diff --git a/src/Class_Mod/DATA_HANDLING.py b/src/Class_Mod/DATA_HANDLING.py
index ddce505fe8da9a984e8b5f8de6a74bfad1287708..892c0c0854533b346a4e2363a61408c4d114a4ae 100644
--- a/src/Class_Mod/DATA_HANDLING.py
+++ b/src/Class_Mod/DATA_HANDLING.py
@@ -154,3 +154,23 @@ class KF_CV:
for i in f.keys():
ycv[idx[i]] = f[i]
return ycv
+
+
+### Selectivity ratio
+def sel_ratio(model, x ):
+ from scipy.stats import f
+
+ x = pd.DataFrame(x)
+ wtp = model.coef_.T/ np.linalg.norm(model.coef_.T)
+ ttp = np.array(x @ wtp)
+ ptp = np.array(x.T) @ np.array(ttp)/(ttp.T @ ttp)
+ qexpi = np.linalg.norm(ttp @ ptp.T, axis = 0)**2
+ e = np.array(x-x.mean()) - ttp @ ptp.T
+ qres = np.linalg.norm(e, axis = 0)**2
+ sr = pd.DataFrame(qexpi/qres, index = x.columns, columns = ['sr'])
+
+ fcr = f.ppf(0.05, sr.shape[0]-2, sr.shape[0]-3)
+ c = sr > fcr
+ sr.index = np.arange(x.shape[1])
+ SR = sr.iloc[c.to_numpy(),:]
+ return SR
\ No newline at end of file
diff --git a/src/Class_Mod/RegModels.py b/src/Class_Mod/RegModels.py
index 18ce604d7737a8bffa14426eafd1bfe1160946b0..63e740d7f40a1ad9f4d55edd7865c242c16afd8e 100644
--- a/src/Class_Mod/RegModels.py
+++ b/src/Class_Mod/RegModels.py
@@ -1,5 +1,5 @@
from Packages import *
-from Class_Mod import metrics, Snv, No_transformation, KF_CV
+from Class_Mod import metrics, Snv, No_transformation, KF_CV, sel_ratio
class Regmodel(object):
@@ -10,14 +10,15 @@ class Regmodel(object):
self._nc, self._nt, self._p = train[0].shape[0], test[0].shape[0], train[0].shape[1]
self._model, self._best = None, None
self._yc, self._ycv, self._yt = None, None, None
- self._cv_df = pd.DataFrame
+ self._cv_df = pd.DataFrame()
+ self._sel_ratio = pd.DataFrame()
self._nfolds = nfolds
self._selected_bands = pd.DataFrame(index = ['from', 'to'])
self.important_features = None
self._hyper_params = {'polyorder': hp.choice('polyorder', [0, 1, 2]),
'deriv': hp.choice('deriv', [0, 1, 2]),
'window_length': hp.choice('window_length', [15, 21, 27, 33]),
- 'scatter': hp.choice('scatter', ['Snv', 'No_transformation'])}
+ 'normalization': hp.choice('normalization', ['Snv', 'No_transformation'])}
if add_hyperparams is not None:
self._hyper_params.update(add_hyperparams)
self._best = None
@@ -54,10 +55,10 @@ class Regmodel(object):
return self._best
@property
def best_hyperparams_print(self):
- if self._best['scatter'] == 'Snv':
+ if self._best['normalization'] == 'Snv':
a = 'Standard Normal Variate (SNV)'
- elif self._best['scatter'] == 'No_transformation':
+ elif self._best['normalization'] == 'No_transformation':
a = " No transformation was performed"
SG = f'- Savitzky-Golay derivative parameters \:(Window_length:{self._best['window_length']}; polynomial order: {self._best['polyorder']}; Derivative order : {self._best['deriv']})'
@@ -85,6 +86,10 @@ class Regmodel(object):
@property
def selected_features_(self):
return self._selected_bands
+
+ @property
+ def sel_ratio_(self):
+ return self._sel_ratio
########################################### #########################################
class Plsr(Regmodel):
@@ -95,7 +100,7 @@ class Plsr(Regmodel):
def objective(self, params):
x0 = [self._xc, self._xt]
- x1 = [eval(str(params['scatter'])+"(x0[i])") for i in range(2)]
+ x1 = [eval(str(params['normalization'])+"(x0[i])") for i in range(2)]
a, b, c = params['deriv'], params['polyorder'], params['window_length']
if a > b or b > c:
@@ -127,6 +132,7 @@ class Plsr(Regmodel):
self._model = Model
self._best = params
self.pretreated = pd.DataFrame(x2[0])
+ self._sel_ratio = sel_ratio(Model, x2[0])
return score
@@ -153,7 +159,7 @@ class TpeIpls(Regmodel):
# ## Preprocessing
x0 = [self._xc, self._xt]
