update theory overview (#55)

* update theory overview

* ops: update pylint rules

---------

Co-authored-by: M-Mouhcine <[email protected]>

.pylintrc

@@ -10,7 +10,10 @@ disable=
     C0302, # allow too many lines in module
     C0411, # allow custom import order
 
+    E0606, # allow false positive used-before-assignment
+
     R0801, # allow similar lines in 2 files
+    R0915, # allow too many statements
 
     W0105, # allow no effect string statement
     W0102, # allow dangerous default value []

deel/puncc/api/nonconformity_scores.py

@@ -24,7 +24,7 @@
 This module provides nonconformity scores for conformal prediction. To be used
 when building a :ref:`calibrator <calibration>`.
 """
-import pkgutil
+import importlib
 from typing import Callable
 from typing import Iterable
 
@@ -33,13 +33,13 @@
 from deel.puncc.api.utils import logit_normalization_check
 from deel.puncc.api.utils import supported_types_check
 
-if pkgutil.find_loader("pandas") is not None:
+if importlib.util.find_spec("pandas") is not None:
     import pandas as pd
 
-if pkgutil.find_loader("tensorflow") is not None:
+if importlib.util.find_spec("tensorflow") is not None:
     import tensorflow as tf
 
-if pkgutil.find_loader("torch") is not None:
+if importlib.util.find_spec("torch") is not None:
     import torch
 
 
@@ -176,7 +176,7 @@ def difference(y_pred: Iterable, y_true: Iterable) -> Iterable:
     """
     supported_types_check(y_pred, y_true)
 
-    if pkgutil.find_loader("torch") is not None and isinstance(
+    if importlib.util.find_spec("torch") is not None and isinstance(
         y_pred, torch.Tensor
     ):
         y_pred = y_pred.cpu().detach().numpy()
@@ -205,7 +205,9 @@ def absolute_difference(y_pred: Iterable, y_true: Iterable) -> Iterable:
     return abs(difference(y_pred, y_true))
 
 
-def scaled_ad(Y_pred: Iterable, y_true: Iterable, eps: float = 1e-12) -> Iterable:
+def scaled_ad(
+    Y_pred: Iterable, y_true: Iterable, eps: float = 1e-12
+) -> Iterable:
     """Scaled Absolute Deviation, normalized by an estimation of the conditional
     mean absolute deviation (conditional MAD). Considering
     :math:`Y_{\\text{pred}} = (\mu_{\\text{pred}}, \sigma_{\\text{pred}})`:
@@ -235,7 +237,7 @@ def scaled_ad(Y_pred: Iterable, y_true: Iterable, eps: float = 1e-12) -> Iterabl
     if len(y_true.shape) != 1:
         raise RuntimeError("Each y_true must contain a point observation.")
 
-    if pkgutil.find_loader("pandas") is not None and isinstance(
+    if importlib.util.find_spec("pandas") is not None and isinstance(
         Y_pred, pd.DataFrame
     ):
         y_pred, sigma_pred = Y_pred.iloc[:, 0], Y_pred.iloc[:, 1]
@@ -245,8 +247,10 @@ def scaled_ad(Y_pred: Iterable, y_true: Iterable, eps: float = 1e-12) -> Iterabl
     # MAD then Scaled MAD and computed
     mean_absolute_deviation = absolute_difference(y_pred, y_true)
     if np.any(sigma_pred + eps <= 0):
-        print("Warning: calibration points with MAD predictions"
-              " below -eps won't be used for calibration.")
+        print(
+            "Warning: calibration points with MAD predictions"
+            " below -eps won't be used for calibration."
+        )
 
     nonneg = sigma_pred + eps > 0
     return mean_absolute_deviation[nonneg] / (sigma_pred[nonneg] + eps)
@@ -282,7 +286,7 @@ def cqr_score(Y_pred: Iterable, y_true: Iterable) -> Iterable:
     if len(y_true.shape) != 1:
         raise RuntimeError("Each y_pred must contain a point observation.")
 
