diff --git a/src/Class_Mod/UMAP_.py b/src/Class_Mod/UMAP_.py
index 05ee5b3f11059363f73b3abe813abf12afb347fb..28d0436e6efe90fb251e60627234376cc10467d0 100644
--- a/src/Class_Mod/UMAP_.py
+++ b/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):
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index 7c24b46a9a4b9b56c18502ee3fa10b11e72a8c0a..df7d4ec20b8b021f7d45f0c6a2d83ab1978857cd 100644
--- a/src/pages/1-samples_selection.py
+++ b/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]:
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index e83931ea53a0309b63756ca9818117bd46ae64db..996302ad78e801c5b6a606064699e0ea878f3a9b 100644
--- a/src/pages/2-model_creation.py
+++ b/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="âš ï¸")
os.unlink(tmp_path)
### split the data