warning]: file {file} contains {len(X)} instances (expected: {qp.environ["SAMPLE_SIZE"]})')
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# assert len(X) == qp.environ['SAMPLE_SIZE'], f'unexpected sample size for file {file}, found {len(X)}'
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X = self.vectorizer.transform(X)
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try:
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test_sample = LabelledCollection(X, y, classes=train_sample.classes_)
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yield train_sample, test_sample
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except ValueError as e:
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print(f'file {file} caused error {e}')
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print(f'file {file} caused an exception: {e}')
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yield None, None
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# print('train #classes:', train_sample.n_classes, train_sample.prevalence())
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# print('test #classes:', test_sample.n_classes, test_sample.prevalence())
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yield train_sample, test_sample
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def _list_queries(self):
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return sorted(glob(join(self.class_home, 'training_Query*200SPLIT.json')))
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def _get_test_sample(self, query_id, max_lines=-1):
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df = self.test_rankings_df
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sel_df = df[df.qid==int(query_id)]
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texts = sel_df.text.values
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try:
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labels = sel_df[self.class_name].values
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except KeyError as e:
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print(f'error: key {self.class_name} not found in test rankings')
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raise e
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if max_lines > 0 and len(texts) > max_lines:
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ranks = sel_df.rank.values
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idx = np.argsort(ranks)[:max_lines]
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texts = np.asarray(texts)[idx]
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labels = np.asarray(labels)[idx]
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return texts, labels
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def total(self):
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return len(self._list_queries())
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def _get_query_id_from_path(self, path):
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prefix = 'training_Query-'
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posfix = 'Sample-200SPLIT'
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qid = path
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qid = qid[:qid.index(posfix)]
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qid = qid[qid.index(prefix) + len(prefix):]
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return qid
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@ -1,427 +0,0 @@
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import os.path
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import numpy as np
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import itertools
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from scipy.stats import ttest_ind_from_stats, wilcoxon
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from pathlib import Path
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from os.path import join
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class Table:
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VALID_TESTS = [None, "wilcoxon", "ttest"]
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def __init__(self, benchmarks, methods, lower_is_better=True, ttest='ttest', prec_mean=3,
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clean_zero=False, show_std=False, prec_std=3, average=True, missing=None, missing_str='--',
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color=True, color_mode='local', maxtone=50):
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assert ttest in self.VALID_TESTS, f'unknown test, valid are {self.VALID_TESTS}'
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self.benchmarks = np.asarray(benchmarks)
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self.benchmark_index = {row:i for i, row in enumerate(benchmarks)}
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self.methods = np.asarray(methods)
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self.method_index = {col:j for j, col in enumerate(methods)}
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self.map = {}
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# keyed (#rows,#cols)-ndarrays holding computations from self.map['values']
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self._addmap('values', dtype=object)
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self.lower_is_better = lower_is_better
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self.ttest = ttest
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self.prec_mean = prec_mean
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self.clean_zero = clean_zero
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self.show_std = show_std
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self.prec_std = prec_std
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self.add_average = average
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self.missing = missing
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self.missing_str = missing_str
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self.color = color
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self.color_mode = color_mode
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self.maxtone = maxtone
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self.touch()
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@property
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def nbenchmarks(self):
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return len(self.benchmarks)
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@property
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def nmethods(self):
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return len(self.methods)
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def touch(self):
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self._modif = True
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def update(self):
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if self._modif:
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self.compute()
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def _getfilled(self):
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return np.argwhere(self.map['fill'])
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@property
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def values(self):
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return self.map['values']
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def _indexes(self):
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return itertools.product(range(self.nbenchmarks), range(self.nmethods))
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def _addmap(self, map, dtype, func=None):
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self.map[map] = np.empty((self.nbenchmarks, self.nmethods), dtype=dtype)
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if func is None:
