minor fix

UMAP_INF_features.ipynb

 %% Cell type:markdown id: tags:
 
 # UMAP projection
 
 %% Cell type:code id: tags:
 
 ``` python
 import os
 import pandas as pd
 from pathlib import Path
 import numpy as np
 
 from bokeh.plotting import figure, output_file, show, save
 from bokeh.io import output_notebook, export_png
 from bokeh.palettes import colorblind
 from bokeh.models import CategoricalColorMapper, ColumnDataSource, LassoSelectTool, WheelZoomTool, ZoomInTool,  BoxZoomTool, ResetTool
 from bokeh.layouts import gridplot
 from bokeh.resources import CDN
 from bokeh.embed import file_html
 
 import numpy as np
 import umap
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 output_notebook()
 ```
 
 %%%% Output: display_data
 
 
 %%%% Output: display_data
 
 
 %% Cell type:markdown id: tags:
 
 ## Load Features datasets
 
 %% Cell type:code id: tags:
 
 ``` python
 TASK = 'subtypes'
 DATASET = 'tcga_breast'
 MODEL = 'randomForest'
 
 layers = 'gene_cnv_prot'
 
 PATH = Path('data') / DATASET / TASK
 PATH_RESULTS =  Path('results') / DATASET / TASK / MODEL
 
 SPLIT = 2# choose a random split for the train, test, and test2 files
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 file_tr = f'{PATH}/{SPLIT}/{layers}_tr.txt' # Fit UMAP
 file_test = f'{PATH}/{SPLIT}/{layers}_ts.txt' # test UMAP on TS
 file_test2 = f'{PATH}/{SPLIT}/{layers}_ts2.txt' # test UMAP on TS2
 
 features_train = pd.read_csv(file_tr, sep='\t', header=0, index_col=0)
 features_test = pd.read_csv(file_test, sep='\t', header=0, index_col=0)
 features_test2 = pd.read_csv(file_test2, sep='\t', header=0, index_col=0)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 BEST = False # restrict the features to the INF signature
 
 INF_feats = pd.read_csv(f'{PATH_RESULTS}/{SPLIT}/rSNFi/{layers}_ts_RandomForest_KBest_featurelist.txt', sep='\t')['FEATURE_NAME'].values.tolist()
 
 best_train = features_train[INF_feats]
 best_test = features_test[INF_feats]
 best_test2 = features_test2[INF_feats]
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 if BEST:
     features_train = best_train
     features_test = best_test
     features_test2 = best_test2
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 samples_tr = features_train.index
 labels_tr = pd.read_csv(f'{PATH}/{SPLIT}/labels_{TASK}_tr.txt', sep='\t', header=None)[0].tolist()
 
 features_train['labels'] = labels_tr
 labels_tr = features_train['labels']
 features_tr = features_train[features_train.columns[:-1]].values
 
 
 samples_test = features_test.index
 labels_test = pd.read_csv(f'{PATH}/{SPLIT}/labels_{TASK}_ts.txt', sep='\t', header=None)[0].tolist()
 
 features_test['labels'] = labels_test
 labels_test = features_test['labels']
 features_ts = features_test[features_test.columns[:-1]].values
 
 
 samples_test2 = features_test2.index
 labels_test2 = pd.read_csv(f'{PATH}/{SPLIT}/labels_{TASK}_ts2.txt', sep='\t', header=None)[0].tolist()
 
 
 features_test2['labels'] = labels_test2
 labels_test2 = features_test2['labels']
 features_ts2 = features_test2[features_test2.columns[:-1]].values
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 train_data = features_tr
 test_data = features_ts
 test2_data = features_ts2
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # Check
 print(len(features_tr),len(samples_tr),len(labels_tr))
 print(len(features_ts),len(samples_test),len(labels_test))
 print(len(features_ts2),len(samples_test2),len(labels_test2))
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Fit on the training data and transform the test set into the learned space
 
 %% Cell type:code id: tags:
 
 ``` python
 mapper = umap.UMAP(n_neighbors=40, min_dist=0.01, n_components=2,
                    metric='euclidean').fit(train_data)
 test_embedding = mapper.transform(test_data)
 test2_embedding = mapper.transform(test2_data)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # Check
 len(mapper.embedding_), len(test_embedding),len(test2_embedding)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Plot UMAP 2D projection
 
