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.

lapros
Release 0.1.3

Release 0.1.3

0.1.4

0.3

0.1.0

0.1.1

0.2.1

0.2.0

0.1.3

0.1.2

0.0.2

0.0.1

Documentation

LaPros

Install

How to use

docstring

suspect

Args

KwArgs

Returns

Stats

Development practices

Releases

Contributors

lapros Release 0.1.3

Release 0.1.3 Toggle Dropdown 0.1.4 0.3 0.1.0 0.1.1 0.2.1 0.2.0 0.1.3 0.1.2 0.0.2 0.0.1

Documentation

LaPros

Install

How to use

docstring

suspect

Args

KwArgs

Returns

Stats

Development practices

Releases

Contributors

lapros
Release 0.1.3

Release 0.1.3

0.1.4

0.3

0.1.0

0.1.1

0.2.1

0.2.0

0.1.3

0.1.2

0.0.2

0.0.1