From 065f0b59e35d10f8345c9520b96bedd76906548c Mon Sep 17 00:00:00 2001 From: RandomDefaultUser Date: Mon, 25 Nov 2024 09:28:02 +0000 Subject: [PATCH] deploy: 6cb1d3f306033c8aedb7d31d0ab383fcd4dad00e --- _modules/mala/common/parameters.html | 48 +++-- _modules/mala/datahandling/data_handler.html | 23 +-- _modules/mala/datahandling/data_scaler.html | 197 +++++++++++++------ _sources/basic_usage/trainingmodel.rst.txt | 15 +- api/mala.common.parameters.html | 48 +++-- api/mala.datahandling.data_handler.html | 4 +- api/mala.datahandling.data_scaler.html | 86 ++++---- api/mala.datahandling.html | 5 +- api/mala.html | 5 +- api/modules.html | 5 +- basic_usage/trainingmodel.html | 15 +- genindex.html | 10 +- objects.inv | Bin 6356 -> 6340 bytes searchindex.js | 2 +- 14 files changed, 288 insertions(+), 175 deletions(-) diff --git a/_modules/mala/common/parameters.html b/_modules/mala/common/parameters.html index c43d01ffa..292a198ea 100644 --- a/_modules/mala/common/parameters.html +++ b/_modules/mala/common/parameters.html @@ -674,27 +674,45 @@

Source code for mala.common.parameters

         Specifies how input quantities are normalized.
         Options:
 
-            - "None": No normalization is applied.
-            - "standard": Standardization (Scale to mean 0, standard
-              deviation 1)
-            - "normal": Min-Max scaling (Scale to be in range 0...1)
-            - "feature-wise-standard": Row Standardization (Scale to mean 0,
-              standard deviation 1)
-            - "feature-wise-normal": Row Min-Max scaling (Scale to be in range
-              0...1)
+            - "None": No scaling is applied.
+            - "standard": Standardization (Scale to mean 0,
+              standard deviation 1) is applied to the entire array.
+            - "minmax": Min-Max scaling (Scale to be in range 0...1) is applied
+              to the entire array.
+            - "feature-wise-standard": Standardization (Scale to mean 0,
+              standard deviation 1) is applied to each feature dimension
+              individually.
+              I.e., if your training data has dimensions (d,f), then each
+              of the f columns with d entries is scaled indiviually.
+            - "feature-wise-minmax": Min-Max scaling (Scale to be in range
+              0...1) is applied to each feature dimension individually.
+              I.e., if your training data has dimensions (d,f), then each
+              of the f columns with d entries is scaled indiviually.
+            - "normal": (DEPRECATED) Old name for "minmax".
+            - "feature-wise-normal": (DEPRECATED) Old name for
+              "feature-wise-minmax"
 
     output_rescaling_type : string
         Specifies how output quantities are normalized.
         Options:
 
-            - "None": No normalization is applied.
+            - "None": No scaling is applied.
             - "standard": Standardization (Scale to mean 0,
-              standard deviation 1)
-            - "normal": Min-Max scaling (Scale to be in range 0...1)
-            - "feature-wise-standard": Row Standardization (Scale to mean 0,
-              standard deviation 1)
-            - "feature-wise-normal": Row Min-Max scaling (Scale to be in
-              range 0...1)
+              standard deviation 1) is applied to the entire array.
+            - "minmax": Min-Max scaling (Scale to be in range 0...1) is applied
+              to the entire array.
+            - "feature-wise-standard": Standardization (Scale to mean 0,
+              standard deviation 1) is applied to each feature dimension
+              individually.
+              I.e., if your training data has dimensions (d,f), then each
+              of the f columns with d entries is scaled indiviually.
+            - "feature-wise-minmax": Min-Max scaling (Scale to be in range
+              0...1) is applied to each feature dimension individually.
+              I.e., if your training data has dimensions (d,f), then each
+              of the f columns with d entries is scaled indiviually.
+            - "normal": (DEPRECATED) Old name for "minmax".
+            - "feature-wise-normal": (DEPRECATED) Old name for
+              "feature-wise-minmax"
 
     use_lazy_loading : bool
         If True, data is lazily loaded, i.e. only the snapshots that are
diff --git a/_modules/mala/datahandling/data_handler.html b/_modules/mala/datahandling/data_handler.html
index 12a262dcf..535540b02 100644
--- a/_modules/mala/datahandling/data_handler.html
+++ b/_modules/mala/datahandling/data_handler.html
@@ -217,6 +217,8 @@ 
Source code for mala.datahandling.data_handler
self.nr_training_snapshots = 0
         self.nr_test_snapshots = 0
         self.nr_validation_snapshots = 0
+        self.input_data_scaler.reset()
+        self.output_data_scaler.reset()
         super(DataHandler, self).clear_data()

