result file format check, read, load, and evaluation with pandas

LeQua2022/data.py

@@ -1,6 +1,12 @@
+import os.path
+from typing import List, Tuple, Union
+
+import pandas as pd
+
 import quapy as qp
 import numpy as np
 import sklearn
+import re
 
 
 # def load_binary_raw_document(path):
@@ -40,40 +46,162 @@ def gen_load_samples_T2B(path_dir:str, ground_truth_path:str = None):
 
 
 class ResultSubmission:
-    def __init__(self, team_name, run_name, task_name):
+    DEV_LEN = 1000
-        assert isinstance(team_name, str) and team_name, \
+    TEST_LEN = 5000
-            f'invalid value encountered for team_name'
+    ERROR_TOL = 1E-3
-        assert isinstance(run_name, str) and run_name, \
+
-            f'invalid value encountered for run_name'
+    def __init__(self, categories: List[str]):
-        assert isinstance(task_name, str) and task_name in {'T1A', 'T1B', 'T2A', 'T2B'}, \
+        if not isinstance(categories, list) or len(categories) < 2:
-            f'invalid value encountered for task_name; valid values are T1A, T1B, T2A, and T2B'
+            raise TypeError('wrong format for categories; a list with at least two category names (str) was expected')
-        self.team_name = team_name
+        self.categories = categories
-        self.run_name = run_name
+        self.df = pd.DataFrame(columns=['filename'] + list(categories))
-        self.task_name = task_name
+        self.inferred_type = None
-        self.data = {}
 
     def add(self, sample_name:str, prevalence_values:np.ndarray):
-        # assert the result is a valid sample_name (not repeated)
+        if not isinstance(sample_name, str):
-        pass
+            raise TypeError(f'error: expected str for sample_sample, found {type(sample_name)}')
+        if not isinstance(prevalence_values, np.ndarray):
+            raise TypeError(f'error: expected np.ndarray for prevalence_values, found {type(prevalence_values)}')
+
+        if self.inferred_type is None:
+            if sample_name.startswith('test'):
+                self.inferred_type = 'test'
+            elif sample_name.startswith('dev'):
+                self.inferred_type = 'dev'
+        else:
+            if not sample_name.startswith(self.inferred_type):
+                raise ValueError(f'error: sample "{sample_name}" is not a valid entry for type "{self.inferred_type}"')
+
+        if not re.match("(test|dev)_sample_\d+\.txt", sample_name):
+            raise ValueError(f'error: wrong format "{sample_name}"; right format is (test|dev)_sample_<number>.txt')
+        if sample_name in self.df.filename.values:
+            raise ValueError(f'error: prevalence values for "{sample_name}" already added')
+        if prevalence_values.ndim!=1 and prevalence_values.size != len(self.categories):
+            raise ValueError(f'error: wrong shape found for prevalence vector {prevalence_values}')
+        if (prevalence_values<0).any() or (prevalence_values>1).any():
+            raise ValueError(f'error: prevalence values out of range [0,1] for "{sample_name}"')
+        if np.abs(prevalence_values.sum()-1) > ResultSubmission.ERROR_TOL:
+            raise ValueError(f'error: prevalence values do not sum up to one for "{sample_name}"'
+                             f'(error tolerance {ResultSubmission.ERROR_TOL})')
+
+        new_entry = dict([('filename',sample_name)]+[(col_i,prev_i) for col_i, prev_i in zip(self.categories, prevalence_values)])
+        self.df = self.df.append(new_entry, ignore_index=True)
 
     def __len__(self):
-        return len(self.data)
+        return len(self.df)
 
     @classmethod
     def load(cls, path: str) -> 'ResultSubmission':
-        pass
+        df, inferred_type = ResultSubmission.check_file_format(path, return_inferred_type=True)
+        r = ResultSubmission(categories=df.columns.values.tolist())
+        r.inferred_type = inferred_type
+        r.df = df
+        return r
 
     def dump(self, path:str):
-        # assert all samples are covered (check for test and dev accordingly)
+        ResultSubmission.check_dataframe_format(self.df)
-        pass
+        self.df.to_csv(path)
 
