Update scalers for release

sarahshi · Dec 10, 2023 · 9f909d5 · 9f909d5
1 parent c249b75
commit 9f909d5
Show file tree

Hide file tree

Showing 6 changed files with 9 additions and 9 deletions.
diff --git a/src/mineralML/_version.py b/src/mineralML/_version.py
@@ -5,4 +5,4 @@
 # 1) we don't load dependencies by storing it in __init__.py
 # 2) we can import it in setup.py for the same reason
 # 3) we can import it into your module
-__version__ = '0.0.0.2'
+__version__ = '0.0.0.3'
diff --git a/src/mineralML/core.py b/src/mineralML/core.py
@@ -102,25 +102,25 @@ def load_df(filepath):
     return df
 
 
-def load_scaler():
+def load_scaler(scaler_path):
 
     """
 
     Loads a pre-fitted scaler's mean and std from a .npz file. This scaler is a StandardScaler
     for normalizing or standardizing input data before passing it to a machine learning model. 
 
     Returns:
-        mean, std (pandas Series): The mean and std from the scaler object 'scaler.npz'.
+        mean, std (pandas Series): The mean and std from the scaler object 'scaler_ae/nn.npz'.
 
     Raises:
-        FileNotFoundError: If 'scaler.npz' is not found in the expected directory.
+        FileNotFoundError: If 'scaler_ae/nn.npz' is not found in the expected directory.
         Exception: Propagates any exception raised during the scaler loading process.
 
     """
 
     # Define the path to the scaler relative to this file's location.
     current_dir = os.path.dirname(__file__)
-    scaler_path = os.path.join(current_dir, 'scaler.npz')  # Note the .joblib extension
+    scaler_path = os.path.join(current_dir, scaler_path)  # Note the .joblib extension
 
     oxides = ['SiO2', 'TiO2', 'Al2O3', 'FeOt', 'MnO', 'MgO', 'CaO', 'Na2O', 'K2O', 'Cr2O3']
 

diff --git a/src/mineralML/scaler_ae.npz b/src/mineralML/scaler_ae.npz
diff --git a/src/mineralML/scaler.npz → src/mineralML/scaler_nn.npz b/src/mineralML/scaler.npz → src/mineralML/scaler_nn.npz
diff --git a/src/mineralML/supervised.py b/src/mineralML/supervised.py
@@ -106,7 +106,7 @@ def norm_data_nn(df):
     """
 
     oxides = ['SiO2', 'TiO2', 'Al2O3', 'FeOt', 'MnO', 'MgO', 'CaO', 'Na2O', 'K2O', 'Cr2O3']
-    mean, std = load_scaler()
+    mean, std = load_scaler('scaler_nn.npz')
 
     scaled_df = df[oxides].copy()
 

diff --git a/src/mineralML/unsupervised.py b/src/mineralML/unsupervised.py
@@ -84,7 +84,7 @@ def norm_data_ae(df):
     """
 
     oxides = ['SiO2', 'TiO2', 'Al2O3', 'FeOt', 'MnO', 'MgO', 'CaO', 'Na2O', 'K2O', 'Cr2O3']
-    mean, std = load_scaler()
+    mean, std = load_scaler('scaler_ae.npz')
 
     if df[oxides].isnull().any().any():
         df, _ = prep_df_ae(df)
@@ -543,8 +543,8 @@ def get_latent_space(df):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     current_dir = os.path.dirname(__file__)
-    # model_path = os.path.join(current_dir, 'ae_best_model.pt')
-    model_path = os.path.join(current_dir, 'ae_best_model_noP_tanh.pt')
+    model_path = os.path.join(current_dir, 'ae_best_model.pt')
+    # model_path = os.path.join(current_dir, 'ae_best_model_noP_tanh.pt')
     model = Tanh_Autoencoder(input_dim=10, hidden_layer_sizes=(256, 64, 16)).to(device)
     optimizer = torch.optim.Adam(model.parameters(), lr=5e-4, weight_decay=0)
     load_model(model, optimizer, model_path)