 
 
@@ -408,7 +410,10 @@ 
Source code for mala.datahandling.data_handler

 [docs]
     def raw_numpy_to_converted_scaled_tensor(
-        self, numpy_array, data_type, units, convert3Dto1D=False
+        self,
+        numpy_array,
+        data_type,
+        units,
     ):
         """
         Transform a raw numpy array into a scaled torch tensor.
@@ -425,9 +430,6 @@ 
Source code for mala.datahandling.data_handler
            processed.
         units : string
             Units of the data that is processed.
-        convert3Dto1D : bool
-            If True (default: False), then a (x,y,z,dim) array is transformed
-            into a (x*y*z,dim) array.
 
         Returns
         -------
@@ -446,12 +448,12 @@ 
Source code for mala.datahandling.data_handler
)
 
         # If desired, the dimensions can be changed.
-        if convert3Dto1D:
+        if len(np.shape(numpy_array)) == 4:
             if data_type == "in":
                 data_dimension = self.input_dimension
             else:
                 data_dimension = self.output_dimension
-            grid_size = np.prod(numpy_array[0:3])
+            grid_size = np.prod(np.shape(numpy_array)[0:3])
             desired_dimensions = [grid_size, data_dimension]
         else:
             desired_dimensions = None
@@ -924,7 +926,6 @@ 
Source code for mala.datahandling.data_handler
# scaling. This should save some performance.
 
         if self.parameters.use_lazy_loading:
-            self.input_data_scaler.start_incremental_fitting()
             # We need to perform the data scaling over the entirety of the
             # training data.
             for snapshot in self.parameters.snapshot_directories_list:
@@ -962,9 +963,7 @@ 
Source code for mala.datahandling.data_handler
[snapshot.grid_size, self.input_dimension]
                     )
                     tmp = torch.from_numpy(tmp).float()
-                    self.input_data_scaler.incremental_fit(tmp)
-
-            self.input_data_scaler.finish_incremental_fitting()
+                    self.input_data_scaler.partial_fit(tmp)
 
         else:
             self.__load_data("training", "inputs")
@@ -985,7 +984,6 @@ 
Source code for mala.datahandling.data_handler
if self.parameters.use_lazy_loading:
             i = 0
-            self.output_data_scaler.start_incremental_fitting()
             # We need to perform the data scaling over the entirety of the
             # training data.
             for snapshot in self.parameters.snapshot_directories_list:
@@ -1021,9 +1019,8 @@ 
Source code for mala.datahandling.data_handler
[snapshot.grid_size, self.output_dimension]
                     )
                     tmp = torch.from_numpy(tmp).float()
-                    self.output_data_scaler.incremental_fit(tmp)
+                    self.output_data_scaler.partial_fit(tmp)
                 i += 1
-            self.output_data_scaler.finish_incremental_fitting()
 
         else:
             self.__load_data("training", "outputs")
diff --git a/_modules/mala/datahandling/data_scaler.html b/_modules/mala/datahandling/data_scaler.html
index 279920bd1..cecf405a5 100644
--- a/_modules/mala/datahandling/data_scaler.html
+++ b/_modules/mala/datahandling/data_scaler.html
@@ -89,15 +89,21 @@ 
Source code for mala.datahandling.data_scaler
import torch.distributed as dist
 
 from mala.common.parameters import printout
+from mala.common.parallelizer import parallel_warn
 
 
+# IMPORTANT: If you change the docstrings, make sure to also change them
+# in the ParametersData subclass, because users do usually not interact
+# with this class directly.
 

 [docs]
 class DataScaler:
     """Scales input and output data.
 
     Sort of emulates the functionality of the scikit-learn library, but by
-    implementing the class by ourselves we have more freedom.
+    implementing the class by ourselves we have more freedom. Specifically
+    assumes data of the form (d,f), where d=x*y*z, i.e., the product of spatial
+    dimensions, and f is the feature dimension.
 
     Parameters
     ----------
@@ -105,14 +111,23 @@ 
Source code for mala.datahandling.data_scaler
        Specifies how scaling should be performed.
         Options:
 
