fix: invalid escape sequence in string (#138)

Nixtla · Nov 26, 2024 · 05d7947 · 05d7947
1 parent 4bb140b
commit 05d7947
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Showing 4 changed files with 4 additions and 4 deletions.
diff --git a/nbs/losses.ipynb b/nbs/losses.ipynb
@@ -1900,7 +1900,7 @@
     "    difference between predicted quantiles and observed values.\n",
     "\n",
     "    The limit behavior of MQL allows to measure the accuracy \n",
-    "    of a full predictive distribution $\\mathbf{\\hat{F}}_{\\\\tau}$ with \n",
+    "    of a full predictive distribution with \n",
     "    the continuous ranked probability score (CRPS). This can be achieved \n",
     "    through a numerical integration technique, that discretizes the quantiles \n",
     "    and treats the CRPS integral with a left Riemann approximation, averaging over \n",

diff --git a/nbs/plotting.ipynb b/nbs/plotting.ipynb
@@ -81,7 +81,7 @@
     "):\n",
     "    out_cols = [id_col, time_col]\n",
     "    if models is not None:\n",
-    "        models_pat = r'|'.join(models).replace('(', '\\(').replace(')', '\\)')\n",
+    "        models_pat = r'|'.join(models).replace(r'(', r'\\(').replace(r')', r'\\)')\n",
     "        interval_cols = [c for c in df.columns if re.search(rf\"^({models_pat})-(?:lo|hi)-\\d+\", c)]\n",
     "        out_cols.extend(models + interval_cols)\n",
     "    mask = ufp.is_in(df[id_col], uids)\n",

diff --git a/utilsforecast/losses.py b/utilsforecast/losses.py
@@ -545,7 +545,7 @@ def mqloss(
     difference between predicted quantiles and observed values.
 
     The limit behavior of MQL allows to measure the accuracy
-    of a full predictive distribution $\mathbf{\hat{F}}_{\\tau}$ with
+    of a full predictive distribution with
     the continuous ranked probability score (CRPS). This can be achieved
     through a numerical integration technique, that discretizes the quantiles
     and treats the CRPS integral with a left Riemann approximation, averaging over

diff --git a/utilsforecast/plotting.py b/utilsforecast/plotting.py
@@ -31,7 +31,7 @@
 def _filter_series(df, id_col, time_col, uids, models=None, max_insample_length=None):
     out_cols = [id_col, time_col]
     if models is not None:
-        models_pat = r"|".join(models).replace("(", "\(").replace(")", "\)")
+        models_pat = r"|".join(models).replace(r"(", r"\(").replace(r")", r"\)")
         interval_cols = [
             c for c in df.columns if re.search(rf"^({models_pat})-(?:lo|hi)-\d+", c)
         ]