- x1 = [eval(str(params['scatter'])+"(x0[i])") for i in range(2)]
+ x1 = [eval(str(params['normalization'])+"(x0[i])") for i in range(2)]
a, b, c = params['deriv'], params['polyorder'], params['window_length']
if a > b or b > c:
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index a30e15e9b743656132755c45e5b5f5aebc9fce3d..1cd06b868fa7c769c57992d5426bc69789c36b9f 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -499,8 +499,7 @@ Ac_Km = ['Spectra_Plot.png', 'Elbow.png', 'graphe_loadings.png', 'plot_axe1_axe2
# Streamlit container
with st.container():
- header3, header4 = st.columns(2)
- if header3.button("Exporter le rapport"):
+ if st.button("Download report"):
if test == '.csv':
if dim_red_method == dim_red_methods[1] and clus_method == cluster_methods[1]:
latex_report = report.report(sam, tcr, Nb_ech, nb_clu, 'sample', Ac_Km, 'csv', 'kmeans')
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index 49a680f7e8a4ff4075f00271a5b7e23ab050bb15..8e3082ae221ddd33363a41ed21187db9e7a18e87 100644
--- a/src/pages/2-model_creation.py
+++ b/src/pages/2-model_creation.py
@@ -67,8 +67,9 @@ files_format = ['.csv', '.dx']
file = M00.radio('Select files format:', options = files_format)
### Data
-spectra = pd.DataFrame
-y = pd.DataFrame
+spectra = pd.DataFrame()
+y = pd.DataFrame()
+
# load .csv file
if file == files_format[0]:
@@ -139,6 +140,10 @@ elif file == files_format[1]:
### split the data
if not spectra.empty and not y.empty:
+ if np.array(spectra.columns).dtype.kind in ['i','f']:
+ colnames = spectra.columns
+ else:
+ colnames = np.arange(spectra.shape[1])
#rd_seed = M1.slider("Customize Train-test split", min_value=1, max_value=100, value=42, format="%i")
@@ -216,39 +221,25 @@ if not spectra.empty and not y.empty:
M1.progress(100, text = "The model has successfully been created!")
time.sleep(1)
reg_model = Reg.model_
- # M3.write('-- Spectral regions used for model creation --')
- # intervalls = Reg.bands.T
- # M3.table(intervalls)
- # fig, ax = plt.subplots(figsize = (12, 6))
- # X_train.mean().plot(ax = ax)
- # for i in range(s):
- # colnames = np.array(y)
- # num = {'u','i','f','c'}
- # if np.array(X_train.columns).dtype.kind in num:
- # plt.plot(X_train.columns, X_train.mean(), color = 'black')
- # ax.axvspan(X_train.columns[intervalls['from'][i]], X_train.columns[intervalls['to'][i]],
- # color='#2a52be', alpha=0.5, lw=0)
- # plt.tight_layout()
- # plt.margins(x = 0)
- # else:
- # plt.plot(np.arange(X_train.shape[1]), X_train.mean(), color = 'black')
- # ax.axvspan(intervalls['from'][i], intervalls['to'][i], color='#2a52be', alpha=0.5, lw=0)
- # plt.tight_layout()
- # plt.margins(x = 0)
-
- # M3.write('-- Visualization of the spectral regions used for model creation -- ')
- # M3.pyplot(fig)
- M2.write('-- Spectral regions used for model creation --')
- intervalls = Reg.selected_features_.T
- M2.table(intervalls)
+ M2.write('-- Important Spectral regions used for model creation --')
+ intervalls = Reg.selected_features_.T
+ intervalls_with_cols = Reg.selected_features_.T
+ for i in range(intervalls.shape[0]):
+ for j in range(intervalls.shape[1]):
+ intervalls_with_cols.iloc[i,j] = spectra.columns[intervalls.iloc[i,j]]
+ M2.table(intervalls_with_cols)
+
# elif regression_algo == reg_algo[4]:
# Reg = PlsR(x_train = X_train, x_test = X_test, y_train = y_train, y_test = y_test)
# reg_model = Reg.model_
+
+
+
################# Model analysis ############
if regression_algo in reg_algo[1:]:
- M2.write('-- Pretreated data (train) visualization and important spectral regions in the model -- ')
+ #M2.write('-- Pretreated data (train) visualization and important spectral regions in the model -- ')
