diff --git a/src/Class_Mod/LWPLSR_.py b/src/Class_Mod/LWPLSR_.py
index 877ca504ebe13bf7b39ad942cba3e6ad80c3f097..d16d187497284ba00860aa75ee9a6fd977954254 100644
--- a/src/Class_Mod/LWPLSR_.py
+++ b/src/Class_Mod/LWPLSR_.py
@@ -1,29 +1,31 @@
from juliacall import Main as jl
import numpy as np
import pandas as pd
+from sklearn.model_selection import KFold
class LWPLSR:
+ """The lwpls regression model from Jchemo (M. Lesnoff)
+
+ Returns:
+ self.scores (DataFrame): various metrics and scores
+ self.predicted_results_on_train (DataFrame):
+ self.predicted_results_on_cv (DataFrame):
+ self.predicted_results_on_test (DataFrame):
+ self.mod (Julia model): the prepared model
"""
- The lwpls regression model from Jchemo (M. Lesnoff)
- """
- def __init__(self, x_train, y_train, x_test, y_test):
+ def __init__(self, x_train, y_train, x_test, y_test, x_train_cv1, y_train_cv1, x_test_cv1, y_test_cv1, x_train_cv2, y_train_cv2, x_test_cv2, y_test_cv2, x_train_cv3, y_train_cv3, x_test_cv3, y_test_cv3):
"""Initiate the LWPLSR and prepare data for Julia computing."""
+
self.x_train, self.y_train, self.x_test, self.y_test = x_train, y_train, x_test, y_test
+ self.x_train_cv1, self.y_train_cv1, self.x_test_cv1, self.y_test_cv1 = x_train_cv1, y_train_cv1, x_test_cv1, y_test_cv1
+ self.x_train_cv2, self.y_train_cv2, self.x_test_cv2, self.y_test_cv2 = x_train_cv2, y_train_cv2, x_test_cv2, y_test_cv2
+ self.x_train_cv3, self.y_train_cv3, self.x_test_cv3, self.y_test_cv3 = x_train_cv3, y_train_cv3, x_test_cv3, y_test_cv3
# prepare to send dataframes to julia and Jchemo
jl.x_train, jl.y_train, jl.x_test, jl.y_test = self.x_train, self.y_train, self.x_test, self.y_test
- # Pre-treatment of x_train and x_test
- # jl.seval("""
- # # using DataFrames
- # # using Pandas
- # using Jchemo
- # mod1 = Jchemo.model(snv; centr = true, scal = true)
- # mod2 = Jchemo.model(savgol; npoint = 15, deriv = 1, degree = 2)
- # mod = Jchemo.pip(mod1, mod2)
- # Jchemo.fit!(mod, x_train)
- # x_train = Jchemo.transf(mod1, x_train)
- # Jchemo.fit!(mod, x_test)
- # x_test = Jchemo.transf(mod1, x_test)
- # """)
+ jl.x_train_cv1, jl.y_train_cv1, jl.x_test_cv1, jl.y_test_cv1 = self.x_train_cv1, self.y_train_cv1, self.x_test_cv1, self.y_test_cv1
+ jl.x_train_cv2, jl.y_train_cv2, jl.x_test_cv2, jl.y_test_cv2 = self.x_train_cv2, self.y_train_cv2, self.x_test_cv2, self.y_test_cv2
+ jl.x_train_cv3, jl.y_train_cv3, jl.x_test_cv3, jl.y_test_cv3= self.x_train_cv3, self.y_train_cv3, self.x_test_cv3, self.y_test_cv3
+ # optimize lwplsr parameters with Jchemo
# jl.seval("""
# ntrain = nro(x_train)
# segm = segmkf(ntrain, 4; rep = 5)
@@ -36,17 +38,22 @@ class LWPLSR:
# initialize vars from the class
y_shape = y_test.shape
- self.scores = pd.DataFrame
+ y_shape_cv1 = y_test_cv1.shape
+ y_shape_cv2 = y_test_cv2.shape
+ y_shape_cv3 = y_test_cv3.shape
+ # self.scores = pd.DataFrame
self.predicted_results_on_test = pd.DataFrame
self.predicted_results_on_train = pd.DataFrame
self.predicted_results_on_cv = pd.DataFrame
self.pred_test = np.zeros(shape=(y_shape[0], 1))
self.pred_train = np.zeros(shape=(y_shape[0], 1))
- self.pred_cv = np.zeros(shape=(y_shape[0], 1))
+ self.pred_cv1 = np.zeros(shape=(y_shape_cv1[0], 1))
+ self.pred_cv2 = np.zeros(shape=(y_shape_cv2[0], 1))
+ self.pred_cv3 = np.zeros(shape=(y_shape_cv3[0], 1))
self.mod = ""
def Jchemo_lwplsr_fit(self):
- """Send data to Julia to compute lwplsr.
