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lapros data for better AI

Project description

LaPros

Install

pip install -U lapros

How to use

LaPros works with classifiers. It ranks the suspicious labels given probabilies by some classification model. You can use normal Python lists, Numpy arrays or Pandas data. Return values are in a Numpy array or a Pandas series, the larger the value, the more suspicious are the coresponding labels.

assert lapros.__version__ == '0.3'
from lapros import suspect
labels = pd.Series(["cat", "dog", "dog", "cat", "cat"])
0    cat
1    dog
2    dog
3    cat
4    cat
dtype: object
probas = pd.DataFrame(dict(
    cat=[0.5, 0.4, 0.3, 0.2, 0.1],
    dog=[0.5, 0.6, 0.7, 0.8, 0.9],
))
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cat dog
0 0.5 0.5
1 0.4 0.6
2 0.3 0.7
3 0.2 0.8
4 0.1 0.9
suspect(
    probas,
    labels=labels,
)
lapros.classification.estimate_noise.avg_confidence:36 [0.26666667 0.65      ]
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err suspected
0 0.000000 False
1 0.183333 True
2 0.000000 False
3 0.216667 True
4 0.416667 True
residual = suspect(
    probas,
    labels=labels,
    rank_method="residual",
    return_non_errors=False,
)
lapros.classification.estimate_noise.avg_confidence:36 [0.26666667 0.65      ]
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err
1 0.4
3 0.8
4 0.9
set_logger("INFO")
confidence = suspect(
    probas,
    labels=labels,
    rank_method="confidence",
    return_non_errors=False,
)
lapros.classification.estimate_noise.avg_confidence:36 [0.26666667 0.65      ]
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err
id
1 0.183333
3 0.216667
4 0.416667
probas.assign(labels=labels, residual=residual, confidence=confidence)
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cat dog labels residual confidence
0 0.5 0.5 cat NaN NaN
1 0.4 0.6 dog 0.4 0.183333
2 0.3 0.7 dog NaN NaN
3 0.2 0.8 cat 0.8 0.216667
4 0.1 0.9 cat 0.9 0.416667

docstring


suspect

Rank the suspicious labels given probas from a classifier. Accept Numpy arrays, Pandas dataframes and series. We can use interger, string or even float labels, given that the probability matrix’s columns are indexed by the same label set.

Args

  • probas (n x m matrix): probabilites for possible classes.

KwArgs

  • labels (n x 1 vector): observed class labels
  • rank_method (str): residual or confidence
  • return_non_errors (bool, default = True): return all rows, including non-errors

Returns

a Pandas DataFrame including 1 index and 2 columns:

  • id (int): the index which is the same to the original data row index
  • err (float): the magnitude of suspiciousness, valued between [0, 1]
  • suspected (bool): whether the data row is suspected as having a label error. This collum is returned iff return_non_errors=True.
help(suspect)
Help on function suspect in module lapros.api:

suspect(...)
    Rank the suspicious labels given probas from a classifier.
    Accept Numpy arrays, Pandas dataframes and series.
    We can use interger, string or even float labels, given that
    the probability matrix's columns are indexed by the same label set.
    
    #### Args
    
    - probas (n x m matrix): probabilites for possible classes.
    
    #### KwArgs
    
    - labels (n x 1 vector): observed class labels
    - rank_method (str): `residual` or `confidence`
    - return_non_errors (bool, default = True): return all rows, including non-errors
    
    #### Returns
    
    a Pandas DataFrame including 1 index and 2 columns:
    
    - id (int): the index which is the same to the original data row index
    - err (float): the magnitude of suspiciousness, valued between [0, 1]
    - suspected (bool):  whether the data row is suspected as having a label error. This collum is returned iff return_non_errors=True.

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