-    if pkgutil.find_loader("pandas") is not None and isinstance(
+    if importlib.util.find_spec("pandas") is not None and isinstance(
         Y_pred, pd.DataFrame
     ):
         q_lo, q_hi = Y_pred.iloc[:, 0], Y_pred.iloc[:, 1]
@@ -295,20 +299,20 @@ def cqr_score(Y_pred: Iterable, y_true: Iterable) -> Iterable:
     if isinstance(diff_lo, np.ndarray):
         return np.maximum(diff_lo, diff_hi)
 
-    if pkgutil.find_loader("pandas") is not None and isinstance(
+    if importlib.util.find_spec("pandas") is not None and isinstance(
         diff_lo, (pd.DataFrame, pd.Series)
     ):
         return (pd.concat([diff_lo, diff_hi]).groupby(level=0)).max()
         # raise NotImplementedError(
         #     "CQR score not implemented for DataFrames. Please provide ndarray or tensors."
         # )
 
-    if pkgutil.find_loader("tensorflow") is not None and isinstance(
+    if importlib.util.find_spec("tensorflow") is not None and isinstance(
         diff_lo, tf.Tensor
     ):
         return tf.math.maximum(diff_lo, diff_hi)
 
-    # if pkgutil.find_loader("torch") is not None and isinstance(
+    # if importlib.util.find_spec("torch") is not None and isinstance(
     #     diff_lo, torch.Tensor
     # ):
     #     return torch.maximum(diff_lo, diff_hi)

deel/puncc/api/splitting.py

@@ -23,7 +23,7 @@
 """
 This module provides data splitting schemes.
 """
-import pkgutil
+import importlib
 from abc import ABC
 from typing import Iterable
 from typing import List
@@ -36,7 +36,7 @@
 from deel.puncc.api.utils import sample_len_check
 from deel.puncc.api.utils import supported_types_check
 
-if pkgutil.find_loader("pandas") is not None:
+if importlib.util.find_spec("pandas") is not None:
     import pandas as pd
 
 
@@ -171,7 +171,7 @@ def __call__(
         folds = []
 
         for fit, calib in kfold.split(X):
-            if pkgutil.find_loader("pandas") is not None and isinstance(
+            if importlib.util.find_spec("pandas") is not None and isinstance(
                 X, pd.DataFrame
             ):
                 if isinstance(y, pd.DataFrame):

deel/puncc/api/utils.py

@@ -24,7 +24,7 @@
 This module implements utility functions.
 """
 import logging
-import pkgutil
+import importlib
 import sys
 from typing import Any
 from typing import Iterable
@@ -34,13 +34,13 @@
 
 import numpy as np
 
-if pkgutil.find_loader("pandas") is not None:
+if importlib.util.find_spec("pandas") is not None:
     import pandas as pd
 
-if pkgutil.find_loader("tensorflow") is not None:
+if importlib.util.find_spec("tensorflow") is not None:
     import tensorflow as tf
 
-if pkgutil.find_loader("torch") is not None:
+if importlib.util.find_spec("torch") is not None:
     import torch
 
 logger = logging.getLogger(__name__)
@@ -126,15 +126,15 @@ def supported_types_check(*data: Iterable):
         if isinstance(a, np.ndarray):
             pass
 
-        elif pkgutil.find_loader("pandas") is not None and isinstance(
+        elif importlib.util.find_spec("pandas") is not None and isinstance(
             a, (pd.DataFrame, pd.Series)
         ):
             pass
-        elif pkgutil.find_loader("tensorflow") is not None and isinstance(
+        elif importlib.util.find_spec("tensorflow") is not None and isinstance(
             a, tf.Tensor
         ):
             pass
-        elif pkgutil.find_loader("torch") is not None and isinstance(
+        elif importlib.util.find_spec("torch") is not None and isinstance(
             a, torch.Tensor
         ):
             pass
@@ -277,15 +277,15 @@ def quantile(
 
     if isinstance(a, np.ndarray):
         pass
-    elif pkgutil.find_loader("pandas") is not None and isinstance(
+    elif importlib.util.find_spec("pandas") is not None and isinstance(
         a, pd.DataFrame
     ):
         a = a.to_numpy()
-    elif pkgutil.find_loader("tensorflow") is not None and isinstance(
+    elif importlib.util.find_spec("tensorflow") is not None and isinstance(
         a, tf.Tensor
     ):
         a = a.numpy()
-    # elif pkgutil.find_loader("torch") is not None:
+    # elif importlib.util.find_spec("torch") is not None:
     #     if isinstance(a, torch.Tensor):
     #         a = a.cpu().detach().numpy()
     else:

deel/puncc/plotting.py

@@ -155,6 +155,9 @@ def plot_prediction_intervals(
 
     """
 
+    # Initialisation
+    current_rcparams = None
+
     # Figure size configuration
     if "figsize" in fig_kw.keys():
         figsize = fig_kw["figsize"]
@@ -256,7 +259,7 @@ def plot_prediction_intervals(
     ax.set_xlim(X[0] - int_size * 0.01, X[-1] + int_size * 0.01)
 
     # restablish rcparams
-    if restablish_rcparams:
+    if current_rcparams is not None and restablish_rcparams:
         matplotlib.rcParams.update(current_rcparams)
 
     return ax