+ """Send data to Julia to fit lwplsr.
Args:
self.jl.x_train (DataFrame):
@@ -55,8 +62,7 @@ class LWPLSR:
self.jl.y_test (DataFrame):
Returns:
- self.scores (DataFrame): various metrics and scores
- self.predicted_results_on_test (DataFrame):
+ self.mod (Julia model): the prepared model
"""
# launch Julia Jchemo lwplsr
jl.seval("""
@@ -67,6 +73,18 @@ class LWPLSR:
y_train |> Pandas.DataFrame |> DataFrames.DataFrame
x_test |> Pandas.DataFrame |> DataFrames.DataFrame
y_test |> Pandas.DataFrame |> DataFrames.DataFrame
+ x_train_cv1 |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_train_cv1 |> Pandas.DataFrame |> DataFrames.DataFrame
+ x_test_cv1 |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_test_cv1 |> Pandas.DataFrame |> DataFrames.DataFrame
+ x_train_cv2 |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_train_cv2 |> Pandas.DataFrame |> DataFrames.DataFrame
+ x_test_cv2 |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_test_cv2 |> Pandas.DataFrame |> DataFrames.DataFrame
+ x_train_cv3 |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_train_cv3 |> Pandas.DataFrame |> DataFrames.DataFrame
+ x_test_cv3 |> Pandas.DataFrame |> DataFrames.DataFrame
+ y_test_cv3 |> Pandas.DataFrame |> DataFrames.DataFrame
""")
# Create LWPLSR model and fit
jl.seval("""
@@ -76,6 +94,18 @@ class LWPLSR:
# Fit model
Jchemo.fit!(mod, x_train, y_train)
""")
+ # CV model and fit
+ jl.seval("""
+ nlvdis = 5 ; metric = :mah
+ h = 1 ; k = 200 ; nlv = 15 #; scal = true
+ mod_cv1 = mod
+ mod_cv2 = mod
+ mod_cv3 = mod
+ # Fit model
+ Jchemo.fit!(mod_cv1, x_train_cv1, y_train_cv1)
+ Jchemo.fit!(mod_cv2, x_train_cv2, y_train_cv2)
+ Jchemo.fit!(mod_cv3, x_train_cv3, y_train_cv3)
+ """)
# jl.seval("""
# mod = Jchemo.model(Jchemo.lwplsr)
@@ -87,77 +117,107 @@ class LWPLSR:
self.mod = jl.mod
def Jchemo_lwplsr_predict(self):
+ """Send data to Julia to predict with lwplsr.
+
+ Args:
+ self.mod (Julia model): the prepared model
+ self.jl.x_train (DataFrame):
+ self.jl.y_train (DataFrame):
+ self.jl.x_test (DataFrame):
+ self.jl.y_test (DataFrame):
+
+ Returns:
+ self.pred_test (Julia DataFrame): predicted values on x_test
+ self.pred_train (Julia DataFrame): predicted values on x_train
+ self.pred_cv (Julia DataFrame): predicted values on x_train with Cross-Validation
+ """
# Predictions on x_test and store in self.pred
self.pred_test = jl.seval("""
+ println("start test predict")
res = Jchemo.predict(mod, x_test)
res.pred
""")
self.pred_train = jl.seval("""
+ println("start train predict")
res = Jchemo.predict(mod, x_train)
res.pred
""")
- self.pred_cv = self.pred_train
-
+ self.pred_cv1 = jl.seval("""
+ println("start test_cv1 predict")
+ res = Jchemo.predict(mod_cv1, x_test_cv1)
+ res.pred
+ """)
+ self.pred_cv2 = jl.seval("""
+ println("start test_cv2 predict")
+ res = Jchemo.predict(mod_cv2, x_test_cv2)
+ res.pred
+ """)
+ self.pred_cv3 = jl.seval("""
+ println("start test_cv3 predict")
+ res = Jchemo.predict(mod_cv3, x_test_cv3)
+ res.pred
+ """)
@property
def pred_data_(self):
# convert predicted data from x_test to Pandas DataFrame
self.predicted_results_on_test = pd.DataFrame(self.pred_test)
