From 41e614bc51c4846cdf1a5089bcbb35985d9f522f Mon Sep 17 00:00:00 2001
From: DIANE <abderrahim.diane@cefe.cnrs.fr>
Date: Tue, 16 Apr 2024 09:53:41 +0200
Subject: [PATCH] NaN values in .dx
---
Class_Mod/DxReader.py | 3 ++-
Class_Mod/Miscellaneous.py | 2 +-
Class_Mod/Regression_metrics.py | 4 +---
pages/2-model_creation.py | 16 ++++++++--------
4 files changed, 12 insertions(+), 13 deletions(-)
diff --git a/Class_Mod/DxReader.py b/Class_Mod/DxReader.py
index f024894..d094294 100644
--- a/Class_Mod/DxReader.py
+++ b/Class_Mod/DxReader.py
@@ -49,7 +49,7 @@ class DxRead:
self.metadata_ = pd.DataFrame(self.__met).T
- self.spectra = pd.DataFrame(np.fliplr(specs), columns= self.__wl[::-1]) # Storing spectra in a pd.dataframe
+ self.spectra = pd.DataFrame(np.fliplr(specs), columns= self.__wl[::-1], index = self.metadata_['name']) # Storing spectra in a pd.dataframe
@@ -72,6 +72,7 @@ class DxRead:
### dataframe conntaining chemical data
self.chem_data = pd.DataFrame(cc, index=elements_name).T.astype(float)
+ self.chem_data.index = self.metadata_['name']
### Method for retrieving the concentration of a single sample
def conc(self,sample):
diff --git a/Class_Mod/Miscellaneous.py b/Class_Mod/Miscellaneous.py
index 79d1708..e1b1271 100644
--- a/Class_Mod/Miscellaneous.py
+++ b/Class_Mod/Miscellaneous.py
@@ -27,7 +27,7 @@ def reg_plot( meas, pred):
sns.regplot(x = meas[0] , y = pred[0], color='blue', label = 'Calib')
sns.regplot(x = meas[1], y = pred[1], color='red', label = 'CV')
sns.regplot(x = meas[2], y = pred[2], color='green', label = 'Test')
- plt.plot([np.min(meas[0])+0.1, np.max([meas[0]])+0.1], [np.min(meas[0])+0.1, np.max([meas[0]])+0.1], color = 'black')
+ plt.plot([np.min(meas[0])-0.05, np.max([meas[0]])+0.05], [np.min(meas[0])-0.05, np.max([meas[0]])+0.05], color = 'black')
ax.set_ylabel('Predicted values')
ax.set_xlabel('Measured values')
plt.legend()
diff --git a/Class_Mod/Regression_metrics.py b/Class_Mod/Regression_metrics.py
index f958d8c..5d7cbe3 100644
--- a/Class_Mod/Regression_metrics.py
+++ b/Class_Mod/Regression_metrics.py
@@ -3,8 +3,7 @@ from Packages import *
class metrics:
def __init__(self, meas, pred):
-
- if isinstance(meas, pd.DataFrame):
+ if isinstance(meas, pd.DataFrame) or isinstance(meas, pd.Series):
self.meas = meas.to_numpy()
else :
self.meas = meas.ravel()
@@ -28,7 +27,6 @@ class metrics:
# REGRESSION OR MODEL
ssm = np.sum(self.pred - xbar)
-
# Compute statistical metrics
metr = pd.DataFrame()
diff --git a/pages/2-model_creation.py b/pages/2-model_creation.py
index 3f506ea..adea651 100644
--- a/pages/2-model_creation.py
+++ b/pages/2-model_creation.py
@@ -72,22 +72,22 @@ elif file == files_format[1]:
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)
- spectra = spectra
- y = chem_data.loc[:,yname]
-
+ measured = chem_data.loc[:,yname] > 0
+ y = chem_data.loc[:,yname].loc[measured]
+ spectra = spectra.loc[measured]
os.unlink(tmp_path)
### split the data
if not spectra.empty and not y.empty:
rd_seed = M1.slider("Customize Train-test split", min_value=1, max_value=100, value=42, format="%i")
# Split data into training and test sets using the kennard_stone method and correlation metric, 25% of data is used for testing
- train_index, test_index = train_test_split_idx(spectra, y=y, method="kennard_stone", metric="correlation", test_size=0.25, random_state=rd_seed)
- # Assign data to training and test sets
- X_train, y_train, X_test, y_test = pd.DataFrame(spectra.iloc[train_index,:]), pd.DataFrame(y.iloc[train_index]), pd.DataFrame(spectra.iloc[test_index,:]), pd.DataFrame(y.iloc[test_index])
- y_train = y_train.iloc[:,0]
- y_test = y_test.iloc[:,0]
+ train_index, test_index = train_test_split_idx(spectra, y = y, method="kennard_stone", metric="correlation", test_size=0.25, random_state=rd_seed)
+ # Assign data to training and test sets
+ X_train, y_train = pd.DataFrame(spectra.iloc[train_index,:]), y.iloc[train_index]
+ X_test, y_test = pd.DataFrame(spectra.iloc[test_index,:]), y.iloc[test_index]
+
#######################################
regression_algo = M1.selectbox("Choose the algorithm for regression", options=reg_algo, key = 12)
if regression_algo == reg_algo[1]:
--
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