From fde1c5821b26b99172e8d351096c55d9d3a1c30c Mon Sep 17 00:00:00 2001
From: DIANE <abderrahim.diane@cefe.cnrs.fr>
Date: Fri, 30 Aug 2024 15:58:47 +0200
Subject: [PATCH] corrections
---
src/Report/report.py | 38 +++++++++++-----------
src/pages/1-samples_selection.py | 1 +
src/pages/3-prediction.py | 4 +--
src/pages/4-inputs.py | 55 ++++++++++++++++----------------
4 files changed, 50 insertions(+), 48 deletions(-)
diff --git a/src/Report/report.py b/src/Report/report.py
index c6443a3..ec88761 100644
--- a/src/Report/report.py
+++ b/src/Report/report.py
@@ -11,21 +11,21 @@ def intersect(l1, l2):
def check(file):
return os.path.isfile(file)
def report(*args):
- signal_preprocess = {'Snv':'Standard Normal Variate (SNV) \cite{barnes1989standard}',
- 'SG': 'Savitzky-Golay (SG) \cite{savitzky1964smoothing}'}
- dim_red_methods= {'PCA':'Principal Components Analysis (PCA) \cite{wold1987principal,ringner2008principal,greenacre2022principal,JMLR:v12:pedregosa11a}',
- 'UMAP':'Uniform Manifold Approximation and Projection (UMAP) \cite{ghojogh2021uniform,JMLR:v12:pedregosa11a}',
- 'NMF':'Non-negative Matrix Factorization (NMF) \cite{lopes2015non}'} # List of dimensionality reduction algos
- cluster_methods = {'Kmeans':'Kmeans \cite{chong2021k,JMLR:v12:pedregosa11a}',
- 'HDBSCAN':'Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) \cite{mcinnes2017hdbscan}',
- 'AP':'Affinity Propagation (AP) \cite{dueck2009affinity,JMLR:v12:pedregosa11a}',
- 'KS':'Kennard-Stone algorithm (KS)',
- 'RDM': 'random approach'} # List of clustering algos
+ signal_preprocess = {'Snv':r'''Standard Normal Variate (SNV) \cite{barnes1989standard}''',
+ 'SG': r'''Savitzky-Golay (SG) \cite{savitzky1964smoothing}'''}
+ dim_red_methods= {'PCA':r'''Principal Components Analysis (PCA) \cite{wold1987principal,ringner2008principal,greenacre2022principal,JMLR:v12:pedregosa11a}''',
+ 'UMAP':r'''Uniform Manifold Approximation and Projection (UMAP) \cite{ghojogh2021uniform,JMLR:v12:pedregosa11a}''',
+ 'NMF':r'''Non-negative Matrix Factorization (NMF) \cite{lopes2015non}'''} # List of dimensionality reduction algos
+ cluster_methods = {'Kmeans':r'''Kmeans \cite{chong2021k,JMLR:v12:pedregosa11a}''',
+ 'HDBSCAN':r'''Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) \cite{mcinnes2017hdbscan}''',
+ 'AP':r'''Affinity Propagation (AP) \cite{dueck2009affinity,JMLR:v12:pedregosa11a}''',
+ 'KS':r'''Kennard-Stone algorithm (KS)''',
+ 'RDM': r'''random approach'''} # List of clustering algos
selec_strategy = {'center':'PCA','random':'PCA'}
- reg_algo ={"PLS":'Partial Least Squares (PLS) \cite{Wold2001,JMLR:v12:pedregosa11a}',
- "LW-PLS": 'Locally Weighted-Partial Least Squares (LW-PLS) \cite{Lesnoff2020}',
- "TPE-iPLS": "Tree-structured Parzen estimator-interval Partial Least Squares (TPE-iPLS)"}
+ reg_algo ={"PLS":r'''Partial Least Squares (PLS) \cite{Wold2001,JMLR:v12:pedregosa11a}''',
+ "LW-PLS": r'''Locally Weighted-Partial Least Squares (LW-PLS) \cite{Lesnoff2020}''',
+ "TPE-iPLS": r'''Tree-structured Parzen estimator-interval Partial Least Squares (TPE-iPLS)'''}
to_report=[]
j=0
@@ -176,19 +176,19 @@ def report(*args):
large residual distance to the subspace, but whose projection is on the subspace; and, finally, bad leverage
points, which have a large residual distance such that the projection on the subspace is away from regular observations.\par"""
- latex_report += """ Results of applying this workflow are displayed in"""
+ latex_report += r""" Results of applying this workflow are displayed in"""
if 'PCA' in to_report:
- latex_report += """ (\cref{pcaplots, hotelling_and_influence, loadings})."""
+ latex_report += r""" (\cref{pcaplots, hotelling_and_influence, loadings})."""
elif 'NMF' in to_report:
- latex_report += """ (\cref{pcaplots, loadings})."""
