From e7d02da16d32ee645a3ae2f4c8ad82ba91bcb7b0 Mon Sep 17 00:00:00 2001
From: BARTHES Nicolas <nicolas.barthes@cefe.cnrs.fr>
Date: Fri, 1 Mar 2024 15:32:20 +0100
Subject: [PATCH] Delete pinard_test.py
---
pinard_test.py | 101 -------------------------------------------------
1 file changed, 101 deletions(-)
delete mode 100644 pinard_test.py
diff --git a/pinard_test.py b/pinard_test.py
deleted file mode 100644
index 405718b..0000000
--- a/pinard_test.py
+++ /dev/null
@@ -1,101 +0,0 @@
-print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
-
-
-# ## Learning
-
-# In[111]:
-
-
-
-
-
-# In[112]:
-
-
-preprocessing
-
-
-# In[113]:
-
-
-# Declare complete pipeline
-pipeline = Pipeline([
- ('scaler', MinMaxScaler()), # scaling the data
- ('preprocessing', FeatureUnion(preprocessing)), # preprocessing
- # Pipeline([('sg1',pp.SavitzkyGolay()),('sg2',pp.SavitzkyGolay())]),
- # ('sg1',pp.SavitzkyGolay()),('sg2',pp.SavitzkyGolay()),
- # preprocessing - nested pipeline to perform the Savitzky-Golay method twice for 2nd order preprocessing
- ('PLS', PLSRegression()) # regressor
-])
-
-
-# In[114]:
-
-
-pipeline
-
-
-# In[115]:
-
-
-# Estimator including y values scaling
-estimator = TransformedTargetRegressor(regressor = pipeline, transformer = MinMaxScaler())
-
-
-# In[116]:
-
-
-estimator
-
-
-# In[117]:
-
-
-# Training
-estimator.fit(X_train, y_train)
-
-
-# In[110]:
-
-
-estimator.score(X_test,y_test)
-
-
-# In[ ]:
-
-
-# Predictions
-Y_preds = estimator.predict(X_test) # make predictions on test data and assign to Y_preds variable
-print("R²", r2_score(y_test, Y_preds))
-
-
-# ## Résultats de prédiction
-
-# In[ ]:
-
-
-print("MAE", mean_absolute_error(y_test, Y_preds))
-print("MSE", mean_squared_error(y_test, Y_preds))
-print("MAPE", mean_absolute_percentage_error(y_test, Y_preds))
-print("R²", r2_score(y_test, Y_preds))
-# print(estimator.get_params())
-
-
-# ## Cross Validation
-
-# In[ ]:
-
-
-print("CV_scores", cross_val_score(estimator, x, y, cv=3))
-print("-- CV predict --")
-Y_preds = cross_val_predict(estimator, x, y, cv=3)
-print("MAE", mean_absolute_error(y, Y_preds))
-print("MSE", mean_squared_error(y, Y_preds))
-print("MAPE", mean_absolute_percentage_error(y, Y_preds))
-print("R²", r2_score(y, Y_preds))
-
-print("-- Cross Validate --")
-cv_results = cross_validate(estimator, x, y, cv=3, return_train_score=True, n_jobs=3)
-for key in cv_results.keys():
- print(key, cv_results[key])
-
--
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