     def get(self, sample_name:str):
-        pass
+        sel = self.df.loc[self.df['filename'] == sample_name]
+        if sel.empty:
+            return None
+        else:
+            return sel.loc[:,self.df.columns[1]:].values.flatten()
+
+    @classmethod
+    def check_file_format(cls, path, return_inferred_type=False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
+        df = pd.read_csv(path, index_col=0)
+        return ResultSubmission.check_dataframe_format(df, path=path, return_inferred_type=return_inferred_type)
+
+    @classmethod
+    def check_dataframe_format(cls, df, path=None, return_inferred_type=False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
+        hint_path = ''  # if given, show the data path in the error messages
+        if path is not None:
+            hint_path = f' in {path}'
+
+        if 'filename' not in df.columns or len(df.columns) < 3:
+            raise ValueError(f'wrong header{hint_path}, the format of the header should be ",filename,<cat_1>,...,<cat_n>"')
+
+        if df.empty:
+            raise ValueError(f'error{hint_path}: results file is empty')
+        elif len(df) == ResultSubmission.DEV_LEN:
+            inferred_type = 'dev'
+            expected_len = ResultSubmission.DEV_LEN
+        elif len(df) == ResultSubmission.TEST_LEN:
+            inferred_type = 'test'
+            expected_len = ResultSubmission.TEST_LEN
+        else:
+            raise ValueError(f'wrong number of prevalence values found{hint_path}; '
+                             f'expected {ResultSubmission.DEV_LEN} for development sets and '
+                             f'{ResultSubmission.TEST_LEN} for test sets; found {len(df)}')
+
+        set_names = frozenset(df.filename)
+        for i in range(expected_len):
+            if f'{inferred_type}_sample_{i}.txt' not in set_names:
+                raise ValueError(f'{hint_path} a file with {len(df)} entries is assumed to be of type '
+                                 f'"{inferred_type}" but entry {inferred_type}_sample_{i}.txt is missing '
+                                 f'(among perhaps many others)')
+
+        for category_name in df.columns[1:]:
+            if (df[category_name] < 0).any() or (df[category_name] > 1).any():
+                raise ValueError(f'{hint_path} column "{category_name}" contains values out of range [0,1]')
+
+        prevs = df.loc[:, df.columns[1]:].values
+        round_errors = np.abs(prevs.sum(axis=-1) - 1.) > ResultSubmission.ERROR_TOL
+        if round_errors.any():
+            raise ValueError(f'warning: prevalence values in rows with id {np.where(round_errors)[0].tolist()} '
+                              f'do not sum up to 1 (error tolerance {ResultSubmission.ERROR_TOL}), '
+                              f'probably due to some rounding errors.')
+
+        if return_inferred_type:
+            return df, inferred_type
+        else:
+            return df
+
+    def sort_categories(self):
+        self.df = self.df.reindex([self.df.columns[0]] + sorted(self.df.columns[1:]), axis=1)
+        self.categories = sorted(self.categories)
 
 
-def evaluate_submission(ground_truth_prevs: ResultSubmission, submission_prevs: ResultSubmission):
 
-    pass
+def evaluate_submission(true_prevs: ResultSubmission, predicted_prevs: ResultSubmission, sample_size=1000, average=True):
+    if len(true_prevs) != len(predicted_prevs):
+        raise ValueError(f'size mismatch, groun truth has {len(true_prevs)} entries '
+                         f'while predictions contain {len(predicted_prevs)} entries')
+    true_prevs.sort_categories()
+    predicted_prevs.sort_categories()
+    if true_prevs.categories != predicted_prevs.categories:
+        raise ValueError(f'these result files are not comparable since the categories are different')
+    ae, rae = [], []
+    for sample_name in true_prevs.df.filename.values:
+        ae.append(qp.error.mae(true_prevs.get(sample_name), predicted_prevs.get(sample_name)))
+        rae.append(qp.error.mrae(true_prevs.get(sample_name), predicted_prevs.get(sample_name), eps=sample_size))
+    ae = np.asarray(ae)
+    rae = np.asarray(rae)
+    if average:
+        return ae.mean(), rae.mean()
+    else:
+        return ae, rae
+
+
+
+
+# r = ResultSubmission(['negative', 'positive'])
+# from tqdm import tqdm
+# for i in tqdm(range(1000), total=1000):
+#     r.add(f'dev_sample_{i}.txt', np.asarray([0.5, 0.5]))
+# r.dump('./path.csv')
+
+r = ResultSubmission.load('./data/T1A/public/dummy_submission.csv')
+t = ResultSubmission.load('./data/T1A/public/dummy_submission (copy).csv')
+# print(r.df)
+# print(r.get('dev_sample_10.txt'))
+print(evaluate_submission(r, t))
+
+# s = ResultSubmission.load('./data/T1A/public/dummy_submission.csv')
+#
+# print(s)
 
 
 

LeQua2022/main_binary_vector.py

@@ -31,8 +31,6 @@ dev_prev = pd.read_csv(os.path.join(path_binary_vector, 'public', 'dev_prevalenc
 print(dev_prev)
 
 
-
-
 scores = {}
 for quantifier in [CC]: #, ACC, PCC, PACC, EMQ, HDy]:
 

quapy/data/base.py

@@ -1,3 +1,4 @@
+from abc import abstractmethod
 from typing import List, Union
 
 import numpy as np
@@ -8,6 +9,112 @@ from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold
 from quapy.functional import artificial_prevalence_sampling, strprev
 