self.predicted_results_on_train = pd.DataFrame(self.pred_train)
- # self.predicted_results_on_cv = pd.DataFrame(self.pred_cv)
- self.predicted_results_on_cv = pd.DataFrame(self.pred_train)
- return self.predicted_results_on_train, self.predicted_results_on_cv, self.predicted_results_on_test
+ self.predicted_results_on_cv1 = pd.DataFrame(self.pred_cv1)
+ self.predicted_results_on_cv2 = pd.DataFrame(self.pred_cv2)
+ self.predicted_results_on_cv3 = pd.DataFrame(self.pred_cv3)
+ return self.predicted_results_on_train, self.predicted_results_on_cv1, self.predicted_results_on_cv2, self.predicted_results_on_cv3, self.predicted_results_on_test
@property
def model_(self):
return self.mod
- @property
- def metrics_(self):
- jl.pred_test = self.pred_test
- jl.seval("""
- using Jchemo
- """)
- scorermsep_test = jl.seval("""
- first(Jchemo.rmsep(pred_test, y_test))
- """)
- scoremr2_test = jl.seval("""
- first(Jchemo.r2(pred_test, y_test))
- """)
- scorerpd_test = jl.seval("""
- first(Jchemo.rpd(pred_test, y_test))
- """)
- scoremsep_test = jl.seval("""
- first(Jchemo.sep(pred_test, y_test))
- """)
- jl.pred_train = self.pred_train
- scorermsep_train = jl.seval("""
- first(Jchemo.rmsep(pred_train, y_train))
- """)
- scoremr2_train = jl.seval("""
- first(Jchemo.r2(pred_train, y_train))
- """)
- scorerpd_train = jl.seval("""
- first(Jchemo.rpd(pred_train, y_train))
- """)
- scoremsep_train = jl.seval("""
- first(Jchemo.sep(pred_train, y_train))
- """)
- jl.pred_cv = self.pred_cv
- scorermsep_cv = jl.seval("""
- first(Jchemo.rmsep(pred_cv, y_train))
- """)
- scoremr2_cv = jl.seval("""
- first(Jchemo.r2(pred_cv, y_train))
- """)
- scorerpd_cv = jl.seval("""
- first(Jchemo.rpd(pred_cv, y_train))
- """)
- scoremsep_cv = jl.seval("""
- first(Jchemo.sep(pred_cv, y_train))
- """)
-
-
- self.scores = pd.DataFrame([[scoremr2_test, scorermsep_test, scoremsep_test, scorerpd_test]], columns=['r2', 'rmsep', 'msep', 'rpd'], index=['test'])
- self.scores = pd.concat([self.scores, pd.DataFrame([[scoremr2_train, scorermsep_train, scoremsep_train, scorerpd_train]], columns=['r2', 'rmsep', 'msep', 'rpd'], index = ["train"]), pd.DataFrame([[scoremr2_cv, scorermsep_cv, scoremsep_cv, scorerpd_cv]], columns=['r2', 'rmsep', 'msep', 'rpd'], index = ["cv"])])#
- return self.scores
+ # @property
+ # def metrics_(self):
+ # jl.pred_test = self.pred_test
+ # jl.seval("""
+ # using Jchemo
+ # """)
+ # scorermsep_test = jl.seval("""
+ # first(Jchemo.rmsep(pred_test, y_test))
+ # """)
+ # scoremr2_test = jl.seval("""
+ # first(Jchemo.r2(pred_test, y_test))
+ # """)
+ # scorerpd_test = jl.seval("""
+ # first(Jchemo.rpd(pred_test, y_test))
+ # """)
+ # scoremsep_test = jl.seval("""
+ # first(Jchemo.sep(pred_test, y_test))
+ # """)
+ # jl.pred_train = self.pred_train
+ # scorermsep_train = jl.seval("""
+ # first(Jchemo.rmsep(pred_train, y_train))
+ # """)
+ # scoremr2_train = jl.seval("""
+ # first(Jchemo.r2(pred_train, y_train))
+ # """)
+ # scorerpd_train = jl.seval("""
+ # first(Jchemo.rpd(pred_train, y_train))
+ # """)
+ # scoremsep_train = jl.seval("""
