comments ruben

sam/validation/base_validator.py

@@ -35,8 +35,8 @@ def fit(self, X, y=None):
     def transform(self, X: pd.DataFrame) -> pd.DataFrame:
         """transform method"""
         X = X.copy()
-        invalids = self.validate(X)
-        X[invalids] = np.nan
+        invalid_data = self.validate(X)
+        X[invalid_data] = np.nan
         return X
 
     def get_feature_names_out(self, input_features=None) -> List[str]:

sam/validation/flatline_validator.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import pandas as pd
+from sam.utils import add_future_warning
 from sam.validation import BaseValidator
 
 logger = logging.getLogger(__name__)
@@ -138,29 +139,26 @@ def validate(self, X: pd.DataFrame) -> pd.DataFrame:
         X: pd.DataFrame
             Input dataframe to validate
         """
-        invalids = pd.DataFrame(
+        invalid_data = pd.DataFrame(
             data=np.zeros_like(X.values).astype(bool),
             index=X.index,
             columns=X.columns,
         )
 
         for col in self.cols:
             window = self.window_dict[col]
-            invalids[col] = self._validate_column(X[col], window)
+            invalid_data[col] = self._validate_column(X[col], window)
 
             logger.info(
-                f"detected {np.sum(invalids[col])} "
+                f"detected {np.sum(invalid_data[col])} "
                 f"flatline samples in {col} "
                 f"with window of {window} "
             )
 
-        return invalids
+        return invalid_data
 
 
 class RemoveFlatlines(FlatlineValidator):
+    @add_future_warning("RemoveFlatlines is deprecated, use FlatlineValidator instead")
     def __init__(self, *args, **kwargs):
-        warnings.warn(
-            "RemoveFlatlines is deprecated, use FlatlineValidator instead",
-            DeprecationWarning,
-        )
         super().__init__(*args, **kwargs)

sam/validation/mad_validator.py

@@ -5,6 +5,7 @@
 import numpy as np
 import pandas as pd
 from sam.validation import BaseValidator
+from sam.utils import add_future_warning
 
 logger = logging.getLogger(__name__)
 
@@ -141,7 +142,7 @@ def validate(self, X: pd.DataFrame):
             input data with columns marked as nan
         """
 
-        invalids = pd.DataFrame(
+        invalid_data = pd.DataFrame(
             data=np.zeros_like(X.values).astype(bool),
             index=X.index,
             columns=X.columns,
@@ -161,23 +162,19 @@ def validate(self, X: pd.DataFrame):
 
             # log number of values removed and tresholds used
             logger.info(
-                "detected %d " % np.sum(extreme_value)
-                + "extreme values from %s. " % c
-                + "using upper threshold of: %.2f " % self.thresh_high[c]
-                + "and lower threshold of: %.2f " % self.thresh_low[c]
-                + "using madthresh of %d " % self.madthresh
-                + "and rollingwindow of %s" % str(self.rollingwindow)
+                f"detected {np.sum(extreme_value)} extreme values from {c}. "
+                f"using upper threshold of: {round(self.thresh_high[c], 2)} "
+                f"and lower threshold of: {round(self.thresh_low[c])} "
+                f"using madthresh of {self.madthresh} "
+                f"and rollingwindow of {str(self.rollingwindow)}"
             )
 
-            invalids[c] = extreme_value
+            invalid_data[c] = extreme_value
 
-        return invalids
+        return invalid_data
 
 
 class RemoveExtremeValues(MADValidator):
+    @add_future_warning("RemoveExtremeValues is deprecated. Use MADValidator instead.")
     def __init__(self, *args, **kwargs):
-        warnings.warn(
-            "RemoveExtremeValues is deprecated. Use MADValidator instead.",
-            DeprecationWarning,
-        )
         super().__init__(*args, **kwargs)

sam/validation/outside_range_validator.py

@@ -79,12 +79,14 @@ def validate(self, X):
             Dataframe containing the features to be checked.
         """
 
-        invalids = pd.DataFrame(
+        invalid_data = pd.DataFrame(
             data=np.zeros_like(X.values).astype(bool),
             index=X.index,
             columns=X.columns,
         )
 
-        invalids[self.cols] = X[self.cols].gt(self.max_value_) | X[self.cols].lt(self.min_value_)
+        invalid_data[self.cols] = X[self.cols].gt(self.max_value_) | X[self.cols].lt(
+            self.min_value_
+        )
 
-        return invalids
+        return invalid_data

sam/visualization/diagnostic_flatline_removal.py

@@ -3,7 +3,7 @@
 
 
 def diagnostic_flatline_removal(
-    fv: FlatlineValidator,
+    flatline_validator: FlatlineValidator,
     raw_data: pd.DataFrame,
     col: str,
 ):
@@ -12,7 +12,7 @@ def diagnostic_flatline_removal(
 
     Parameters:
     ----------
-    fv: sam.validation.FlatlineValidator
+    flatline_validator: sam.validation.FlatlineValidator
         fitted FlatlineValidator object
     raw_data: pd.DataFrame
         non-transformed data
@@ -28,7 +28,7 @@ def diagnostic_flatline_removal(
 
     # get data
     x = raw_data[col].copy()
-    invalid_w = fv.validate(raw_data)[col]
+    invalid_w = flatline_validator.validate(raw_data)[col]
     invalid_values = x[invalid_w]
 
     # generate plot

sam/visualization/extreme_removal_plot.py

@@ -3,7 +3,7 @@
 
 
 def diagnostic_extreme_removal(
-    madv: MADValidator,
+    mad_validator: MADValidator,
     raw_data: pd.DataFrame,
     col: str,
 ):
@@ -12,7 +12,7 @@ def diagnostic_extreme_removal(
 
     Parameters:
     ----------
-    rev: sam.validation.MADValidator
+    mad_validator: sam.validation.MADValidator
         fitted MADValidator object
     raw_data: pd.DataFrame
         non-transformed data data
@@ -29,9 +29,9 @@ def diagnostic_extreme_removal(
 
     # get data
     x = raw_data[col].copy()
-    invalid_w = madv.validate(raw_data)[col]
+    invalid_w = mad_validator.validate(raw_data)[col]
     invalid_values = x.loc[invalid_w]
-    rolling = madv._compute_rolling(x)
+    rolling = mad_validator._compute_rolling(x)
     diff = x.values - rolling
 
     # generate plot
@@ -65,8 +65,8 @@ def diagnostic_extreme_removal(
 
     plt.subplot(212)
     plt.plot(diff.values, label="abs(original - rolling)")
-    plt.axhline(madv.thresh_high[col], ls="--", c="r")
-    plt.axhline(madv.thresh_low[col], ls="--", c="r", label="thresholds")
+    plt.axhline(mad_validator.thresh_high[col], ls="--", c="r")
+    plt.axhline(mad_validator.thresh_low[col], ls="--", c="r", label="thresholds")
     plt.legend(loc="best")
     sns.despine()
     plt.tight_layout()