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import logging
import os
import pickle
from pathlib import Path
from typing import Dict, List
import numpy as np
import pandas as pd
import scanpy as sc
from anndata import AnnData
from sklearn.neighbors import NearestNeighbors
from .file import create_dirs, load
logger = logging.getLogger(__name__)
class Results_Handler():
def __init__(self,embedds_path:str,splits:List,mc_flag:bool=False,read_dataset:bool=False,create_knn_graph:bool=False,run_name:str=None,save_results:bool=False) -> None:
self.save_results = save_results
self.all_splits = ['train','valid','test','ood','artificial','no_annotation']
if splits == ['all']:
self.splits = self.all_splits
else:
self.splits = splits
self.mc_flag = mc_flag
#if embedds is not at the end of the embedds_path then append it
if not embedds_path.endswith('embedds'):
embedds_path = embedds_path+'/embedds'
_,self.splits_df_dict = self.get_data(embedds_path,self.splits)
#set column names
self.embedds_cols:List = [col for col in self.splits_df_dict[f'{splits[0]}_df'] if "Embedds" in col[0]]
self.seq_col:str = 'RNA Sequences'
self.label_col:str = 'Labels'
#create directories
self.parent_path:str = '/'.join(embedds_path.split('/')[:-1])
self.figures_path:str = self.parent_path+'/figures'
self.analysis_path:str = self.parent_path+'/analysis'
self.meta_path:str = self.parent_path+'/meta'
self.umaps_path:str = self.parent_path+'/umaps'
self.post_models_path:str = self.parent_path+'/post_models'
create_dirs([self.figures_path,self.analysis_path,self.post_models_path])
#get half of embedds cols if the model is Seq
model_name = self.get_hp_param(hp_param="model_name")
if model_name == 'seq':
self.embedds_cols = self.embedds_cols[:len(self.embedds_cols)//2]
if not run_name:
self.run_name = self.get_hp_param(hp_param="model_input")
if type(self.run_name) == list:
self.run_name = '-'.join(self.run_name)
ad_path = self.get_hp_param(hp_param="dataset_path_train")
if read_dataset:
self.dataset = load(ad_path)
if isinstance(self.dataset,AnnData):
self.dataset = self.dataset.var
self.seperate_label_from_split(split='artificial',removed_label='artificial_affix')
self.seperate_label_from_split(split='artificial',removed_label='random')
self.seperate_label_from_split(split='artificial',removed_label='recombined')
self.sc_to_mc_mapper_dict = self.load_mc_mapping_dict()
#get whether curr results are trained on ID or FULL
self.trained_on = self.get_hp_param(hp_param="trained_on")
#the main config of models trained on ID is not logged as for FULL
if self.trained_on == None:
self.trained_on = 'id'
#read train to be used for knn training and inference
train_df = self.splits_df_dict['train_df']
self.knn_seqs = train_df[self.seq_col].values
self.knn_labels = train_df[self.label_col].values
#create knn model if does not exist
if create_knn_graph:
self.create_knn_model()
def create_knn_model(self):
#get all train embedds
train_embedds = self.splits_df_dict['train_df'][self.embedds_cols].values
#linalg
train_embedds = train_embedds/np.linalg.norm(train_embedds,axis=1)[:,None]
#create knn model
self.knn_model = NearestNeighbors(n_neighbors=10,algorithm='brute',n_jobs=-1)
self.knn_model.fit(train_embedds)
#save knn model
filename = self.post_models_path+'/knn_model.sav'
pickle.dump(self.knn_model,open(filename,'wb'))
return
