data reader for lequa 2022 competition
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from typing import Tuple, Union
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import pandas as pd
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import numpy as np
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import os
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from quapy.protocol import AbstractProtocol
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DEV_SAMPLES = 1000
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TEST_SAMPLES = 5000
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ERROR_TOL = 1E-3
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def load_category_map(path):
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cat2code = {}
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with open(path, 'rt') as fin:
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for line in fin:
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category, code = line.split()
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cat2code[category] = int(code)
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code2cat = [cat for cat, code in sorted(cat2code.items(), key=lambda x: x[1])]
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return cat2code, code2cat
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def load_raw_documents(path):
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df = pd.read_csv(path)
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documents = list(df["text"].values)
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labels = None
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if "label" in df.columns:
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labels = df["label"].values.astype(np.int)
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return documents, labels
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def load_vector_documents(path):
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D = pd.read_csv(path).to_numpy(dtype=np.float)
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labelled = D.shape[1] == 301
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if labelled:
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X, y = D[:, 1:], D[:, 0].astype(np.int).flatten()
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else:
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X, y = D, None
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return X, y
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class SamplesFromDir(AbstractProtocol):
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def __init__(self, path_dir:str, ground_truth_path:str, load_fn):
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self.path_dir = path_dir
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self.load_fn = load_fn
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self.true_prevs = ResultSubmission.load(ground_truth_path)
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def __call__(self):
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for id, prevalence in self.true_prevs.iterrows():
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sample, _ = self.load_fn(os.path.join(self.path_dir, f'{id}.txt'))
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yield sample, prevalence
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class ResultSubmission:
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def __init__(self):
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self.df = None
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def __init_df(self, categories: int):
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if not isinstance(categories, int) or categories < 2:
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raise TypeError('wrong format for categories: an int (>=2) was expected')
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df = pd.DataFrame(columns=list(range(categories)))
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df.index.set_names('id', inplace=True)
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self.df = df
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@property
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def n_categories(self):
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return len(self.df.columns.values)
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def add(self, sample_id: int, prevalence_values: np.ndarray):
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if not isinstance(sample_id, int):
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raise TypeError(f'error: expected int for sample_sample, found {type(sample_id)}')
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if not isinstance(prevalence_values, np.ndarray):
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raise TypeError(f'error: expected np.ndarray for prevalence_values, found {type(prevalence_values)}')
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if self.df is None:
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self.__init_df(categories=len(prevalence_values))
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if sample_id in self.df.index.values:
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raise ValueError(f'error: prevalence values for "{sample_id}" already added')
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if prevalence_values.ndim != 1 and prevalence_values.size != self.n_categories:
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raise ValueError(f'error: wrong shape found for prevalence vector {prevalence_values}')
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if (prevalence_values < 0).any() or (prevalence_values > 1).any():
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raise ValueError(f'error: prevalence values out of range [0,1] for "{sample_id}"')
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if np.abs(prevalence_values.sum() - 1) > ERROR_TOL:
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raise ValueError(f'error: prevalence values do not sum up to one for "{sample_id}"'
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f'(error tolerance {ERROR_TOL})')
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self.df.loc[sample_id] = prevalence_values
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def __len__(self):
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return len(self.df)
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@classmethod
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def load(cls, path: str) -> 'ResultSubmission':
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df = ResultSubmission.check_file_format(path)
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r = ResultSubmission()
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r.df = df
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return r
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def dump(self, path: str):
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ResultSubmission.check_dataframe_format(self.df)
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self.df.to_csv(path)
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def prevalence(self, sample_id: int):
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sel = self.df.loc[sample_id]
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if sel.empty:
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return None
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else:
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return sel.values.flatten()
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def iterrows(self):
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for index, row in self.df.iterrows():
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prevalence = row.values.flatten()
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yield index, prevalence
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@classmethod
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def check_file_format(cls, path) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
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try:
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df = pd.read_csv(path, index_col=0)
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except Exception as e:
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print(f'the file {path} does not seem to be a valid csv file. ')
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print(e)
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return ResultSubmission.check_dataframe_format(df, path=path)
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@classmethod
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def check_dataframe_format(cls, df, path=None) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
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hint_path = '' # if given, show the data path in the error message
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if path is not None:
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hint_path = f' in {path}'
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if df.index.name != 'id' or len(df.columns) < 2:
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raise ValueError(f'wrong header{hint_path}, '
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f'the format of the header should be "id,0,...,n-1", '
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f'where n is the number of categories')
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if [int(ci) for ci in df.columns.values] != list(range(len(df.columns))):
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raise ValueError(f'wrong header{hint_path}, category ids should be 0,1,2,...,n-1, '
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f'where n is the number of categories')
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if df.empty:
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raise ValueError(f'error{hint_path}: results file is empty')
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elif len(df) != DEV_SAMPLES and len(df) != TEST_SAMPLES:
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raise ValueError(f'wrong number of prevalence values found{hint_path}; '
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f'expected {DEV_SAMPLES} for development sets and '
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f'{TEST_SAMPLES} for test sets; found {len(df)}')
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ids = set(df.index.values)
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expected_ids = set(range(len(df)))
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if ids != expected_ids:
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missing = expected_ids - ids
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if missing:
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raise ValueError(f'there are {len(missing)} missing ids{hint_path}: {sorted(missing)}')
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unexpected = ids - expected_ids
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if unexpected:
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raise ValueError(f'there are {len(missing)} unexpected ids{hint_path}: {sorted(unexpected)}')
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for category_id in df.columns:
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if (df[category_id] < 0).any() or (df[category_id] > 1).any():
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raise ValueError(f'error{hint_path} column "{category_id}" contains values out of range [0,1]')
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prevs = df.values
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round_errors = np.abs(prevs.sum(axis=-1) - 1.) > ERROR_TOL
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if round_errors.any():
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raise ValueError(f'warning: prevalence values in rows with id {np.where(round_errors)[0].tolist()} '
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f'do not sum up to 1 (error tolerance {ERROR_TOL}), '
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f'probably due to some rounding errors.')
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return df
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