joaopfonseca / ml-research Goto Github PK

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt, os
from mlresearch.utils import set_matplotlib_style
import warnings
warnings.filterwarnings("ignore")

set_matplotlib_style()
%config InlineBackend.figure_format = 'retina' 

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
Input In [2], in <cell line: 8>()
      6 import numpy as np
      7 import matplotlib.pyplot as plt, os
----> 8 from mlresearch.utils import set_matplotlib_style
      9 import warnings
     10 warnings.filterwarnings("ignore")

File ~/opt/anaconda3/lib/python3.9/site-packages/mlresearch/_init_.py:45, in <module>
     41     sys.stderr.write("Partial import of imblearn during the build process.\n")
     42     # We are not importing the rest of scikit-learn during the build
     43     # process, as it may not be compiled yet
     44 else:
---> 45     from . import active_learning
     46     from . import data_augmentation
     47     from . import datasets

File ~/opt/anaconda3/lib/python3.9/site-packages/mlresearch/active_learning/_init_.py:4, in <module>
      1 """
      2 Module which contains Active Learning implementations.
      3 """
----> 4 from ._active_learning import StandardAL, AugmentationAL
      5 from ._acquisition_functions import ACQUISITION_FUNCTIONS
      7 _all_ = ["StandardAL", "AugmentationAL", "ACQUISITION_FUNCTIONS"]

File ~/opt/anaconda3/lib/python3.9/site-packages/mlresearch/active_learning/_active_learning.py:5, in <module>
      3 import numpy as np
      4 from sklearn.base import ClassifierMixin, BaseEstimator, clone
----> 5 from sklearn.model_selection import GridSearchCV
      6 from imblearn.pipeline import Pipeline
      7 from imblearn.over_sampling.base import BaseOverSampler

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_init_.py:23, in <module>
     20 from ._split import train_test_split
     21 from ._split import check_cv
---> 23 from ._validation import cross_val_score
     24 from ._validation import cross_val_predict
     25 from ._validation import cross_validate

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:32, in <module>
     30 from ..utils.fixes import delayed
     31 from ..utils.metaestimators import _safe_split
---> 32 from ..metrics import check_scoring
     33 from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer
     34 from ..exceptions import FitFailedWarning

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_init_.py:41, in <module>
     37 from ._classification import multilabel_confusion_matrix
     39 from ._dist_metrics import DistanceMetric
---> 41 from . import cluster
     42 from .cluster import adjusted_mutual_info_score
     43 from .cluster import adjusted_rand_score

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/cluster/_init_.py:22, in <module>
     20 from ._supervised import fowlkes_mallows_score
     21 from ._supervised import entropy
---> 22 from ._unsupervised import silhouette_samples
     23 from ._unsupervised import silhouette_score
     24 from ._unsupervised import calinski_harabasz_score

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/cluster/_unsupervised.py:16, in <module>
     14 from ...utils import check_X_y
     15 from ...utils import _safe_indexing
---> 16 from ..pairwise import pairwise_distances_chunked
     17 from ..pairwise import pairwise_distances
     18 from ...preprocessing import LabelEncoder

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py:33, in <module>
     30 from ..utils.fixes import delayed
     31 from ..utils.fixes import sp_version, parse_version
---> 33 from ._pairwise_distances_reduction import PairwiseDistancesArgKmin
     34 from ._pairwise_fast import _chi2_kernel_fast, _sparse_manhattan
     35 from ..exceptions import DataConversionWarning

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise_distances_reduction/init_.py:89, in <module>
      1 # Pairwise Distances Reductions
      2 # =============================
      3 #
   (...)
     85 #    using Generalized Matrix Multiplication over `float64` data (see the
     86 #    docstring of :class:`GEMMTermComputer64` for details).
---> 89 from ._dispatcher import (
     90     ArgKmin,
     91     ArgKminClassMode,
     92     BaseDistancesReductionDispatcher,
     93     RadiusNeighbors,
     94     sqeuclidean_row_norms,
     95 )
     97 _all_ = [
     98     "BaseDistancesReductionDispatcher",
     99     "ArgKmin",
   (...)
    102     "sqeuclidean_row_norms",
    103 ]