-        - "None": No normalization is applied.
+        - "None": No scaling is applied.
         - "standard": Standardization (Scale to mean 0,
-          standard deviation 1)
-        - "normal": Min-Max scaling (Scale to be in range 0...1)
-        - "feature-wise-standard": Row Standardization (Scale to mean 0,
-          standard deviation 1)
-        - "feature-wise-normal": Row Min-Max scaling (Scale to be in range
-          0...1)
+          standard deviation 1) is applied to the entire array.
+        - "minmax": Min-Max scaling (Scale to be in range 0...1) is applied
+          to the entire array.
+        - "feature-wise-standard": Standardization (Scale to mean 0,
+          standard deviation 1) is applied to each feature dimension
+          individually.
+          I.e., if your training data has dimensions (d,f), then each
+          of the f columns with d entries is scaled indiviually.
+        - "feature-wise-minmax": Min-Max scaling (Scale to be in range
+          0...1) is applied to each feature dimension individually.
+          I.e., if your training data has dimensions (d,f), then each
+          of the f columns with d entries is scaled indiviually.
+        - "normal": (DEPRECATED) Old name for "minmax".
+        - "feature-wise-normal": (DEPRECATED) Old name for
+          "feature-wise-minmax"
 
     use_ddp : bool
         If True, the DataScaler will use ddp to check that data is
@@ -123,7 +138,7 @@ 
Source code for mala.datahandling.data_scaler
self.use_ddp = use_ddp
         self.typestring = typestring
         self.scale_standard = False
-        self.scale_normal = False
+        self.scale_minmax = False
         self.feature_wise = False
         self.cantransform = False
         self.__parse_typestring()
@@ -142,25 +157,34 @@ 
Source code for mala.datahandling.data_scaler
def __parse_typestring(self):
         """Parse the typestring to class attributes."""
         self.scale_standard = False
-        self.scale_normal = False
+        self.scale_minmax = False
         self.feature_wise = False
 
         if "standard" in self.typestring:
             self.scale_standard = True
         if "normal" in self.typestring:
-            self.scale_normal = True
+            parallel_warn(
+                "Options 'normal' and 'feature-wise-normal' will be "
+                "deprecated, starting in MALA v1.4.0. Please use 'minmax' and "
+                "'feature-wise-minmax' instead.",
+                min_verbosity=0,
+                category=FutureWarning,
+            )
+            self.scale_minmax = True
+        if "minmax" in self.typestring:
+            self.scale_minmax = True
         if "feature-wise" in self.typestring:
             self.feature_wise = True
-        if self.scale_standard is False and self.scale_normal is False:
+        if self.scale_standard is False and self.scale_minmax is False:
             printout("No data rescaling will be performed.", min_verbosity=1)
             self.cantransform = True
             return
-        if self.scale_standard is True and self.scale_normal is True:
+        if self.scale_standard is True and self.scale_minmax is True:
             raise Exception("Invalid input data rescaling.")
 
-

-[docs]
-    def start_incremental_fitting(self):
+

+[docs]
+    def reset(self):
         """
         Start the incremental calculation of scaling parameters.
 
@@ -169,9 +193,9 @@ 
Source code for mala.datahandling.data_scaler
self.total_data_count = 0

 
 
-

-[docs]
-    def incremental_fit(self, unscaled):
+

+[docs]
+    def partial_fit(self, unscaled):
         """
         Add data to the incremental calculation of scaling parameters.
 
@@ -183,7 +207,16 @@ 
Source code for mala.datahandling.data_scaler
            Data that is to be added to the fit.
 
         """
-        if self.scale_standard is False and self.scale_normal is False:
+        if len(unscaled.size()) != 2:
+            raise ValueError(
+                "MALA DataScaler are designed for 2D-arrays, "
+                "while a {0}D-array has been provided.".format(
+                    len(unscaled.size())
+                )
+            )
+
+        if self.scale_standard is False and self.scale_minmax is False:
+            self.cantransform = True
             return
         else:
             with torch.no_grad():
@@ -232,7 +265,7 @@ 
Source code for mala.datahandling.data_scaler
self.stds = new_std
                         self.total_data_count += current_data_count
 
-                    if self.scale_normal:
+                    if self.scale_minmax:
                         new_maxs = torch.max(unscaled, 0, keepdim=True)
                         if list(self.maxs.size())[0] > 0:
                             for i in range(list(new_maxs.values.size())[1]):
@@ -295,24 +328,14 @@ 
Source code for mala.datahandling.data_scaler
self.total_std = torch.sqrt(self.total_std)
                         self.total_data_count += current_data_count
 
-                    if self.scale_normal:
+                    if self.scale_minmax:
                         new_max = torch.max(unscaled)
                         if new_max > self.total_max:
                             self.total_max = new_max
 
                         new_min = torch.min(unscaled)
                         if new_min < self.total_min:
-                            self.total_min = new_min

-
-
-

-[docs]
-    def finish_incremental_fitting(self):
-        """
-        Indicate that all data has been added to the incremental calculation.
-
-        This is necessary for lazy loading.
-        """
+                            self.total_min = new_min
         self.cantransform = True

 
 
@@ -328,7 +351,15 @@ 
Source code for mala.datahandling.data_scaler
            Data that on which the scaling will be calculated.
 