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return
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m = self.map[map]
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f = func
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indexes = self._indexes() if map == 'fill' else self._getfilled()
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for i, j in indexes:
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m[i, j] = f(self.values[i, j])
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def _addrank(self):
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for i in range(self.nbenchmarks):
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filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
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col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
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ranked_cols_idx = filled_cols_idx[np.argsort(col_means)]
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if not self.lower_is_better:
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ranked_cols_idx = ranked_cols_idx[::-1]
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self.map['rank'][i, ranked_cols_idx] = np.arange(1, len(filled_cols_idx)+1)
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def _addcolor(self):
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minval = {}
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maxval = {}
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if self.color_mode == 'global':
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filled_cols_idx = np.argwhere(self.map['fill'])
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col_means = [self.map['mean'][i, j] for i, j in filled_cols_idx]
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if len(filled_cols_idx) > 0:
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global_minval = min(col_means)
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global_maxval = max(col_means)
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for i in range(self.nbenchmarks):
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minval[i] = global_minval
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maxval[i] = global_maxval
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elif self.color_mode == 'local':
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for i in range(self.nbenchmarks):
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filled_cols_idx = np.argwhere(self.map['fill'][i, i + 1])
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if len(filled_cols_idx)>0:
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col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
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minval[i] = min(col_means)
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maxval[i] = max(col_means)
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else:
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print(f'color mode {self.color_mode} not understood, valid ones are "local" and "global"; skip')
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return
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for i in range(self.nbenchmarks):
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filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
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for col_idx in filled_cols_idx:
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val = self.map['mean'][i,col_idx]
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if i not in maxval or i not in minval:
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continue
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norm = (maxval[i] - minval[i])
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if norm > 0:
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normval = (val - minval[i]) / norm
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else:
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normval = 0.5
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if self.lower_is_better:
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normval = 1 - normval
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normval = np.clip(normval, 0,1)
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self.map['color'][i, col_idx] = color_red2green_01(normval, self.maxtone)
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def _run_ttest(self, row, col1, col2):
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mean1 = self.map['mean'][row, col1]
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std1 = self.map['std'][row, col1]
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nobs1 = self.map['nobs'][row, col1]
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mean2 = self.map['mean'][row, col2]
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std2 = self.map['std'][row, col2]
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nobs2 = self.map['nobs'][row, col2]
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_, p_val = ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
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return p_val
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def _run_wilcoxon(self, row, col1, col2):
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values1 = self.map['values'][row, col1]
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values2 = self.map['values'][row, col2]
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try:
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_, p_val = wilcoxon(values1, values2)
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except ValueError:
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p_val = 0
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return p_val
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def _add_statistical_test(self):
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if self.ttest is None:
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return
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self.some_similar = [False]*self.nmethods
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for i in range(self.nbenchmarks):
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filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
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if len(filled_cols_idx) <= 1:
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continue
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col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
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best_pos = filled_cols_idx[np.argmin(col_means)]
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for j in filled_cols_idx:
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if j==best_pos:
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continue
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if self.ttest == 'ttest':
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p_val = self._run_ttest(i, best_pos, j)
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else:
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p_val = self._run_wilcoxon(i, best_pos, j)
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pval_outcome = pval_interpretation(p_val)
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self.map['ttest'][i, j] = pval_outcome
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if pval_outcome != 'Diff':
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self.some_similar[j] = True
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def compute(self):
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self._addmap('fill', dtype=bool, func=lambda x: x is not None)
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self._addmap('mean', dtype=float, func=np.mean)
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self._addmap('std', dtype=float, func=np.std)
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self._addmap('nobs', dtype=float, func=len)
