Delete pinard_test.py

pinard_test.py

-print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
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-# ## Learning
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-# In[111]:
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-# In[112]:
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-preprocessing
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-# In[113]:
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-# 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
-])
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-# In[114]:
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-pipeline
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-# In[115]:
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-# Estimator including y values scaling
-estimator = TransformedTargetRegressor(regressor = pipeline, transformer = MinMaxScaler())
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-# In[116]:
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-estimator
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-# In[117]:
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-# Training
-estimator.fit(X_train, y_train)
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-# In[110]:
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-estimator.score(X_test,y_test)
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-# In[ ]:
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-# 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))
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-# ## Résultats de prédiction
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-# In[ ]:
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-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())
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-# ## Cross Validation
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-# In[ ]:
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-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))
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-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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