forked from moreo/QuaPy
170 lines
6.3 KiB
Python
170 lines
6.3 KiB
Python
from typing import Tuple, Union
|
|
import pandas as pd
|
|
import numpy as np
|
|
import os
|
|
|
|
from quapy.protocol import AbstractProtocol
|
|
|
|
DEV_SAMPLES = 1000
|
|
TEST_SAMPLES = 5000
|
|
|
|
ERROR_TOL = 1E-3
|
|
|
|
|
|
def load_category_map(path):
|
|
cat2code = {}
|
|
with open(path, 'rt') as fin:
|
|
for line in fin:
|
|
category, code = line.split()
|
|
cat2code[category] = int(code)
|
|
code2cat = [cat for cat, code in sorted(cat2code.items(), key=lambda x: x[1])]
|
|
return cat2code, code2cat
|
|
|
|
|
|
def load_raw_documents(path):
|
|
df = pd.read_csv(path)
|
|
documents = list(df["text"].values)
|
|
labels = None
|
|
if "label" in df.columns:
|
|
labels = df["label"].values.astype(np.int)
|
|
return documents, labels
|
|
|
|
|
|
def load_vector_documents(path):
|
|
D = pd.read_csv(path).to_numpy(dtype=np.float)
|
|
labelled = D.shape[1] == 301
|
|
if labelled:
|
|
X, y = D[:, 1:], D[:, 0].astype(np.int).flatten()
|
|
else:
|
|
X, y = D, None
|
|
return X, y
|
|
|
|
|
|
class SamplesFromDir(AbstractProtocol):
|
|
|
|
def __init__(self, path_dir:str, ground_truth_path:str, load_fn):
|
|
self.path_dir = path_dir
|
|
self.load_fn = load_fn
|
|
self.true_prevs = ResultSubmission.load(ground_truth_path)
|
|
|
|
def __call__(self):
|
|
for id, prevalence in self.true_prevs.iterrows():
|
|
sample, _ = self.load_fn(os.path.join(self.path_dir, f'{id}.txt'))
|
|
yield sample, prevalence
|
|
|
|
|
|
class ResultSubmission:
|
|
|
|
def __init__(self):
|
|
self.df = None
|
|
|
|
def __init_df(self, categories: int):
|
|
if not isinstance(categories, int) or categories < 2:
|
|
raise TypeError('wrong format for categories: an int (>=2) was expected')
|
|
df = pd.DataFrame(columns=list(range(categories)))
|
|
df.index.set_names('id', inplace=True)
|
|
self.df = df
|
|
|
|
@property
|
|
def n_categories(self):
|
|
return len(self.df.columns.values)
|
|
|
|
def add(self, sample_id: int, prevalence_values: np.ndarray):
|
|
if not isinstance(sample_id, int):
|
|
raise TypeError(f'error: expected int for sample_sample, found {type(sample_id)}')
|
|
if not isinstance(prevalence_values, np.ndarray):
|
|
raise TypeError(f'error: expected np.ndarray for prevalence_values, found {type(prevalence_values)}')
|
|
if self.df is None:
|
|
self.__init_df(categories=len(prevalence_values))
|
|
if sample_id in self.df.index.values:
|
|
raise ValueError(f'error: prevalence values for "{sample_id}" already added')
|
|
if prevalence_values.ndim != 1 and prevalence_values.size != self.n_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_id}"')
|
|
if np.abs(prevalence_values.sum() - 1) > ERROR_TOL:
|
|
raise ValueError(f'error: prevalence values do not sum up to one for "{sample_id}"'
|
|
f'(error tolerance {ERROR_TOL})')
|
|
|
|
self.df.loc[sample_id] = prevalence_values
|
|
|
|
def __len__(self):
|
|
return len(self.df)
|
|
|
|
@classmethod
|
|
def load(cls, path: str) -> 'ResultSubmission':
|
|
df = ResultSubmission.check_file_format(path)
|
|
r = ResultSubmission()
|
|
r.df = df
|
|
return r
|
|
|
|
def dump(self, path: str):
|
|
ResultSubmission.check_dataframe_format(self.df)
|
|
self.df.to_csv(path)
|
|
|
|
def prevalence(self, sample_id: int):
|
|
sel = self.df.loc[sample_id]
|
|
if sel.empty:
|
|
return None
|
|
else:
|
|
return sel.values.flatten()
|
|
|
|
def iterrows(self):
|
|
for index, row in self.df.iterrows():
|
|
prevalence = row.values.flatten()
|
|
yield index, prevalence
|
|
|
|
@classmethod
|
|
def check_file_format(cls, path) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
|
|
try:
|
|
df = pd.read_csv(path, index_col=0)
|
|
except Exception as e:
|
|
print(f'the file {path} does not seem to be a valid csv file. ')
|
|
print(e)
|
|
return ResultSubmission.check_dataframe_format(df, path=path)
|
|
|
|
@classmethod
|
|
def check_dataframe_format(cls, df, path=None) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
|
|
hint_path = '' # if given, show the data path in the error message
|
|
if path is not None:
|
|
hint_path = f' in {path}'
|
|
|
|
if df.index.name != 'id' or len(df.columns) < 2:
|
|
raise ValueError(f'wrong header{hint_path}, '
|
|
f'the format of the header should be "id,0,...,n-1", '
|
|
f'where n is the number of categories')
|
|
if [int(ci) for ci in df.columns.values] != list(range(len(df.columns))):
|
|
raise ValueError(f'wrong header{hint_path}, category ids should be 0,1,2,...,n-1, '
|
|
f'where n is the number of categories')
|
|
if df.empty:
|
|
raise ValueError(f'error{hint_path}: results file is empty')
|
|
elif len(df) != DEV_SAMPLES and len(df) != TEST_SAMPLES:
|
|
raise ValueError(f'wrong number of prevalence values found{hint_path}; '
|
|
f'expected {DEV_SAMPLES} for development sets and '
|
|
f'{TEST_SAMPLES} for test sets; found {len(df)}')
|
|
|
|
ids = set(df.index.values)
|
|
expected_ids = set(range(len(df)))
|
|
if ids != expected_ids:
|
|
missing = expected_ids - ids
|
|
if missing:
|
|
raise ValueError(f'there are {len(missing)} missing ids{hint_path}: {sorted(missing)}')
|
|
unexpected = ids - expected_ids
|
|
if unexpected:
|
|
raise ValueError(f'there are {len(missing)} unexpected ids{hint_path}: {sorted(unexpected)}')
|
|
|
|
for category_id in df.columns:
|
|
if (df[category_id] < 0).any() or (df[category_id] > 1).any():
|
|
raise ValueError(f'error{hint_path} column "{category_id}" contains values out of range [0,1]')
|
|
|
|
prevs = df.values
|
|
round_errors = np.abs(prevs.sum(axis=-1) - 1.) > 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 {ERROR_TOL}), '
|
|
f'probably due to some rounding errors.')
|
|
|
|
return df
|
|
|
|
|