 
+# class Sampling:
+#
+#     @abstractmethod
+#     def load(cls, path: str, loader_func: callable, classes=None): ...
+#
+#     @abstractmethod
+#     @property
+#     def __len__(self): ...
+#
+#     @abstractmethod
+#     @property
+#     def prevalence(self): ...
+#
+#     @abstractmethod
+#     @property
+#     def n_classes(self):
+#
+#     @property
+#     def binary(self):
+#         return self.n_classes == 2
+#
+#     def uniform_sampling_index(self, size):
+#         return np.random.choice(len(self), size, replace=False)
+#
+#     def uniform_sampling(self, size):
+#         unif_index = self.uniform_sampling_index(size)
+#         return self.sampling_from_index(unif_index)
+#
+#     def sampling(self, size, *prevs, shuffle=True):
+#         prev_index = self.sampling_index(size, *prevs, shuffle=shuffle)
+#         return self.sampling_from_index(prev_index)
+#
+#     def sampling_from_index(self, index):
+#         documents = self.instances[index]
+#         labels = self.labels[index]
+#         return LabelledCollection(documents, labels, classes_=self.classes_)
+#
+#     def split_stratified(self, train_prop=0.6, random_state=None):
+#         # with temp_seed(42):
+#         tr_docs, te_docs, tr_labels, te_labels = \
+#             train_test_split(self.instances, self.labels, train_size=train_prop, stratify=self.labels,
+#                              random_state=random_state)
+#         return LabelledCollection(tr_docs, tr_labels), LabelledCollection(te_docs, te_labels)
+#
+#     def artificial_sampling_generator(self, sample_size, n_prevalences=101, repeats=1):
+#         dimensions = self.n_classes
+#         for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
+#             yield self.sampling(sample_size, *prevs)
+#
+#     def artificial_sampling_index_generator(self, sample_size, n_prevalences=101, repeats=1):
+#         dimensions = self.n_classes
+#         for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
+#             yield self.sampling_index(sample_size, *prevs)
+#
+#     def natural_sampling_generator(self, sample_size, repeats=100):
+#         for _ in range(repeats):
+#             yield self.uniform_sampling(sample_size)
+#
+#     def natural_sampling_index_generator(self, sample_size, repeats=100):
+#         for _ in range(repeats):
+#             yield self.uniform_sampling_index(sample_size)
+#
+#     def __add__(self, other):
+#         if other is None:
+#             return self
+#         elif issparse(self.instances) and issparse(other.instances):
+#             join_instances = vstack([self.instances, other.instances])
+#         elif isinstance(self.instances, list) and isinstance(other.instances, list):
+#             join_instances = self.instances + other.instances
+#         elif isinstance(self.instances, np.ndarray) and isinstance(other.instances, np.ndarray):
+#             join_instances = np.concatenate([self.instances, other.instances])
+#         else:
+#             raise NotImplementedError('unsupported operation for collection types')
+#         labels = np.concatenate([self.labels, other.labels])
+#         return LabelledCollection(join_instances, labels)
+#
+#     @property
+#     def Xy(self):
+#         return self.instances, self.labels
+#
+#     def stats(self, show=True):
+#         ninstances = len(self)
+#         instance_type = type(self.instances[0])
+#         if instance_type == list:
+#             nfeats = len(self.instances[0])
+#         elif instance_type == np.ndarray or issparse(self.instances):
+#             nfeats = self.instances.shape[1]
+#         else:
+#             nfeats = '?'
+#         stats_ = {'instances': ninstances,
+#                   'type': instance_type,
+#                   'features': nfeats,
+#                   'classes': self.classes_,
+#                   'prevs': strprev(self.prevalence())}
+#         if show:
+#             print(f'#instances={stats_["instances"]}, type={stats_["type"]}, #features={stats_["features"]}, '
+#                   f'#classes={stats_["classes"]}, prevs={stats_["prevs"]}')
+#         return stats_
+#
+#     def kFCV(self, nfolds=5, nrepeats=1, random_state=0):
+#         kf = RepeatedStratifiedKFold(n_splits=nfolds, n_repeats=nrepeats, random_state=random_state)
+#         for train_index, test_index in kf.split(*self.Xy):
+#             train = self.sampling_from_index(train_index)
+#             test = self.sampling_from_index(test_index)
+#             yield train, test
+
 class LabelledCollection:
     '''
     A LabelledCollection is a set of objects each with a label associated to it.
@@ -176,104 +283,7 @@ class LabelledCollection:
             yield train, test
 