         """
-        if self.scale_standard is False and self.scale_normal is False:
+        if len(unscaled.size()) != 2:
+            raise ValueError(
+                "MALA DataScaler are designed for 2D-arrays, "
+                "while a {0}D-array has been provided.".format(
+                    len(unscaled.size())
+                )
+            )
+
+        if self.scale_standard is False and self.scale_minmax is False:
             return
         else:
             with torch.no_grad():
@@ -342,7 +373,7 @@ 
Source code for mala.datahandling.data_scaler
self.means = torch.mean(unscaled, 0, keepdim=True)
                         self.stds = torch.std(unscaled, 0, keepdim=True)
 
-                    if self.scale_normal:
+                    if self.scale_minmax:
                         self.maxs = torch.max(unscaled, 0, keepdim=True).values
                         self.mins = torch.min(unscaled, 0, keepdim=True).values
 
@@ -356,7 +387,7 @@ 
Source code for mala.datahandling.data_scaler
self.total_mean = torch.mean(unscaled)
                         self.total_std = torch.std(unscaled)
 
-                    if self.scale_normal:
+                    if self.scale_minmax:
                         self.total_max = torch.max(unscaled)
                         self.total_min = torch.min(unscaled)
 
@@ -365,7 +396,7 @@ 
Source code for mala.datahandling.data_scaler

 [docs]
-    def transform(self, unscaled):
+    def transform(self, unscaled, copy=False):
         """
         Transform data from unscaled to scaled.
 
@@ -377,13 +408,29 @@ 
Source code for mala.datahandling.data_scaler
        unscaled : torch.Tensor
             Real world data.
 
+        copy : bool
+            If False, data is modified in-place. If True, a copy of the
+            data is modified. Default is False.
+
         Returns
         -------
         scaled : torch.Tensor
             Scaled data.
         """
+        if len(unscaled.size()) != 2:
+            raise ValueError(
+                "MALA DataScaler are designed for 2D-arrays, "
+                "while a {0}D-array has been provided.".format(
+                    len(unscaled.size())
+                )
+            )
+
+        # Backward compatability.
+        if not hasattr(self, "scale_minmax") and hasattr(self, "scale_normal"):
+            self.scale_minmax = self.scale_normal
+
         # First we need to find out if we even have to do anything.
-        if self.scale_standard is False and self.scale_normal is False:
+        if self.scale_standard is False and self.scale_minmax is False:
             pass
 
         elif self.cantransform is False:
@@ -394,6 +441,8 @@ 
Source code for mala.datahandling.data_scaler
# Perform the actual scaling, but use no_grad to make sure
         # that the next couple of iterations stay untracked.
+        scaled = unscaled.clone() if copy else unscaled
+
         with torch.no_grad():
             if self.feature_wise:
 
@@ -402,12 +451,12 @@ 
Source code for mala.datahandling.data_scaler
##########################
 
                 if self.scale_standard:
-                    unscaled -= self.means
-                    unscaled /= self.stds
+                    scaled -= self.means
+                    scaled /= self.stds
 
-                if self.scale_normal:
-                    unscaled -= self.mins
-                    unscaled /= self.maxs - self.mins
+                if self.scale_minmax:
+                    scaled -= self.mins
+                    scaled /= self.maxs - self.mins
 
             else:
 
@@ -416,17 +465,19 @@ 
Source code for mala.datahandling.data_scaler
##########################
 
                 if self.scale_standard:
-                    unscaled -= self.total_mean
-                    unscaled /= self.total_std
+                    scaled -= self.total_mean
+                    scaled /= self.total_std
 
-                if self.scale_normal:
-                    unscaled -= self.total_min
-                    unscaled /= self.total_max - self.total_min

+                if self.scale_minmax:
+                    scaled -= self.total_min
+                    scaled /= self.total_max - self.total_min
+
+        return scaled

 
 
 

 [docs]
-    def inverse_transform(self, scaled, as_numpy=False):
+    def inverse_transform(self, scaled, copy=False, as_numpy=False):
         """
         Transform data from scaled to unscaled.
 
@@ -439,7 +490,11 @@ 
Source code for mala.datahandling.data_scaler
            Scaled data.
 
         as_numpy : bool
-            If True, a numpy array is returned, otherwsie.
+            If True, a numpy array is returned, otherwise a torch tensor.
+
+        copy : bool
+            If False, data is modified in-place. If True, a copy of the
+            data is modified. Default is False.
 
         Returns
         -------
@@ -447,9 +502,25 @@ 
Source code for mala.datahandling.data_scaler
            Real world data.
 