fig, (ax1, ax2) = plt.subplots(2,1, figsize = (12, 6))
fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.02)
@@ -270,43 +261,22 @@ if not spectra.empty and not y.empty:
# M3.plotly_chart(fig)
- from matplotlib.colors import Normalize
- color_variable = y_train
- norm = Normalize(vmin=color_variable.min(), vmax= color_variable.max())
- cmap = plt.get_cmap('viridis')
- colors = cmap(norm(color_variable.values))
- fig, ax = plt.subplots(figsize = (10,3))
-
- for i in range(Reg.pretreated_spectra_.shape[0]):
- ax.plot(Reg.pretreated_spectra_.columns, Reg.pretreated_spectra_.iloc[i,:], color = colors[i])
- sm = ScalarMappable(norm = norm, cmap = cmap)
- cbar = plt.colorbar(sm, ax = ax)
- # cbar.set_label('Target range')
- plt.tight_layout()
- htmlfig = mpld3.fig_to_html(fig)
- with M2:
- st.components.v1.html(htmlfig, height=600)
-
-
-
- # X_train.mean().plot(ax = ax2)
- # for i in range(s):
- # colnames = np.array(y)
- # num = {'u','i','f','c'}
- # if np.array(X_train.columns).dtype.kind in num:
- # plt.plot(X_train.columns, X_train.mean(), color = 'black')
- # ax2.axvspan(X_train.columns[intervalls['from'][i]], X_train.columns[intervalls['to'][i]],
- # color='#2a52be', alpha=0.5, lw=0)
- # plt.tight_layout()
- # plt.margins(x = 0)
- # else:
- # plt.plot(np.arange(X_train.shape[1]), X_train.mean(), color = 'black')
- # ax2.axvspan(intervalls['from'][i], intervalls['to'][i], color='#2a52be', alpha=0.5, lw=0)
- # plt.tight_layout()
- # plt.margins(x = 0)
-
- # pd.DataFrame(Reg.pretreated_spectra_).plot(ax = ax1)
- # M3.pyplot(fig)
+ # from matplotlib.colors import Normalize
+ # color_variable = y_train
+ # norm = Normalize(vmin=color_variable.min(), vmax= color_variable.max())
+ # cmap = plt.get_cmap('viridis')
+ # colors = cmap(norm(color_variable.values))
+ # fig, ax = plt.subplots(figsize = (10,3))
+
+ # for i in range(Reg.pretreated_spectra_.shape[0]):
+ # ax.plot(Reg.pretreated_spectra_.columns, Reg.pretreated_spectra_.iloc[i,:], color = colors[i])
+ # sm = ScalarMappable(norm = norm, cmap = cmap)
+ # cbar = plt.colorbar(sm, ax = ax)
+ # # cbar.set_label('Target range')
+ # plt.tight_layout()
+ # htmlfig = mpld3.fig_to_html(fig)
+ # with M2:
+ # st.components.v1.html(htmlfig, height=600)
############
@@ -329,7 +299,7 @@ if not spectra.empty and not y.empty:
cv1.plotly_chart(fig0, use_container_width=True)
fig0.write_image("./Report/figures/Predictions_V.png")
-
+
yc = Reg.pred_data_[0]
yt = Reg.pred_data_[1]
@@ -339,8 +309,8 @@ if not spectra.empty and not y.empty:
M1.write(Reg.best_hyperparams_print)
a_Test=Reg.best_hyperparams_print
- # with open("data/params/Preprocessing.json", "w") as outfile:
- # json.dump(Reg.best_hyperparams_, outfile)
+ with open("data/params/Preprocessing.json", "w") as outfile:
+ json.dump(Reg.best_hyperparams_, outfile)
##########
M1.write("-- Model performance --")
M1.dataframe(metrics(c = [y_train, yc], t = [y_test, yt], method='regression').scores_)
@@ -361,7 +331,7 @@ if not spectra.empty and not y.empty:
M8.pyplot(residual_plot)
plt.savefig('./Report/figures/residual_plot.png')
- rega = Reg.important_features_ ##### ADD FEATURES IMPORTANCE PLOT
+ rega = Reg.selected_features_ ##### ADD FEATURES IMPORTANCE PLOT
#model_export = M1.selectbox("Choose way to export", options=["pickle", "joblib"], key=20)
model_name = M9.text_input('Give it a name')
@@ -374,8 +344,7 @@ if not spectra.empty and not y.empty:
'_and_' + ycal_csv.name[:ycal_csv.name.find(".")] + '_data_' + '.pkl','wb') as f:
joblib.dump(reg_model, f)
if regression_algo == reg_algo[3]:
- rega[1].sort()
- pd.DataFrame(rega[1]).to_csv(path + model_name + date_time + '_on_' + xcal_csv.name[:xcal_csv.name.find(".")]
