Merge branch 'master' of https://src.koda.cnrs.fr/cefe/pace/nirs_workflow

src/Class_Mod/UMAP_.py

@@ -6,8 +6,7 @@ class Umap:
     """
     The UMAP dimension reduction algorithm from scikit learn
     """
-    def __init__(self, data_import, numerical_data, cat_data):
-        self.x = data_import
+    def __init__(self, numerical_data, cat_data):
         self.numerical_data = numerical_data
         if cat_data is None:
             self.categorical_data_encoded = cat_data
@@ -21,7 +20,7 @@ class Umap:
         self.model = UMAP(n_neighbors=20, n_components=3, min_dist=0.0, random_state=42,)
         self.model.fit(self.numerical_data, y = self.categorical_data_encoded)
         self.scores_raw = self.model.transform(self.numerical_data)
-        self.scores = pd.DataFrame(self.scores_raw, index = self.x.index)
+        self.scores = pd.DataFrame(self.scores_raw, index = self.numerical_data.index)
 
     @property
     def scores_(self):

src/pages/1-samples_selection.py

@@ -109,7 +109,7 @@ if not spectra.empty:
             supervised = meta_data[col]
         else:
             supervised = None
-        dr_model = Umap(data_import = imp, numerical_data = MinMaxScale(spectra), cat_data = supervised)
+        dr_model = Umap(numerical_data = MinMaxScale(spectra), cat_data = supervised)
 
     if dr_model:
         axis1 = pc.selectbox("x-axis", options = dr_model.scores_.columns, index=0)
@@ -240,7 +240,7 @@ if not t.empty:
         
         plt.savefig("./Report/Figures/test.png")
         st.plotly_chart(fig, use_container_width=True)
-        
+
         import plotly.express as px
 
         if labels:
@@ -269,29 +269,7 @@ if not t.empty:
             fig_axe2_axe3.update_traces(marker=dict(size=4))
             fig_axe2_axe3.write_image("./Report/Figures/plot_axe2_axe3.png")
 
-        else :
-
-            fig_axe1_axe2 = px.scatter(tcr, x=axis1, y=axis2, color=labels if list(labels) else None)
-            fig_axe1_axe2.update_layout(title='Axe1-Axe2')
-            fig_axe1_axe2.update_traces(marker=dict(size=4))
-            fig_axe1_axe2.write_image("./Report/Figures/plot_axe1_axe2.png")
-
-
-            # Créer et exporter le graphique Axe1-Axe3 en PNG
-            fig_axe1_axe3 = px.scatter(tcr, x=axis1, y=axis3, color=labels if list(labels) else None)
-            fig_axe1_axe3.update_layout(title='Axe1-Axe3')
-            fig_axe1_axe3.update_traces(marker=dict(size=4))
-            fig_axe1_axe3.write_image("./Report/Figures/plot_axe1_axe3.png")
-
-
-            # Créer et exporter le graphique Axe2-Axe3 en PNG
-            fig_axe2_axe3 = px.scatter(tcr, x=axis2, y=axis3, color=labels if list(labels) else None)
-            fig_axe2_axe3.update_layout(title='Axe2-Axe3')
-            fig_axe2_axe3.update_traces(marker=dict(size=4))
-            fig_axe2_axe3.write_image("./Report/Figures/plot_axe2_axe3.png")            
-
 
-#################################################################
 if not spectra.empty:
     if dim_red_method == dim_red_methods[1]:
 

src/pages/2-model_creation.py

@@ -19,10 +19,8 @@ st.session_state["interface"] = st.session_state.get('interface')
 if st.session_state["interface"] == 'simple':
     hide_pages("Predictions")
 
-########################################################################################
-reg_algo = ["","Full-PLSR", "Locally Weighted PLSR", "Interval-PLSR", "Full-PLSR-sklearn", "PrePLStester"]
 
-# page Design
+    ####################################### page Design #######################################
 st.header("Calibration Model Development", divider='blue')
 st.write("Create a predictive model, then use it for predicting your target variable (chemical values) from NIRS spectra")
 M1, M2, M3 = st.columns([2,3,2])
@@ -35,12 +33,15 @@ M7.write('Predicted vs Measured values')
 M8.write('Residuals plot')
 M9, M10 = st.columns([2,2])
 M9.write("-- Save the model --")
+            ######################################################################
+
 
+reg_algo = ["","Full-PLSR", "Locally Weighted PLSR", "Interval-PLSR", "Full-PLSR-sklearn", "PrePLStester"]
+      #######################################        ###########################################
 
 files_format = ['.csv', '.dx']
 file = M3.radio('select data file format:', options = files_format)
 
-
 ### Data
 spectra = pd.DataFrame
 y = pd.DataFrame
@@ -57,14 +58,11 @@ if file == files_format[0]:
         else: col = False
         
     ycal_csv = M3.file_uploader("Select corresponding Chemical Data", type="csv", help=" :mushroom: select a csv matrix with samples as rows and chemical values as a column")
-        
-            
     if ycal_csv:
         sepy = M3.radio("separator (Y file): ", options=[";", ","], key=2)
         hdry = M3.radio("samples name (Y file)?: ", options=["no", "yes"], key=3)
         if hdry == "yes": col = 0
         else: col = False
-        
     
     if xcal_csv and ycal_csv:
         spectra, meta_data = col_cat(pd.read_csv(xcal_csv, decimal='.', sep=sepx, index_col=col, header=0))
@@ -77,13 +75,11 @@ if file == files_format[0]:
 
         if spectra.shape[0] == y.shape[0]:
             pass
+
         else:
             M3.warning('The number of samples is different in X and Y')
             y = pd.DataFrame
             spectra = pd.DataFrame
-            
-            
-
 
 ## Load .dx file
 elif file == files_format[1]:
@@ -94,10 +90,13 @@ elif file == files_format[1]:
             tmp_path = tmp.name
             chem_data, spectra, meta_data = read_dx(file =  tmp_path)
             M3.success("The data have been loaded successfully", icon="✅")
-            yname = M3.selectbox('Select target', options=chem_data.columns)
-            measured = chem_data.loc[:,yname] > 0
-            y = chem_data.loc[:,yname].loc[measured]
-            spectra = spectra.loc[measured]
+            if chem_data.shape[1]>0:
+                yname = M3.selectbox('Select target', options=chem_data.columns)
+                measured = chem_data.loc[:,yname] > 0
+                y = chem_data.loc[:,yname].loc[measured]
+                spectra = spectra.loc[measured]
+            else:
+                M3.warning('Warning: Chemical data are not included in your file !', icon="⚠️")
         os.unlink(tmp_path)
 
 ### split the data