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:8, in <module>
      5 from scipy.sparse import issparse
      7 from ... import get_config
----> 8 from .._dist_metrics import BOOL_METRICS, METRIC_MAPPING64
      9 from ._argkmin import (
     10     ArgKmin32,
     11     ArgKmin64,
     12 )
     13 from ._argkmin_classmode import (
     14     ArgKminClassMode32,
     15     ArgKminClassMode64,
     16 )

ImportError: cannot import name 'METRIC_MAPPING64' from 'sklearn.metrics._dist_metrics' (/Users/josefonseca/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_dist_metrics.cpython-39-darwin.so)

from mlresearch.metrics import Authenticity

Authenticity()

Out[1]: make_scorer(Authenticity, metric=euclidean, n_jobs=None)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File ~/miniconda3/envs/mlresearch/lib/python3.12/site-packages/IPython/core/formatters.py:711, in PlainTextFormatter.__call__(self, obj)
   704 stream = StringIO()
   705 printer = pretty.RepresentationPrinter(stream, self.verbose,
   706     self.max_width, self.newline,
   707     max_seq_length=self.max_seq_length,
   708     singleton_pprinters=self.singleton_printers,
   709     type_pprinters=self.type_printers,
   710     deferred_pprinters=self.deferred_printers)
--> 711 printer.pretty(obj)
   712 printer.flush()
   713 return stream.getvalue()

File ~/miniconda3/envs/mlresearch/lib/python3.12/site-packages/IPython/lib/pretty.py:411, in RepresentationPrinter.pretty(self, obj)
   408                         return meth(obj, self, cycle)
   409                 if cls is not object \
   410                         and callable(cls.__dict__.get('__repr__')):
--> 411                     return _repr_pprint(obj, self, cycle)
   413     return _default_pprint(obj, self, cycle)
   414 finally:

File ~/miniconda3/envs/mlresearch/lib/python3.12/site-packages/IPython/lib/pretty.py:779, in _repr_pprint(obj, p, cycle)
   777 """A pprint that just redirects to the normal repr function."""
   778 # Find newlines and replace them with p.break_()
--> 779 output = repr(obj)
   780 lines = output.splitlines()
   781 with p.group():

File ~/miniconda3/envs/mlresearch/lib/python3.12/site-packages/sklearn/metrics/_scorer.py:206, in _BaseScorer.__repr__(self)
   205 def __repr__(self):
--> 206     sign_string = "" if self._sign > 0 else ", greater_is_better=False"
   207     response_method_string = f", response_method={self._response_method!r}"
   208     kwargs_string = "".join([f", {k}={v}" for k, v in self._kwargs.items()])

AttributeError: 'Authenticity' object has no attribute '_sign'

import numpy as np
import pandas as pd
from mlresearch.latex import make_mean_sem_table

means = pd.DataFrame(np.random.random((3,3)))
sem = pd.DataFrame(np.random.random((3,3)))
make_mean_sem_table(means, sem)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[67], line 7
      5 means = pd.DataFrame(np.random.random((3,3)))
      6 sem = pd.DataFrame(np.random.random((3,3)))
----> 7 make_mean_sem_table(means, sem)

File ~/miniconda3/envs/recourse-game/lib/python3.10/site-packages/mlresearch/latex/_utils.py:293, in make_mean_sem_table(mean_vals, sem_vals, make_bold, maximum, threshold, decimals, axis)
    287     if type(sem_vals) is np.ndarray:
    288         sem_vals = pd.DataFrame(
    289             sem_vals, index=mean_vals.index, columns=mean_vals.columns
    290         )
    292     scores = (
--> 293         mean_vals.map(("{:,.%sf}" % decimals).format)
    294         + r" $\pm$ "
    295         + sem_vals.map(("{:,.%sf}" % decimals).format)
    296     )
    297 else:
    298     scores = mean_vals.map(("{:,.%sf}" % decimals).format)

File ~/miniconda3/envs/recourse-game/lib/python3.10/site-packages/pandas/core/generic.py:5902, in NDFrame.__getattr__(self, name)
   5895 if (
   5896     name not in self._internal_names_set
   5897     and name not in self._metadata
   5898     and name not in self._accessors
   5899     and self._info_axis._can_hold_identifiers_and_holds_name(name)
   5900 ):
   5901     return self[name]
-> 5902 return object.__getattribute__(self, name)

AttributeError: 'DataFrame' object has no attribute 'map'