def get_knn_model(self):
filename = self.post_models_path+'/knn_model.sav'
self.knn_model = pickle.load(open(filename,'rb'))
return
def seperate_label_from_split(self,split,removed_label:str='artificial_affix'):
if split in self.splits:
logger.debug(f"splitting {removed_label} from split: {split}")
#get art affx
removed_label_df = self.splits_df_dict[f"{split}_df"].loc[self.splits_df_dict[f"{split}_df"][self.label_col]['0'] == removed_label]
#append art affx as key
self.splits_df_dict[f'{removed_label}_df'] = removed_label_df
#remove art affx from ood
removed_label_ids = self.splits_df_dict[f"{split}_df"].index.isin(removed_label_df.index)
self.splits_df_dict[f"{split}_df"] = self.splits_df_dict[f"{split}_df"][~removed_label_ids].reset_index(drop=True)
#resetf {split}_affix_idx
self.splits_df_dict[f'{removed_label}_df'] = self.splits_df_dict[f'{removed_label}_df'].reset_index(drop=True)
self.all_splits.append(f'{removed_label}')
def append_loco_variants(self):
train_classes = self.splits_df_dict["train_df"]["Logits"].columns.values
if self.mc_flag:
all_loco_classes_df = self.dataset['small_RNA_class_annotation'][self.dataset['small_RNA_class_annotation_hico'].isnull()].str.split(';', expand=True)
else:
all_loco_classes_df = self.dataset['subclass_name'][self.dataset['hico'].isnull()].str.split(';', expand=True)
all_loco_classes = all_loco_classes_df.values
#TODO: optimize getting unique values
loco_classes = []
for col in all_loco_classes_df.columns:
loco_classes.extend(all_loco_classes_df[col].unique())
loco_classes = list(set(loco_classes))
if np.nan in loco_classes:
loco_classes.remove(np.nan)
if None in loco_classes:
loco_classes.remove(None)
#compute loco not in train mask
loco_classes_not_in_train = list(set(loco_classes).difference(set(train_classes)))
loco_mask_not_in_train_df = all_loco_classes_df.isin(loco_classes_not_in_train)
mixed_and_not_in_train_df = all_loco_classes_df.iloc[loco_mask_not_in_train_df.values.sum(axis=1) >= 1]
train_classes_mask = mixed_and_not_in_train_df.isin(train_classes)
loco_not_in_train_df = mixed_and_not_in_train_df[train_classes_mask.values.sum(axis=1) == 0]
loco_mixed_df = mixed_and_not_in_train_df[~(train_classes_mask.values.sum(axis=1) == 0)]
nans_and_loco_train_df = all_loco_classes_df.iloc[loco_mask_not_in_train_df.values.sum(axis=1) == 0]
nans_mask = nans_and_loco_train_df.isin([None,np.nan])
nanas_df = nans_and_loco_train_df[nans_mask.values.sum(axis=1) == len(nans_mask.columns)]
loco_in_train_df = nans_and_loco_train_df[nans_mask.values.sum(axis=1) < len(nans_mask.columns)]
self.splits_df_dict["loco_not_in_train_df"] = self.splits_df_dict["no_annotation_df"][self.splits_df_dict["no_annotation_df"][self.seq_col]['0'].isin(loco_not_in_train_df.index)]
self.splits_df_dict["loco_mixed_df"] = self.splits_df_dict["no_annotation_df"][self.splits_df_dict["no_annotation_df"][self.seq_col]['0'].isin(loco_mixed_df.index)]
self.splits_df_dict["loco_in_train_df"] = self.splits_df_dict["no_annotation_df"][self.splits_df_dict["no_annotation_df"][self.seq_col]['0'].isin(loco_in_train_df.index)]
self.splits_df_dict["no_annotation_df"] = self.splits_df_dict["no_annotation_df"][self.splits_df_dict["no_annotation_df"][self.seq_col]['0'].isin(nanas_df.index)]
def get_data(self,path:str,splits:List,ith_run:int = -1):
#results exist in the outputs folder.