         """
+        if len(scaled.size()) != 2:
+            raise ValueError(
+                "MALA DataScaler are designed for 2D-arrays, "
+                "while a {0}D-array has been provided.".format(
+                    len(scaled.size())
+                )
+            )
+
+        # Backward compatability.
+        if not hasattr(self, "scale_minmax") and hasattr(self, "scale_normal"):
+            self.scale_minmax = self.scale_normal
+
+        # Perform the actual scaling, but use no_grad to make sure
+        # that the next couple of iterations stay untracked.
+        unscaled = scaled.clone() if copy else scaled
+
         # First we need to find out if we even have to do anything.
-        if self.scale_standard is False and self.scale_normal is False:
-            unscaled = scaled
+        if self.scale_standard is False and self.scale_minmax is False:
+            pass
 
         else:
             if self.cantransform is False:
@@ -468,12 +539,12 @@ 
Source code for mala.datahandling.data_scaler
##########################
 
                     if self.scale_standard:
-                        unscaled = (scaled * self.stds) + self.means
+                        unscaled *= self.stds
+                        unscaled += self.means
 
-                    if self.scale_normal:
-                        unscaled = (
-                            scaled * (self.maxs - self.mins)
-                        ) + self.mins
+                    if self.scale_minmax:
+                        unscaled *= self.maxs - self.mins
+                        unscaled += self.mins
 
                 else:
 
@@ -482,13 +553,13 @@ 
Source code for mala.datahandling.data_scaler
##########################
 
                     if self.scale_standard:
-                        unscaled = (scaled * self.total_std) + self.total_mean
+                        unscaled *= self.total_std
+                        unscaled += self.total_mean
+
+                    if self.scale_minmax:
+                        unscaled *= self.total_max - self.total_min
+                        unscaled += self.total_min
 
-                    if self.scale_normal:
-                        unscaled = (
-                            scaled * (self.total_max - self.total_min)
-                        ) + self.total_min
-        #
         if as_numpy:
             return unscaled.detach().numpy().astype(np.float64)
         else:
diff --git a/_sources/basic_usage/trainingmodel.rst.txt b/_sources/basic_usage/trainingmodel.rst.txt
index e6bc8c967..53cb8a8df 100644
--- a/_sources/basic_usage/trainingmodel.rst.txt
+++ b/_sources/basic_usage/trainingmodel.rst.txt
@@ -28,7 +28,7 @@ options to train a simple network with example data, namely
             parameters = mala.Parameters()
 
             parameters.data.input_rescaling_type = "feature-wise-standard"
-            parameters.data.output_rescaling_type = "normal"
+            parameters.data.output_rescaling_type = "minmax"
 
             parameters.network.layer_activations = ["ReLU"]
 
@@ -43,15 +43,18 @@ sub-objects dealing with the individual aspects of the workflow. In the first
 two lines, which data scaling MALA should employ. Scaling data greatly
 improves the performance of NN based ML models. Options are
 
-* ``None``: No normalization is applied.
+* ``None``: No scaling is applied.
 
-* ``standard``: Standardization (Scale to mean 0, standard deviation 1)
+* ``standard``: Standardization (Scale to mean 0, standard deviation 1) is
+  applied to the entire array.
 
-* ``normal``: Min-Max scaling (Scale to be in range 0...1)
+* ``minmax``: Min-Max scaling (Scale to be in range 0...1) is applied to the entire array.
 
-* ``feature-wise-standard``: Row Standardization (Scale to mean 0, standard deviation 1)
+* ``feature-wise-standard``: Standardization (Scale to mean 0, standard
+  deviation 1) is applied to each feature dimension individually.
 
-* ``feature-wise-normal``: Row Min-Max scaling (Scale to be in range 0...1)
+* ``feature-wise-minmax``: Min-Max scaling (Scale to be in range 0...1) is
+  applied to each feature dimension individually.
 
 Here, we specify that MALA should standardize the input (=descriptors)
 by feature (i.e., each entry of the vector separately on the grid) and
diff --git a/api/mala.common.parameters.html b/api/mala.common.parameters.html
index 7e0ca4ecd..ef62caae2 100644
--- a/api/mala.common.parameters.html
+++ b/api/mala.common.parameters.html
@@ -505,14 +505,23 @@
 Options:

 

 
    
-
  • “None”: No normalization is applied.
    • 
-
  • “standard”: Standardization (Scale to mean 0, standard
-deviation 1)
    • 
-
  • “normal”: Min-Max scaling (Scale to be in range 0…1)
    • 
-
  • “feature-wise-standard”: Row Standardization (Scale to mean 0,
-standard deviation 1)
    • 
-
  • “feature-wise-normal”: Row Min-Max scaling (Scale to be in range
-0…1)
    • 
+
  • “None”: No scaling is applied.
    • 
+
  • “standard”: Standardization (Scale to mean 0,
+standard deviation 1) is applied to the entire array.
    • 
+
  • “minmax”: Min-Max scaling (Scale to be in range 0…1) is applied
+to the entire array.
    • 
+
  • “feature-wise-standard”: Standardization (Scale to mean 0,
+standard deviation 1) is applied to each feature dimension
+individually.
+I.e., if your training data has dimensions (d,f), then each
+of the f columns with d entries is scaled indiviually.
    • 
+
  • “feature-wise-minmax”: Min-Max scaling (Scale to be in range
+0…1) is applied to each feature dimension individually.
+I.e., if your training data has dimensions (d,f), then each
+of the f columns with d entries is scaled indiviually.
    • 
+
  • “normal”: (DEPRECATED) Old name for “minmax”.
    • 
+
  • “feature-wise-normal”: (DEPRECATED) Old name for
+“feature-wise-minmax”
    • 
 