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self._addmap('rank', dtype=int, func=None)
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self._addmap('color', dtype=object, func=None)
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self._addmap('ttest', dtype=object, func=None)
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self._addmap('latex', dtype=object, func=None)
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self._addrank()
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self._addcolor()
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self._add_statistical_test()
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if self.add_average:
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self._addave()
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self._modif = False
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def _is_column_full(self, col):
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return all(self.map['fill'][:, self.method_index[col]])
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def _addave(self):
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ave = Table(['ave'], self.methods,
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lower_is_better=self.lower_is_better,
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ttest=self.ttest,
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average=False,
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missing=self.missing,
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missing_str=self.missing_str,
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prec_mean=self.prec_mean,
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prec_std=self.prec_std,
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clean_zero=self.clean_zero,
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show_std=self.show_std,
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color=self.color,
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maxtone=self.maxtone)
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for col in self.methods:
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values = None
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if self._is_column_full(col):
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if self.ttest == 'ttest':
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# values = np.asarray(self.map['mean'][:, self.method_index[col]])
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values = np.concatenate(self.values[:, self.method_index[col]])
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else: # wilcoxon
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# values = np.asarray(self.map['mean'][:, self.method_index[col]])
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values = np.concatenate(self.values[:, self.method_index[col]])
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ave.add('ave', col, values)
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self.average = ave
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def add(self, benchmark, method, values):
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if values is not None:
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values = np.asarray(values)
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if values.ndim==0:
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values = values.flatten()
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rid, cid = self._coordinates(benchmark, method)
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self.map['values'][rid, cid] = values
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self.touch()
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def get(self, benchmark, method, attr='mean'):
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self.update()
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assert attr in self.map, f'unknwon attribute {attr}'
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rid, cid = self._coordinates(benchmark, method)
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if self.map['fill'][rid, cid]:
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v = self.map[attr][rid, cid]
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if v is None or (isinstance(v,float) and np.isnan(v)):
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return self.missing
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return v
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else:
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return self.missing
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def _coordinates(self, benchmark, method):
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assert benchmark in self.benchmark_index, f'benchmark {benchmark} out of range'
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assert method in self.method_index, f'method {method} out of range'
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rid = self.benchmark_index[benchmark]
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cid = self.method_index[method]
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return rid, cid
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def get_average(self, method, attr='mean'):
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self.update()
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if self.add_average:
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return self.average.get('ave', method, attr=attr)
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return None
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def get_color(self, benchmark, method):
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color = self.get(benchmark, method, attr='color')
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if color is None:
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return ''
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return color
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def latex(self, benchmark, method):
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self.update()
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i,j = self._coordinates(benchmark, method)
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if self.map['fill'][i,j] == False:
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return self.missing_str
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mean = self.map['mean'][i,j]
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l = f" {mean:.{self.prec_mean}f}"
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if self.clean_zero:
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l = l.replace(' 0.', '.')
|
||||
|
||||
isbest = self.map['rank'][i,j] == 1
|
||||
if isbest:
|
||||
l = "\\textbf{"+l.strip()+"}"
|
||||
|
||||
stat = '' if self.ttest is None else '^{\phantom{\ddag}}'
|
||||
if self.ttest is not None and self.some_similar[j]:
|
||||
test_label = self.map['ttest'][i,j]
|
||||
if test_label == 'Sim':
|
||||
stat = '^{\dag}'
|
||||
elif test_label == 'Same':
|
||||
stat = '^{\ddag}'
|
||||
elif isbest or test_label == 'Diff':
|
||||
stat = '^{\phantom{\ddag}}'
|
||||
|
||||
std = ''
|
||||
if self.show_std:
|
||||
std = self.map['std'][i,j]
|
||||
std = f" {std:.{self.prec_std}f}"
|
||||
if self.clean_zero:
|
||||
std = std.replace(' 0.', '.')
|
||||
std = f"\pm {std:{self.prec_std}}"
|
||||
|
||||
if stat!='' or std!='':
|
||||
l = f'{l}${stat}{std}$'
|
||||
|
||||
if self.color:
|
||||
l += ' ' + self.map['color'][i,j]
|
||||
|
||||
return l
|
||||
|
||||
def latexPDF(self, path, name:str, *args, **kwargs):
|
||||
if not name.endswith('.tex'):
|
||||
name += '.tex'
|
||||
|
||||
self.latexSaveDocument(join(path, name), *args, **kwargs)
|
||||
|
||||
print("[Tables Done] runing latex")
|
||||
os.chdir(path)
|
||||
os.system('pdflatex '+name)
|
||||
basename = name.replace('.tex', '')
|
||||
os.system(f'rm {basename}.aux {basename}.bbl {basename}.blg {basename}.log {basename}.out {basename}.dvi')
|
||||
os.chdir('..')