+ # first(Jchemo.sep(pred_train, y_train))
+ # """)
+ # jl.pred_cv = self.pred_cv
+ # scorermsep_cv = jl.seval("""
+ # first(Jchemo.rmsep(pred_cv, y_train))
+ # """)
+ # scoremr2_cv = jl.seval("""
+ # first(Jchemo.r2(pred_cv, y_train))
+ # """)
+ # scorerpd_cv = jl.seval("""
+ # first(Jchemo.rpd(pred_cv, y_train))
+ # """)
+ # scoremsep_cv = jl.seval("""
+ # first(Jchemo.sep(pred_cv, y_train))
+ # """)
+ #
+ #
+ # self.scores = pd.DataFrame([[scoremr2_test, scorermsep_test, scoremsep_test, scorerpd_test]], columns=['r2', 'rmsep', 'msep', 'rpd'], index=['test'])
+ # self.scores = pd.concat([self.scores, pd.DataFrame([[scoremr2_train, scorermsep_train, scoremsep_train, scorerpd_train]], columns=['r2', 'rmsep', 'msep', 'rpd'], index = ["train"]), pd.DataFrame([[scoremr2_cv, scorermsep_cv, scoremsep_cv, scorerpd_cv]], columns=['r2', 'rmsep', 'msep', 'rpd'], index = ["cv"])])#
+ # return self.scores
diff --git a/src/Class_Mod/LWPLSR_Call.py b/src/Class_Mod/LWPLSR_Call.py
index 47bf8c82ef890af6db341d1f359504064d15e421..3d8523b655c09df10e65f14637491e1687179e54 100644
--- a/src/Class_Mod/LWPLSR_Call.py
+++ b/src/Class_Mod/LWPLSR_Call.py
@@ -5,23 +5,24 @@ from LWPLSR_ import LWPLSR
# loading the lwplsr_inputs.json
temp_path = Path("temp/")
-for i in ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']:
+for i in ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']:#,'x_train_np_cv1', 'y_train_np_cv1', 'x_test_np_cv1', 'y_test_np_cv1', 'x_train_np_cv2', 'y_train_np_cv2', 'x_test_np_cv2', 'y_test_np_cv2', 'x_train_np_cv3', 'y_train_np_cv3', 'x_test_np_cv3', 'y_test_np_cv3',]:
globals()[i] = np.genfromtxt(temp_path / str(i + ".csv"), delimiter=',')
print('CSV imported')
print('start model creation')
-Reg = LWPLSR(x_train_np, y_train_np, x_test_np, y_test_np)
+Reg = LWPLSR(x_train_np, y_train_np, x_test_np, y_test_np, x_train_np_cv1, y_train_np_cv1, x_test_np_cv1, y_test_np_cv1, x_train_np_cv2, y_train_np_cv2, x_test_np_cv2, y_test_np_cv2, x_train_np_cv3, y_train_np_cv3, x_test_np_cv3, y_test_np_cv3)
print('model created. \n now fit')
LWPLSR.Jchemo_lwplsr_fit(Reg)
print('now predict')
LWPLSR.Jchemo_lwplsr_predict(Reg)
json_export = {}
-data_to_export = ['model', 'pred_data', 'metrics']
json_export['pred_data_train'] = Reg.pred_data_[0].to_dict()
-json_export['pred_data_cv'] = Reg.pred_data_[1].to_dict()
-json_export['pred_data_test'] = Reg.pred_data_[2].to_dict()
-json_export['metrics'] = Reg.metrics_.to_dict()
+json_export['pred_data_cv1'] = Reg.pred_data_[1].to_dict()
+json_export['pred_data_cv2'] = Reg.pred_data_[2].to_dict()
+json_export['pred_data_cv3'] = Reg.pred_data_[3].to_dict()
+json_export['pred_data_test'] = Reg.pred_data_[4].to_dict()
json_export['model'] = str(Reg.model_)
+# json_export['metrics'] = Reg.metrics_.to_dict()
with open(temp_path / "lwplsr_outputs.json", "w+") as outfile:
json.dump(json_export, outfile)
print(Reg.metrics_)
\ No newline at end of file
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index cde267b0c88d018f5ac8c0c6049ef380bdd83b99..2556511d54dbe58dbbe768ea857a347182c62cfa 100644
--- a/src/pages/2-model_creation.py