 
-class MultilingualLabelledCollection:
-    def __init__(self, langs:List[str], labelledCollections:List[LabelledCollection]):
-        assert len(langs) == len(labelledCollections), 'length mismatch for langs and labelledCollection lists'
-        assert all(isinstance(lc, LabelledCollection) for lc in labelledCollections), 'unexpected type for labelledCollections'
-        assert all(labelledCollections[0].classes_ == lc_i.classes_ for lc_i in labelledCollections[1:]), \
-            'inconsistent classes found for some labelled collections'
-        self.llc = {l: lc for l, lc in zip(langs, labelledCollections)}
-        self.classes_=labelledCollections[0].classes_
 
-    @classmethod
-    def fromLangDict(cls, lang_labelledCollection:dict):
-        return MultilingualLabelledCollection(*list(zip(*list(lang_labelledCollection.items()))))
-
-    def langs(self):
-        return list(sorted(self.llc.keys()))
-
-    def __getitem__(self, lang)->LabelledCollection:
-        return self.llc[lang]
-
-    @classmethod
-    def load(cls, path: str, loader_func: callable):
-        return MultilingualLabelledCollection(*loader_func(path))
-
-    def __len__(self):
-        return sum(map(len, self.llc.values()))
-
-    def prevalence(self):
-        prev = np.asarray([lc.prevalence() * len(lc) for lc in self.llc.values()]).sum(axis=0)
-        return prev / prev.sum()
-
-    def language_prevalence(self):
-        lang_count = np.asarray([len(self.llc[l]) for l in self.langs()])
-        return lang_count / lang_count.sum()
-
-    def counts(self):
-        return np.asarray([lc.counts() for lc in self.llc.values()]).sum(axis=0)
-
-    @property
-    def n_classes(self):
-        return len(self.classes_)
-
-    @property
-    def binary(self):
-        return self.n_classes == 2
-
-    def __check_langs(self, l_dict:dict):
-        assert len(l_dict)==len(self.langs()), 'wrong number of languages'
-        assert all(l in l_dict for l in self.langs()), 'missing languages in l_sizes'
-
-    def __check_sizes(self, l_sizes: Union[int,dict]):
-        assert isinstance(l_sizes, int) or isinstance(l_sizes, dict), 'unexpected type for l_sizes'
-        if isinstance(l_sizes, int):
-            return {l:l_sizes for l in self.langs()}
-        self.__check_langs(l_sizes)
-        return l_sizes
-
-    def sampling_index(self, l_sizes: Union[int,dict], *prevs, shuffle=True):
-        l_sizes = self.__check_sizes(l_sizes)
-        return {l:lc.sampling_index(l_sizes[l], *prevs, shuffle=shuffle) for l,lc in self.llc.items()}
-
-    def uniform_sampling_index(self, l_sizes: Union[int, dict]):
-        l_sizes = self.__check_sizes(l_sizes)
-        return {l: lc.uniform_sampling_index(l_sizes[l]) for l,lc in self.llc.items()}
-
-    def uniform_sampling(self, l_sizes: Union[int, dict]):
-        l_sizes = self.__check_sizes(l_sizes)
-        return MultilingualLabelledCollection.fromLangDict(
-            {l: lc.uniform_sampling(l_sizes[l]) for l,lc in self.llc.items()}
-        )
-
-    def sampling(self, l_sizes: Union[int, dict], *prevs, shuffle=True):
-        l_sizes = self.__check_sizes(l_sizes)
-        return MultilingualLabelledCollection.fromLangDict(
-            {l: lc.sampling(l_sizes[l], *prevs, shuffle=shuffle) for l,lc in self.llc.items()}
-        )
-
-    def sampling_from_index(self, l_index:dict):
-        self.__check_langs(l_index)
-        return MultilingualLabelledCollection.fromLangDict(
-            {l: lc.sampling_from_index(l_index[l]) for l,lc in self.llc.items()}
-        )
-
-    def split_stratified(self, train_prop=0.6, random_state=None):
-        train, test = list(zip(*[self[l].split_stratified(train_prop, random_state) for l in self.langs()]))
-        return MultilingualLabelledCollection(self.langs(), train), MultilingualLabelledCollection(self.langs(), test)
-
-    def asLabelledCollection(self, return_langs=False):
-        lXy_list = [([l]*len(lc),*lc.Xy) for l, lc in self.llc.items()]  # a list with (lang_i, Xi, yi)
-        ls,Xs,ys = list(zip(*lXy_list))
-        ls = np.concatenate(ls)
-        vertstack = vstack if issparse(Xs[0]) else np.vstack
-        Xs = vertstack(Xs)
-        ys = np.concatenate(ys)
-        lc = LabelledCollection(Xs, ys, classes_=self.classes_)
-        # return lc, ls if return_langs else lc
-#
-#
-#
 class Dataset:
 
     def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None, name=''):