 

 

@@ -529,14 +538,23 @@
 Options:

 

 
    
-
  • “None”: No normalization is applied.
    • 
+
  • “None”: No scaling is applied.
    • 
 
  • “standard”: Standardization (Scale to mean 0,
-standard deviation 1)
    • 
-
  • “normal”: Min-Max scaling (Scale to be in range 0…1)
    • 
-
  • “feature-wise-standard”: Row Standardization (Scale to mean 0,
-standard deviation 1)
    • 
-
  • “feature-wise-normal”: Row Min-Max scaling (Scale to be in
-range 0…1)
    • 
+standard deviation 1) is applied to the entire array.
    
+
  • “minmax”: Min-Max scaling (Scale to be in range 0…1) is applied
+to the entire array.
    • 
+
  • “feature-wise-standard”: Standardization (Scale to mean 0,
+standard deviation 1) is applied to each feature dimension
+individually.
+I.e., if your training data has dimensions (d,f), then each
+of the f columns with d entries is scaled indiviually.
    • 
+
  • “feature-wise-minmax”: Min-Max scaling (Scale to be in range
+0…1) is applied to each feature dimension individually.
+I.e., if your training data has dimensions (d,f), then each
+of the f columns with d entries is scaled indiviually.
    • 
+
  • “normal”: (DEPRECATED) Old name for “minmax”.
    • 
+
  • “feature-wise-normal”: (DEPRECATED) Old name for
+“feature-wise-minmax”
    • 
 

 

 

diff --git a/api/mala.datahandling.data_handler.html b/api/mala.datahandling.data_handler.html
index b4ad1ba50..53e74cd26 100644
--- a/api/mala.datahandling.data_handler.html
+++ b/api/mala.datahandling.data_handler.html
@@ -225,7 +225,7 @@
 
 

 

-raw_numpy_to_converted_scaled_tensor(numpy_array, data_type, units, convert3Dto1D=False)[source]

+raw_numpy_to_converted_scaled_tensor(numpy_array, data_type, units)[source]
 
Transform a raw numpy array into a scaled torch tensor.

 
This tensor will also be in the right units, i.e. a tensor that can
 simply be put into a MALA network.

@@ -236,8 +236,6 @@
 
  • data_type (string) – Either “in” or “out”, depending if input or output data is
 processed.
    • 
 
  • units (string) – Units of the data that is processed.
    • 
-
  • convert3Dto1D (bool) – If True (default: False), then a (x,y,z,dim) array is transformed
-into a (x*y*z,dim) array.
    • 
 
 
    
 
    Returns:
    
diff --git a/api/mala.datahandling.data_scaler.html b/api/mala.datahandling.data_scaler.html
index aaa18a513..a0d4ca47f 100644
--- a/api/mala.datahandling.data_scaler.html
+++ b/api/mala.datahandling.data_scaler.html
@@ -122,21 +122,32 @@
 
    Bases: object
    
 
    Scales input and output data.
    
 
    Sort of emulates the functionality of the scikit-learn library, but by
-implementing the class by ourselves we have more freedom.
    
+implementing the class by ourselves we have more freedom. Specifically
+assumes data of the form (d,f), where d=x*y*z, i.e., the product of spatial
+dimensions, and f is the feature dimension.
    
 
    
 
    Parameters:
    
 
      
 
    • typestring (string) – 
      Specifies how scaling should be performed.
 Options:
      
 
        
-
      • ”None”: No normalization is applied.
        • 
+
      • ”None”: No scaling is applied.
        • 
 
      • ”standard”: Standardization (Scale to mean 0,
-standard deviation 1)
        • 
-
      • ”normal”: Min-Max scaling (Scale to be in range 0…1)
        • 
-
      • ”feature-wise-standard”: Row Standardization (Scale to mean 0,
-standard deviation 1)
        • 
-
      • ”feature-wise-normal”: Row Min-Max scaling (Scale to be in range
-0…1)
        • 
+standard deviation 1) is applied to the entire array.
        