+ Reg.selected_features_.T.to_csv(path + model_name + date_time + '_on_' + xcal_csv.name[:xcal_csv.name.find(".")]
+ '_and_' + ycal_csv.name[:ycal_csv.name.find(".")] + '_data_'+'Wavelengths_index.csv', sep = ';')
elif file == files_format[1]:
@@ -383,13 +352,8 @@ if not spectra.empty and not y.empty:
with open(path + model_name + '_on_'+ data_file.name[:data_file.name.find(".")] + '_data_' + '.pkl','wb') as f:
joblib.dump(reg_model, f)
if regression_algo == reg_algo[3]:
- rega[1].sort()
- pd.DataFrame(rega[1]).to_csv(path +data_file.name[:data_file.name.find(".")]+ model_name + date_time+ '_on_' + '_data_'+'Wavelengths_index.csv', sep = ';')
+ Reg.selected_features_.T.to_csv(path +data_file.name[:data_file.name.find(".")]+ model_name + date_time+ '_on_' + '_data_'+'Wavelengths_index.csv', sep = ';')
st.write('Model Exported ')
-
- if regression_algo == reg_algo[3]:
- st.write('Model Exported')
-
# create a report with information on the model
## see https://stackoverflow.com/a/59578663
@@ -418,4 +382,39 @@ with st.container():
pass
else:
- pass
\ No newline at end of file
+ pass
+
+
+if not spectra.empty and not y.empty:
+ if regression_algo in reg_algo[1:]:
+ fig, (ax1, ax2) = plt.subplots(2,1, figsize = (12, 4), sharex=True)
+ ax1.plot(colnames, np.mean(X_train, axis = 0), color = 'black', label = 'Average spectrum (Raw)')
+ ax2.plot(colnames, np.mean(Reg.pretreated_spectra_ , axis = 0), color = 'black', label = 'Average spectrum (pretreated)')
+
+
+ ax2.set_xlabel('Wavelenghts')
+ plt.tight_layout()
+
+ for i in range(2):
+ eval(f'ax{i+1}').grid(color='grey', linestyle=':', linewidth=0.2)
+ eval(f'ax{i+1}').margins(x = 0)
+ eval(f'ax{i+1}').legend(loc = 'upper right')
+ eval(f'ax{i+1}').set_ylabel('Intensity')
+ if regression_algo == reg_algo[3]:
+ for j in range(s):
+ if np.array(spectra.columns).dtype.kind in ['i','f']:
+ min, max = intervalls_with_cols['from'][j], intervalls_with_cols['to'][j]
+ else:
+ min, max = intervalls['from'][j], intervalls['to'][j]
+
+ eval(f'ax{i+1}').axvspan(min, max, color='#00ff00', alpha=0.5, lw=0)
+ if regression_algo == reg_algo[1]:
+ ax1.scatter(colnames[np.array(Reg.sel_ratio_.index)], np.mean(X_train, axis = 0).ravel()[np.array(Reg.sel_ratio_.index)],
+ color = 'red', label = 'Important variables')
+ ax2.scatter(colnames[Reg.sel_ratio_.index], np.mean(Reg.pretreated_spectra_, axis = 0).ravel()[np.array(Reg.sel_ratio_.index)],
+ color = 'red', label = 'Important variables')
+ ax1.legend()
+ ax2.legend()
+
+ M2.write('-- Visualization of the spectral regions used for model creation -- ')
+ M2.pyplot(fig)
\ No newline at end of file
diff --git a/src/pages/3-prediction.py b/src/pages/3-prediction.py
index 2f515438326e5483d76a429e84cfbdbcbbacbb6c..b79dafdfe07ab2e0afc7894fc66d46598d4ccca2 100644
--- a/src/pages/3-prediction.py
+++ b/src/pages/3-prediction.py
@@ -17,18 +17,21 @@ local_css(css_file / "style_model.css")
st.header("Data loading", divider='blue')
-model_column1, space1, file_column1= st.columns([2, 1, 1])
+M1, M2= st.columns([2, 1])
+
+st.header('Data preprocessing', divider='blue')
+M3, M4= st.columns([2, 1])
+
st.header("Prediction making", divider='blue')
-model_column2, space2, file_column2= st.columns([2, 1, 1])
-_, space3, _ = st.columns([1, 3, 1])
+M5, M6 = st.columns([2, 0.01])