 %% Cell type:code id: tags:
 
 ``` python
 TOOLTIPS = [
     ("index", "$index"),
     ("(x,y)", "($x, $y)"),
     ("desc", "@desc"),
 ]
 
 mycols = colorblind['Colorblind'][4]
 myclasses = pd.unique(labels_tr).tolist()
 
 p = figure(plot_width=1200, plot_height=1200, tooltips = TOOLTIPS, tools='save', toolbar_location="left")
 
 
 p.title.align = "center"
 p.title.text_color = "black"
 p.title.text_font_size = "25px"
 
 size=12
 
 for col, theclass in zip(mycols,myclasses):
 
     idx_tr = np.where(np.array(labels_tr)==theclass)[0].tolist()
     samples_train = np.expand_dims(samples_tr[idx_tr,], axis=1)
     data_tr = np.hstack((mapper.embedding_[idx_tr,], samples_train))
     df_tr = pd.DataFrame(data_tr,columns=['x','y','sample'])
 
     source_tr = ColumnDataSource(data=dict(
     x=df_tr['x'],
     y=df_tr['y'],
     desc=df_tr['sample']))
     p.circle(x='x',y='y',size=size,source=source_tr,color=col,alpha=0.8,legend=str(theclass))
 
     idx_ts = np.where(np.array(labels_test)==theclass)[0].tolist()
     samples_ts = np.expand_dims(samples_test[idx_ts,], axis=1)
     data_ts = np.hstack((test_embedding[idx_ts,], samples_ts))
     df_ts = pd.DataFrame(data_ts,columns=['x','y','sample'])
 
     source_ts = ColumnDataSource(data=dict(
     x=df_ts['x'],
     y=df_ts['y'],
     desc=df_ts['sample']))
     p.triangle(x='x',y='y',size=size,source=source_ts,color=col,alpha=0.8)
 
     idx_ts2 = np.where(np.array(labels_test2)==theclass)[0].tolist()
     samples_ts2 = np.expand_dims(samples_test2[idx_ts2,], axis=1)
     data_ts2 = np.hstack((test2_embedding[idx_ts2,], samples_ts2))
     df_ts2 = pd.DataFrame(data_ts2,columns=['x','y','sample'])
 
     source_ts2 = ColumnDataSource(data=dict(
     x=df_ts2['x'],
     y=df_ts2['y'],
     desc=df_ts2['sample']))
     p.diamond(x='x',y='y',size=size,source=source_ts2,color=col,alpha=0.8)
 
 
 p.add_tools(LassoSelectTool())
 p.add_tools(WheelZoomTool())
 p.legend.label_text_font_size = "20pt"
 p.yaxis.major_label_text_font_size = "15pt"
 p.xaxis.major_label_text_font_size = "15pt"
 
 
 p.add_tools(ZoomInTool())
 p.add_tools(ResetTool())
 p.add_tools(BoxZoomTool())
 p.legend.location = "top_left"
 p.legend.click_policy='hide'
 # p.title()
 
 if BEST:
     export_png(p, filename=f"subtypes_INF_split{SPLIT}.png") #save the plot
 else:
     export_png(p, filename=f"subtypes_juXT_split{SPLIT}.png")
 
 show(p)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Grid plot for all other splits
 
 %% Cell type:code id: tags:
 
 ``` python
 def range_with_ignore(start, stop, ignore):
     return np.concatenate([
         np.arange(start, ignore),
         np.arange(ignore + 1, stop)
     ])
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 BEST = False
 
 mycols = colorblind['Colorblind'][4]
 plots = []
 size = 12
 
-for split in range_with_ignore(0,9,SPLIT).tolist():
+for split in range_with_ignore(0,10,SPLIT).tolist():
 
     file_tr = f'{PATH}/{split}/{layers}_tr.txt' # Fit UMAP
     file_test = f'{PATH}/{split}/{layers}_ts.txt' # test UMAP
     file_test2 = f'{PATH}/{split}/{layers}_ts2.txt' # test UMAP
 
     features_train = pd.read_csv(file_tr, sep='\t', header=0, index_col=0)
     features_test = pd.read_csv(file_test, sep='\t', header=0, index_col=0)
     features_test2 = pd.read_csv(file_test2, sep='\t', header=0, index_col=0)
 
 
     INF_feats = pd.read_csv(f'{PATH_RESULTS}/{split}/rSNFi/{layers}_ts_RandomForest_KBest_featurelist.txt', sep='\t')['FEATURE_NAME'].values.tolist()
 
     best_train = features_train[INF_feats]
     best_test = features_test[INF_feats]
     best_test2 = features_test2[INF_feats]
 
 
     if BEST:
         features_train = best_train
         features_test = best_test
         features_test2 = best_test2
 
 
 
     samples_tr = features_train.index
     labels_tr = pd.read_csv(f'{PATH}/{split}/labels_{TASK}_tr.txt', sep='\t', header=None)[0].tolist()
 
     features_train['labels'] = labels_tr
     labels_tr = features_train['labels']
     features_tr = features_train[features_train.columns[:-1]].values
 
 
     samples_test = features_test.index
     labels_test = pd.read_csv(f'{PATH}/{split}/labels_{TASK}_ts.txt', sep='\t', header=None)[0].tolist()
 
     features_test['labels'] = labels_test
     labels_test = features_test['labels']
     features_ts = features_test[features_test.columns[:-1]].values
 
 
     samples_test2 = features_test2.index
     labels_test2 = pd.read_csv(f'{PATH}/{split}/labels_{TASK}_ts2.txt', sep='\t', header=None)[0].tolist()
 
 
     features_test2['labels'] = labels_test2
     labels_test2 = features_test2['labels']
     features_ts2 = features_test2[features_test2.columns[:-1]].values
 
 
     train_data = features_tr
     test_data = features_ts
     test2_data = features_ts2
 
 
     mapper = umap.UMAP(n_neighbors=40, min_dist=0.01, n_components=2, metric='euclidean').fit(train_data)
     test_embedding = mapper.transform(test_data)
     test2_embedding = mapper.transform(test2_data)
 
 
 
     myclasses = pd.unique(labels_tr).tolist()
 
     p = figure(title=f'split {split}')
     p.title.text_font_size = '25pt'
 
 
     p.title.align = "center"
     p.title.text_color = "black"
     p.title.text_font_size = "25px"
 
 
     for col, theclass in zip(mycols,myclasses):
 
         idx_tr = np.where(np.array(labels_tr)==theclass)[0].tolist()
         samples_train = np.expand_dims(samples_tr[idx_tr,], axis=1)
         data_tr = np.hstack((mapper.embedding_[idx_tr,], samples_train))