+
      • ”minmax”: Min-Max scaling (Scale to be in range 0…1) is applied
+to the entire array.
        • 
+
      • ”feature-wise-standard”: Standardization (Scale to mean 0,
+standard deviation 1) is applied to each feature dimension
+individually.
+I.e., if your training data has dimensions (d,f), then each
+of the f columns with d entries is scaled indiviually.
        • 
+
      • ”feature-wise-minmax”: Min-Max scaling (Scale to be in range
+0…1) is applied to each feature dimension individually.
+I.e., if your training data has dimensions (d,f), then each
+of the f columns with d entries is scaled indiviually.
        • 
+
      • ”normal”: (DEPRECATED) Old name for “minmax”.
        • 
+
      • ”feature-wise-normal”: (DEPRECATED) Old name for
+“feature-wise-minmax”
        • 
 
      
 
      • 
 
    • use_ddp (bool) – If True, the DataScaler will use ddp to check that data is
@@ -144,13 +155,6 @@
 
    
 
    
 
    
-
    
-
    
-finish_incremental_fitting()[source]
    
-
    Indicate that all data has been added to the incremental calculation.
    
-
    This is necessary for lazy loading.
    
-
    
-
 
    
 
    
 fit(unscaled)[source]
    
@@ -162,21 +166,9 @@
 
    
 
    
 
-
    
-
    
-incremental_fit(unscaled)[source]
    
-
    Add data to the incremental calculation of scaling parameters.
    
-
    This is necessary for lazy loading.
    
-
    
-
    Parameters:
    
-
    unscaled (torch.Tensor) – Data that is to be added to the fit.
    
-
    
-
    
-
    
-
 
    
 
    
-inverse_transform(scaled, as_numpy=False)[source]
    
+inverse_transform(scaled, copy=False, as_numpy=False)[source]
 
    Transform data from scaled to unscaled.
    
 
    Unscaled means real world data, scaled means data as is used in
 the network.
    
@@ -184,7 +176,9 @@
 
    Parameters:
    
 
      
 
    • scaled (torch.Tensor) – Scaled data.
      • 
-
    • as_numpy (bool) – If True, a numpy array is returned, otherwsie.
      • 
+
    • as_numpy (bool) – If True, a numpy array is returned, otherwise a torch tensor.
      • 
+
    • copy (bool) – If False, data is modified in-place. If True, a copy of the
+data is modified. Default is False.
      • 
 
    
 
    
 
    Returns:
    
@@ -216,6 +210,25 @@
 
    
 
    
 
+
    
+
    
+partial_fit(unscaled)[source]
    
+
    Add data to the incremental calculation of scaling parameters.
    
+
    This is necessary for lazy loading.
    
+
    
+
    Parameters:
    
+
    unscaled (torch.Tensor) – Data that is to be added to the fit.
    
+
    
+
    
+
    
+
+
    
+
    
+reset()[source]
    
+
    Start the incremental calculation of scaling parameters.
    
+
    This is necessary for lazy loading.
    
+
    
+
 
    
 
    
 save(filename, save_format='pickle')[source]
    
@@ -230,22 +243,19 @@
 
    
 
    
 
-
    
-
    
-start_incremental_fitting()[source]
    
-
    Start the incremental calculation of scaling parameters.
    
-
    This is necessary for lazy loading.
    
-
    
-
 
    
 
    
-transform(unscaled)[source]
    
+transform(unscaled, copy=False)[source]
 
    Transform data from unscaled to scaled.
    
 
    Unscaled means real world data, scaled means data as is used in
 the network. Data is transformed in-place.
    
 
    
 
    Parameters:
    
-
    unscaled (torch.Tensor) – Real world data.
    
+
      
+
    • unscaled (torch.Tensor) – Real world data.
      • 
+
    • copy (bool) – If False, data is modified in-place. If True, a copy of the
+data is modified. Default is False.
      • 
+
    
 
    
 
    Returns:
    
 
    scaled – Scaled data.
    
diff --git a/api/mala.datahandling.html b/api/mala.datahandling.html
index 74cd4618f..79c96c2d3 100644
--- a/api/mala.datahandling.html
+++ b/api/mala.datahandling.html
@@ -154,13 +154,12 @@ 
    datahandlingdata_repo
 
  • data_scaler
      
 
    • DataScaler
 
      • 
diff --git a/api/mala.html b/api/mala.html
index 9a063b04e..83a0e7dc4 100644
--- a/api/mala.html
+++ b/api/mala.html
@@ -348,13 +348,12 @@ 
      mala<
 
    • data_repo
      • 
 
    • data_scaler
        
 
      • DataScaler
 
        • 
diff --git a/api/modules.html b/api/modules.html
index a37fb419a..b9120e8bf 100644
--- a/api/modules.html
+++ b/api/modules.html
@@ -335,13 +335,12 @@ 
        API referencedata_repo
 