|
||||
|
||||
def latexSaveDocument(self, path, *args, **kwargs):
|
||||
document = self.latexDocument(*args, **kwargs)
|
||||
parent = Path(path).parent
|
||||
os.makedirs(parent, exist_ok=True)
|
||||
with open(path, 'wt') as foo:
|
||||
foo.write(document)
|
||||
print('text file save at ', path)
|
||||
|
||||
def latexDocument(self, *args, **kwargs):
|
||||
document = """
|
||||
\\documentclass[10pt,a4paper]{article}
|
||||
\\usepackage[utf8]{inputenc}
|
||||
\\usepackage{amsmath}
|
||||
\\usepackage{amsfonts}
|
||||
\\usepackage{amssymb}
|
||||
\\usepackage{graphicx}
|
||||
\\usepackage{xcolor}
|
||||
\\usepackage{colortbl}
|
||||
|
||||
\\begin{document}
|
||||
"""
|
||||
document += self.latexTable(*args, **kwargs)
|
||||
document += "\n\end{document}\n"
|
||||
return document
|
||||
|
||||
def latexTable(self, benchmark_replace={}, method_replace={}, aslines=False, endl='\\\\\hline', resizebox=True):
|
||||
table = """
|
||||
\\begin{table}
|
||||
\center
|
||||
%%%\\resizebox{\\textwidth}{!}{% \n
|
||||
"""
|
||||
table += "\n\\begin{tabular}{|c"+"|c" * self.nmethods + "|}\n"
|
||||
table += self.latexTabular(benchmark_replace, method_replace, aslines, endl)
|
||||
table += "\n\\end{tabular}\n"
|
||||
table += """
|
||||
%%%}%
|
||||
\end{table}
|
||||
"""
|
||||
if resizebox:
|
||||
table = table.replace("%%%", "")
|
||||
return table
|
||||
|
||||
def latexTabular(self, benchmark_replace={}, method_replace={}, aslines=False, endl='\\\\\hline'):
|
||||
lines = []
|
||||
l = '\multicolumn{1}{c|}{} & '
|
||||
l += ' & '.join([method_replace.get(col, col) for col in self.methods])
|
||||
l += ' \\\\\hline'
|
||||
lines.append(l)
|
||||
|
||||
for row in self.benchmarks:
|
||||
rowname = benchmark_replace.get(row, row)
|
||||
l = rowname + ' & '
|
||||
l += self.latexRow(row, endl=endl)
|
||||
lines.append(l)
|
||||
|
||||
if self.add_average:
|
||||
# l += '\hline\n'
|
||||
l = '\hline \n \\textit{Average} & '
|
||||
l += self.latexAverage(endl=endl)
|
||||
lines.append(l)
|
||||
if not aslines:
|
||||
lines='\n'.join(lines)
|
||||
return lines
|
||||
|
||||
def latexRow(self, benchmark, endl='\\\\\hline\n'):
|
||||
s = [self.latex(benchmark, col) for col in self.methods]
|
||||
s = ' & '.join(s)
|
||||
s += ' ' + endl
|
||||
return s
|
||||
|
||||
def latexAverage(self, endl='\\\\\hline\n'):
|
||||
if self.add_average:
|
||||
return self.average.latexRow('ave', endl=endl)
|
||||
|
||||
def getRankTable(self, prec_mean=0):
|
||||
t = Table(benchmarks=self.benchmarks, methods=self.methods, prec_mean=prec_mean, average=True, maxtone=self.maxtone, ttest=None)
|
||||
for rid, cid in self._getfilled():
|
||||
row = self.benchmarks[rid]
|
||||
col = self.methods[cid]
|
||||
t.add(row, col, self.get(row, col, 'rank'))
|
||||
t.compute()
|
||||
return t
|
||||
|
||||
def dropMethods(self, methods):
|
||||
drop_index = [self.method_index[m] for m in methods]
|
||||
new_methods = np.delete(self.methods, drop_index)
|
||||
new_index = {col:j for j, col in enumerate(new_methods)}
|
||||
|
||||
self.map['values'] = self.values[:,np.asarray([self.method_index[m] for m in new_methods], dtype=int)]