files_format = ['.csv', '.dx']
-file = file_column1.file_uploader("Select NIRS Data to predict", type = files_format, help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns")
+file = M2.file_uploader("Select NIRS Data to predict", type = files_format, help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns")
export_folder = './data/predictions/'
export_name = 'Predictions_of_'
reg_algo = ["Interval-PLS"]
-pred_data = pd.DataFrame
+pred_data = pd.DataFrame()
loaded_model = None
@@ -38,8 +41,8 @@ if file:
if test == files_format[0]:
#
- qsep = file_column1.selectbox("Select csv separator - _detected_: " + str(find_delimiter('data/'+file.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+file.name))), key=2)
- qhdr = file_column1.selectbox("indexes column in csv? - _detected_: " + str(find_col_index('data/'+file.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+file.name))), key=3)
+ qsep = M2.selectbox("Select csv separator - _detected_: " + str(find_delimiter('data/'+file.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+file.name))), key=2)
+ qhdr = M2.selectbox("indexes column in csv? - _detected_: " + str(find_col_index('data/'+file.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+file.name))), key=3)
if qhdr == 'yes':
col = 0
else:
@@ -51,55 +54,61 @@ if file:
tmp.write(file.read())
tmp_path = tmp.name
chem_data, spectra, meta_data, _ = read_dx(file = tmp_path)
- file_column1.success("The data have been loaded successfully", icon="✅")
+ M2.success("The data have been loaded successfully", icon="✅")
if chem_data.to_numpy().shape[1]>0:
- yname = file_column1.selectbox('Select target', options=chem_data.columns)
+ yname = M2.selectbox('Select target', options=chem_data.columns)
measured = chem_data.loc[:,yname] == 0
y = chem_data.loc[:,yname].loc[measured]
pred_data = spectra.loc[measured]
else:
pred_data = spectra
-
os.unlink(tmp_path)
# Load parameters
if not pred_data.empty:# Load the model with joblib
- model_column1.write('Raw spectra')
+ M1.write('Raw spectra')
fig = plot_spectra(pred_data, xunits = 'lab', yunits = "meta_data.loc[:,'yunits'][0]")
- model_column1.pyplot(fig)
+ M1.pyplot(fig)
+
### preprocessing
preprocessed = pd.DataFrame
if not pred_data.empty:
- params = file_column1.file_uploader("Load preprocessings params", type = '.json', help=" .json file")
+ params = M4.file_uploader("Load preprocessings params", type = '.json', help=" .json file")
if params:
prep = json.load(params)
+ # M4.write(ProcessLookupError)
- if prep['Scatter'] == 'SNV':
+ if prep['normalization'] == 'Snv':
x1 = Snv(pred_data)
+ norm = 'Standard Normal Variate'
else:
+ norm = 'No Normalization was applied'
x1 = pred_data
x2 = savgol_filter(x1,
- window_length = prep["Saitzky-Golay derivative parameters"]["window_length"],
- polyorder = prep["Saitzky-Golay derivative parameters"]["polyorder"],
- deriv=prep["Saitzky-Golay derivative parameters"]["deriv"],
+ window_length = prep["window_length"],
+ polyorder = prep["polyorder"],
+ deriv=prep["deriv"],
delta=1.0, axis=-1, mode="interp", cval=0.0)
preprocessed = pd.DataFrame(x2, index = pred_data.index, columns = pred_data.columns)
-
+
+################################################################################################
## plot preprocessed spectra
if not preprocessed.empty:
- model_column1.write('Preprocessed spectra')
+ M3.write('Preprocessed spectra')
fig2 = plot_spectra(preprocessed, xunits = 'lab', yunits = "meta_data.loc[:,'yunits'][0]")
- model_column1.pyplot(fig2)
-
+ M3.pyplot(fig2)
+ SG = f'- Savitzky-Golay derivative parameters \:(Window_length:{prep['window_length']}; polynomial order: {prep['polyorder']}; Derivative order : {prep['deriv']})'
+ Norm = f'- Spectral Normalization \: {norm}'
+ M4.write('The spectra were preprocessed using:\n'+SG+"\n"+Norm)
################### Predictions making ##########################
if not pred_data.empty:# Load the model with joblib
#dir = os.listdir('data/models/')[1:]
dir = os.listdir('data/models/')
dir.insert(0,'')
- model_name = model_column2.selectbox("Select your model from the dropdown list:", options = dir, key = 21)
+ model_name = M5.selectbox("Select your model from the dropdown list:", options = dir, key = 21)
if model_name and model_name !='':
export_name += '_with_' + model_name[:model_name.find('.')]
@@ -107,16 +116,18 @@ if not pred_data.empty:# Load the model with joblib
loaded_model = joblib.load(f)
if loaded_model:
- model_column2.success("The model has been loaded successfully", icon="✅")
- s = model_column2.checkbox('the model is of ipls type?')
+ M5.success("The model has been loaded successfully", icon="✅")
+ s = M5.checkbox('the model is of ipls type?')
if s:
- index = model_column2.file_uploader("select wavelengths index file", type="csv")
+ index = M5.file_uploader("select wavelengths index file", type="csv")
if index:
- idx = pd.read_csv(index, sep=';', index_col=0).iloc[:,0].to_numpy()
-
+ intervalls = pd.read_csv(index, sep=';', index_col=0).to_numpy()
+ idx = []
+ for i in range(intervalls.shape[0]):
+ idx.extend(np.arange(intervalls[i,0], intervalls[i,1]+1))
if loaded_model:
- if model_column2.button('Predict'):
+ if M5.button('Predict'):
if s:
result = loaded_model.predict(preprocessed.iloc[:,idx])
else:
@@ -124,19 +135,14 @@ if loaded_model:
result = loaded_model.predict(x2)
result = pd.DataFrame(result, index = pred_data.index)
- st.write('Predicted values')
- st.dataframe(result.T)
#############################
+ M5.write('Predicted values distribution')
# Creating histogram
- fig, axs = plt.subplots(1, 1,
- figsize =(12, 6),
+ fig, axs = plt.subplots(1, 1, figsize =(15, 3),
tight_layout = True)
# Add x, y gridlines
- axs.grid( color ='grey',
- linestyle ='-.', linewidth = 0.5,
- alpha = 0.6)
- plt.title('Predicted values distribution')
+ axs.grid( color ='grey', linestyle ='-.', linewidth = 0.5, alpha = 0.6)
# Remove axes splines
for s in ['top', 'bottom', 'left', 'right']:
axs.spines[s].set_visible(False)
@@ -156,13 +162,13 @@ if loaded_model:
color = plt.cm.viridis(norm(thisfrac))
thispatch.set_facecolor(color)
- space3.pyplot(fig)
+ M5.pyplot(fig)
+ M6.write('Predicted values table')
+ M6.dataframe(result.T)
##################################
result.to_csv(export_folder + export_name + '.csv', sep = ';')
# export to local drive - Download
download_results(export_folder + export_name + '.csv', export_name + '.csv')
# create a report with information on the prediction
- ## see https://stackoverflow.com/a/59578663
-
-
+ ## see https://stackoverflow.com/a/59578663
\ No newline at end of file