+++ b/src/pages/2-model_creation.py
@@ -140,8 +140,9 @@ if not spectra.empty and not y.empty:
reg_model = Reg.model_
#M2.dataframe(Pin.pred_data_)
elif regression_algo == reg_algo[2]:
- data_to_work_with = ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']
+ data_to_work_with = ['x_train_np', 'y_train_np', 'x_test_np', 'y_test_np']#,'x_train_np_cv1', 'y_train_np_cv1', 'x_test_np_cv1', 'y_test_np_cv1', 'x_train_np_cv2', 'y_train_np_cv2', 'x_test_np_cv2', 'y_test_np_cv2', 'x_train_np_cv3', 'y_train_np_cv3', 'x_test_np_cv3', 'y_test_np_cv3',]
x_train_np, y_train_np, x_test_np, y_test_np = X_train.to_numpy(), y_train.to_numpy(), X_test.to_numpy(), y_test.to_numpy()
+ # x_train_np_cv1, y_train_np_cv1, x_test_np_cv1, y_test_np_cv1, x_train_np_cv2, y_train_np_cv2, x_test_np_cv2, y_test_np_cv2, x_train_np_cv3, y_train_np_cv3, x_test_np_cv3, y_test_np_cv3, = X_train_cv1.to_numpy(), y_train_cv1.to_numpy(), X_test_cv1.to_numpy(), y_test_cv1.to_numpy(), X_train_cv2.to_numpy(), y_train_cv2.to_numpy(), X_test_cv2.to_numpy(), y_test_cv2.to_numpy(), X_train_cv3.to_numpy(), y_train_cv3.to_numpy(), X_test_cv3.to_numpy(), y_test_cv3.to_numpy()
temp_path = Path('temp/')
for i in data_to_work_with: np.savetxt(temp_path / str(i + ".csv"), vars()[i], delimiter=",")
import subprocess
@@ -151,13 +152,17 @@ if not spectra.empty and not y.empty:
Reg_json = json.load(outfile)
for i in data_to_work_with: os.unlink(temp_path / str(i + ".csv"))
os.unlink(temp_path / "lwplsr_outputs.json")
- Reg = type('obj', (object,), {'metrics_' : pd.json_normalize(Reg_json['metrics']), 'pred_data_' : [pd.json_normalize(Reg_json['pred_data_train']), pd.json_normalize(Reg_json['pred_data_cv']),pd.json_normalize(Reg_json['pred_data_test'])]})
+ Reg = type('obj', (object,), {'model' : pd.json_normalize(Reg_json['model']), 'pred_data_' : [pd.json_normalize(Reg_json['pred_data_train']), pd.json_normalize(Reg_json['pred_data_cv1']), pd.json_normalize(Reg_json['pred_data_cv2']), pd.json_normalize(Reg_json['pred_data_cv3']), pd.json_normalize(Reg_json['pred_data_test'])]})
Reg.pred_data_[0] = Reg.pred_data_[0].T.reset_index().drop(columns = ['index'])
Reg.pred_data_[0].index = list(y_train.index)
- Reg.pred_data_[1] = Reg.pred_data_[1].T.reset_index().drop(columns = ['index'])
- Reg.pred_data_[1].index = list(y_train.index)
- Reg.pred_data_[2] = Reg.pred_data_[2].T.reset_index().drop(columns = ['index'])
- Reg.pred_data_[2].index = list(y_test.index)
+ # Reg.pred_data_[1] = Reg.pred_data_[1].T.reset_index().drop(columns = ['index'])
+ # Reg.pred_data_[1].index = list(y_train_cv1.index)
+ # Reg.pred_data_[2] = Reg.pred_data_[2].T.reset_index().drop(columns = ['index'])
+ # Reg.pred_data_[2].index = list(y_train_cv2.index)
+ # Reg.pred_data_[3] = Reg.pred_data_[3].T.reset_index().drop(columns = ['index'])
+ # Reg.pred_data_[3].index = list(y_train_cv3.index)
+ Reg.pred_data_[4] = Reg.pred_data_[4].T.reset_index().drop(columns = ['index'])
+ Reg.pred_data_[4].index = list(y_test.index)
elif regression_algo == reg_algo[3]:
s = M1.number_input(label='Enter the maximum number of intervals', min_value=1, max_value=6, value=3)