      • data_scaler
 
        Here, we specify that MALA should standardize the input (=descriptors)
 by feature (i.e., each entry of the vector separately on the grid) and
diff --git a/genindex.html b/genindex.html
index 1e71a7fca..2188f0615 100644
--- a/genindex.html
+++ b/genindex.html
@@ -410,8 +410,6 @@ 
        F
        
       
      • fermi_function() (in module mala.targets.calculation_helpers)
 
        • 
       
      • finalize() (in module mala.common.parallelizer)
-
        • 
-      
      • finish_incremental_fitting() (DataScaler method)
 
        • 
       
      • first_snapshot (TrajectoryAnalyzer property)
 
        • 
@@ -682,8 +680,6 @@ 
        I
        
 
        
   
          
       
        • implemented_properties (MALA attribute)
-
          • 
-      
        • incremental_fit() (DataScaler method)
 
          • 
       
        • inference_data_grid (ParametersRunning attribute)
 
          • 
@@ -1492,6 +1488,8 @@ 
          P
          
       
        • parse_trial_oat() (ObjectiveBase method)
 
          • 
       
        • parse_trial_optuna() (ObjectiveBase method)
+
          • 
+      
        • partial_fit() (DataScaler method)
 
          • 
       
        • perform_study() (ACSDAnalyzer method)
 
@@ -1608,6 +1606,8 @@ 
          R
          
       
        • readline() (CubeFile method)
 
          • 
       
        • requeue_zombie_trials() (HyperOptOptuna static method)
+
          • 
+      
        • reset() (DataScaler method)
 
          • 
       
        • resize_snapshots_for_debugging() (DataHandler method)
 
          • 
@@ -1734,8 +1734,6 @@ 
          S
          
       
        • snapshot_directories_list (ParametersData attribute)
 
          • 
       
        • ssf_parameters (ParametersTargets attribute)
-
          • 
-      
        • start_incremental_fitting() (DataScaler method)
 
          • 
       
        • static_structure_factor_from_atoms() (Target static method)
 
          • 
diff --git a/objects.inv b/objects.inv
index dc991bf1e7a5e98ca18e2bd6a45affa75fbad0f0..f3b3d4b50e2238bd0106f81d8f2656c3a7ad8de6 100644
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diff --git a/searchindex.js b/searchindex.js
index 989859a88..9888b3db6 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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"installing-quantum-espresso-total-energy-module"]], "Installing the Python extension": [[76, "installing-the-python-extension"], [78, "installing-the-python-extension"]], "Installing the Python library": [[77, "installing-the-python-library"]], "Issues": [[0, "issues"]], "License": [[0, "license"]], "List of hyperparameters": [[70, "list-of-hyperparameters"], [70, "id1"]], "MALA contributors": [[0, "mala-contributors"]], "MALA publications": [[75, "mala-publications"]], "Parallel data conversion": [[2, "parallel-data-conversion"]], "Parallel predictions": [[5, "parallel-predictions"]], "Parallelizing a hyperparameter search": [[3, "parallelizing-a-hyperparameter-search"]], "Predictions on GPUs": [[5, "predictions-on-gpus"]], "Prerequisites": [[76, "prerequisites"], [77, "prerequisites"], [78, "prerequisites"]], "Pull Requests": [[0, "pull-requests"]], "Setting parameters": [[73, "setting-parameters"]], "Storing data with OpenPMD": [[4, "storing-data-with-openpmd"]], "Testing a model": [[73, "testing-a-model"]], "Training an ML-DFT model": [[73, "training-an-ml-dft-model"]], "Training in parallel": [[6, "training-in-parallel"]], "Tuning descriptors": [[2, "id1"]], "Using MALA in production": [[5, "using-mala-in-production"]], "Using ML-DFT models for predictions": [[72, "using-ml-dft-models-for-predictions"]], "Using a GPU": [[6, "using-a-gpu"]], "Using lazy loading": [[6, "using-lazy-loading"]], "Using tensorboard": [[6, "using-tensorboard"]], "Using the MALA ASE calculator": [[72, "using-the-mala-ase-calculator"]], "Versioning and releases": [[0, "versioning-and-releases"]], "Visualizing observables": [[5, "visualizing-observables"]], "Welcome to MALA!": [[75, "welcome-to-mala"]], "What is MALA?": [[75, "what-is-mala"]], "Where to start?": [[75, "where-to-start"]], "Who is behind MALA?": [[75, "who-is-behind-mala"]], "acsd_analyzer": [[39, "module-mala.network.acsd_analyzer"]], "ase_calculator": [[37, "module-mala.interfaces.ase_calculator"]], "atomic_density": 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\ No newline at end of file
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