+ latex_report += r""" (\cref{pcaplots, loadings})."""
else:
- latex_report += """ (\cref{pcaplots})."""
+ latex_report += r""" (\cref{pcaplots})."""
if 'KS' in to_report or 'RDM' in to_report:
- latex_report += """ Based of the features extracted using {"""+to_report[2]+ r"""}, """
+ latex_report += r""" Based of the features extracted using {"""+to_report[2]+ r"""}, """
else:
- latex_report += """ Based of the features extracted using {"""+to_report[2]+ r"""},
+ latex_report += r""" Based of the features extracted using {"""+to_report[2]+ r"""},
{"""+to_report[3]+ r"""} revealed the existance of {"""+to_report[5] + r"""} data clusters, visualized with different colors, from which """
latex_report += r"""a subset of {"""+to_report[8]+ r"""} samples was selected"""
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index 710da37..56d8a0d 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -414,6 +414,7 @@ if not t.empty:
st.write('Scores plot')
tcr_plot = tcr.copy()
colfilter = st.selectbox('Color by:', options= filter,format_func = lambda x: x if x else "<Select>")
+ p_hash(colfilter)
if colfilter in cluster_methods:
tcr_plot[colfilter] = labels
elif not meta_data.empty and colfilter in md_df_st_.columns.tolist():
diff --git a/src/pages/3-prediction.py b/src/pages/3-prediction.py
index bed4a0e..eb3cf20 100644
--- a/src/pages/3-prediction.py
+++ b/src/pages/3-prediction.py
@@ -158,8 +158,8 @@ try:
with M4:
@st.cache_data
def prep_info(change):
- SG = f'- Savitzky-Golay derivative parameters \:(Window_length:{prep['window_length']}; polynomial order: {prep['polyorder']}; Derivative order : {prep['deriv']})'
- Norm = f'- Spectral Normalization \: {norm}'
+ SG = r'''- Savitzky-Golay derivative parameters \:(Window_length:{prep['window_length']}; polynomial order: {prep['polyorder']}; Derivative order : {prep['deriv']})'''
+ Norm = r'''- Spectral Normalization \: {norm}'''
return SG, Norm
SG, Norm = prep_info(change = hash_)
st.info('The spectra were preprocessed using:\n'+SG+"\n"+Norm)
diff --git a/src/pages/4-inputs.py b/src/pages/4-inputs.py
index 472cde4..959f84d 100644
--- a/src/pages/4-inputs.py
+++ b/src/pages/4-inputs.py
@@ -69,30 +69,31 @@ with st.container():
}
submitted = st.form_submit_button(label='Save')
- if submitted:
- if '' not in form_data.values():
- # Save the form data here
- st.session_state['form_submitted'] = True
- st.success('Form was saved successfully!', icon="✅")
- # Enregistrement des données dans un fichier JSON
- with open('form_data.json', 'w') as json_file:
- json.dump(form_data, json_file)
-
- with st.container():
- if st.session_state['interface'] == 'simple':
- header3, header4 = st.columns(2)
- if header3.button("Samples Selection"):
- st.switch_page(pages_folder / '1-samples_selection.py')
- if header4.button("Model Creation"):
- st.switch_page(pages_folder / '2-model_creation.py')
- elif st.session_state['interface'] == 'advanced':
- header3, header4, header5 = st.columns(3)
- if header3.button("Samples Selection"):
- st.switch_page(pages_folder / '1-samples_selection.py')
- if header4.button("Model Creation"):
- st.switch_page(pages_folder / '2-model_creation.py')
- if header5.button("Prediction"):
- st.switch_page(pages_folder / '3-prediction.py')
-
- else:
- st.error('Error: The form was not saved, please ensure the required fields are filled!')
\ No newline at end of file
+ if submitted:
+ if '' not in form_data.values():
+ # Save the form data here
+ st.session_state['form_submitted'] = True
+ st.success('Form was saved successfully!', icon="✅")
+ # Enregistrement des données dans un fichier JSON
+ with open('form_data.json', 'w') as json_file:
+ json.dump(form_data, json_file)
+
+ if st.session_state['interface'] == 'simple':
+ header3, header4 = st.columns(2)
+ if header3.button("Samples Selection"):
+ st.switch_page(pages_folder / '1-samples_selection.py')
+ if header4.button("Model Creation"):
+ st.switch_page(pages_folder / '2-model_creation.py')
+ elif st.session_state['interface'] == 'advanced':
+ header3, header4, header5 = st.columns(3)
+ if header3.button("Samples Selection"):
+ st.switch_page(pages_folder / '1-samples_selection.py')
+ if header4.button("Model Creation"):
+ st.switch_page(pages_folder / '2-model_creation.py')
+ if header5.button("Prediction"):
+ st.switch_page(pages_folder / '3-prediction.py')
+
+ else:
+ st.error('Error: The form was not saved, please ensure the required fields are filled!')
+
+
--
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