|
||||
self.methods = new_methods
|
||||
self.method_index = new_index
|
||||
self.touch()
|
||||
|
||||
|
||||
def pval_interpretation(p_val):
|
||||
if 0.005 >= p_val:
|
||||
return 'Diff'
|
||||
elif 0.05 >= p_val > 0.005:
|
||||
return 'Sim'
|
||||
elif p_val > 0.05:
|
||||
return 'Same'
|
||||
|
||||
|
||||
def color_red2green_01(val, maxtone=50):
|
||||
if np.isnan(val): return None
|
||||
assert 0 <= val <= 1, f'val {val} out of range [0,1]'
|
||||
|
||||
|
||||
# rescale to [-1,1]
|
||||
val = val * 2 - 1
|
||||
if val < 0:
|
||||
color = 'red'
|
||||
tone = maxtone * (-val)
|
||||
else:
|
||||
color = 'green'
|
||||
tone = maxtone * val
|
||||
return '\cellcolor{' + color + f'!{int(tone)}' + '}'
|
||||
|
|
@ -1,66 +0,0 @@
|
|||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
|
||||
from sklearn.metrics import make_scorer, f1_score
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
from quapy.data.base import LabelledCollection
|
||||
from sklearn.model_selection import cross_val_score, GridSearchCV
|
||||
|
||||
from os.path import join
|
||||
|
||||
"""
|
||||
In this experiment, I simply try to understand whether the learning task can be learned or not.
|
||||
The problem is that we are quantifying the categories based on the alphabetical order (of what?).
|
||||
"""
|
||||
|
||||
def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
|
||||
if verbose:
|
||||
print(f'loading {path}...', end='')
|
||||
df = pd.read_csv(path, sep='\t')
|
||||
if verbose:
|
||||
print('[done]')
|
||||
X = df['text'].values
|
||||
y = df['continent'].values
|
||||
|
||||
if parse_columns:
|
||||
rank = df['rank'].values
|
||||
scores = df['score'].values
|
||||
order = np.argsort(rank)
|
||||
X = X[order]
|
||||
y = y[order]
|
||||
rank = rank[order]
|
||||
scores = scores[order]
|
||||
|
||||
if max_lines is not None:
|
||||
X = X[:max_lines]
|
||||
y = y[:max_lines]
|
||||
|
||||
return X, y
|
||||
|
||||
data_path = './50_50_split_trec'
|
||||
train_path = join(data_path, 'train_50_50_continent.txt')
|
||||
|
||||
tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
|
||||
data = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True, parse_columns=False)
|
||||
data = data.sampling(20000)
|
||||
train, test = data.split_stratified()
|
||||
train.instances = tfidf.fit_transform(train.instances)
|
||||
test.instances = tfidf.transform(test.instances)
|
||||
|
||||
# svm = LinearSVC()
|
||||
# cls = GridSearchCV(svm, param_grid={'C':np.logspace(-3,3,7), 'class_weight':['balanced', None]})
|
||||
cls = LogisticRegression()
|
||||
cls.fit(*train.Xy)
|
||||
|
||||
# score = cross_val_score(LogisticRegressionCV(), *data.Xy, scoring=make_scorer(f1_score, average='macro'), n_jobs=-1, cv=5)
|
||||
# print(score)
|
||||
# print(np.mean(score))
|
||||
|
||||
y_pred = cls.predict(test.instances)
|
||||
macrof1 = f1_score(y_true=test.labels, y_pred=y_pred, average='macro')
|
||||
microf1 = f1_score(y_true=test.labels, y_pred=y_pred, average='micro')
|
||||
|
||||
print('macro', macrof1)
|
||||
print('micro', microf1)
|
||||
Loading…
Reference in New Issue