#outputs folder has two depth levels, first level indicates day and second indicates time per day
#if path not given, get results from last run
#ith run specifies the last run (-1), second last(-2)... etc
if not path:
files = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '../../..', 'outputs'))
logging.debug(files)
#newest
paths = sorted(list(Path(files).rglob('')), key=lambda x: Path.stat(x).st_mtime, reverse=True)
ith_run = abs(ith_run)
for path in paths:
if str(path).endswith('embedds'):
ith_run-= 1
if ith_run == 0:
path = str(path)
break
split_dfs = {}
splits_to_remove = []
for split in splits:
try:
#read logits csv
split_df = load(
path+f'/{split}_embedds.tsv',
header=[0, 1],
index_col=0,
)
split_df['split','0'] = split
split_dfs[f"{split}_df"] = split_df
except:
splits_to_remove.append(split)
logger.info(f'{split} does not exist in embedds! Removing it from splits')
for split in splits_to_remove:
self.splits.remove(split)
return path,split_dfs
def get_hp_param(self,hp_param):
hp_settings = load(path=self.meta_path+'/hp_settings.yaml')
#hp_param could be in hp_settings .keyes or in a key of a key
hp_val = hp_settings.get(hp_param)
if not hp_val:
for key in hp_settings.keys():
try:
hp_val = hp_settings[key].get(hp_param)
except:
pass
if hp_val != None:
break
if hp_val == None:
raise ValueError(f"hp_param {hp_param} not found in hp_settings")
return hp_val
def load_mc_mapping_dict(self):
mapping_dict_path = self.get_hp_param(hp_param="mapping_dict_path")
return load(mapping_dict_path)
def compute_umap(self,
ad,
nn=50,
spread=10,
min_dist=1.0,
):
sc.tl.pca(ad)
sc.pp.neighbors(ad, n_neighbors=nn, n_pcs=None, use_rep="X_pca")
sc.tl.umap(ad, n_components=2, spread=spread, min_dist=min_dist)
logger.info(f'cords are: {ad.obsm}')
return ad
def plot_umap(self,ad,
ncols=3,
colors=['Labels',"Unseen Labels"],
edges=False,
edges_width=0.05,
run_name = None,
path=None,
task=None
):
sc.set_figure_params(dpi = 80,figsize=[10,10])
fig = sc.pl.umap(
ad,
ncols=ncols,
color=colors,
edges=edges,
edges_width=edges_width,
title=[f"{run_name} approach: {c} {ad.shape}" for c in colors],
size = ad.obs["size"],
return_fig=True,
save=False
)
#fig.savefig(f'{path}{run_name}_{task}_umap.png')
def merge_all_splits(self):
all_dfs = [self.splits_df_dict[f'{split}_df'] for split in self.all_splits]
self.splits_df_dict['all_df'] = pd.concat(all_dfs).reset_index(drop=True)
return
def correct_labels(predicted_labels:pd.DataFrame,actual_labels:pd.DataFrame,mapping_dict:Dict):
'''
This function corrects the predicted labelsfor the bin based sub classes, tRNAs and miRNAs.
First both the actual and predicted labels are converted to major class. There are three classes of major classes:
1. tRNA: if the actual and predicted agree of all the tRNA sub class name except for the last part after -, then the predicted label is corrected to the actual label
2. bin based sub classes: if the actual and the predicted agree on sub class and the bin number is within 1 of the actual bin number, then the predicted label is corrected to the actual label
the bin number is after the last -
3. miRNAs: if the predicted and the actual agree on the first and last part of the subclass, and agree with the numeric part of the middle part, then the predicted label is corrected to the actual label
'''
if type(predicted_labels) == pd.Series:
predicted_labels = predicted_labels.values
actual_labels = actual_labels.values
import re
corrected_labels = []
for i in range(len(predicted_labels)):
predicted_label = predicted_labels[i]
actual_label = actual_labels[i]
if predicted_label == actual_label:
corrected_labels.append(predicted_label)
else:
mc = mapping_dict[actual_label]
if mc == 'tRNA':
if mapping_dict[predicted_label] == 'tRNA':
predicted_prec = predicted_label.split('__')[1]
actual_prec = actual_label.split('__')[1]
#the precursor is split by a -, if both have the share the same first part, then correct
if predicted_prec == actual_prec:
corrected_labels.append(actual_label)
else:
corrected_labels.append(predicted_label)
else:
corrected_labels.append(predicted_label)
elif mc == 'miRNA' and ('mir' in predicted_label.lower() or 'let' in predicted_label.lower()):
#check that the both share the same prime end (either 3p or 5p)
if predicted_label.split('-')[-1] == actual_label.split('-')[-1]:
#check that the both share the same numeric part
predicted_numeric = re.findall(r'\d+', predicted_label.split('-')[1])[0]
actual_numeric = re.findall(r'\d+', actual_label.split('-')[1])[0]
if predicted_numeric == actual_numeric:
corrected_labels.append(actual_label)
else:
corrected_labels.append(predicted_label)
else:
corrected_labels.append(predicted_label)
elif 'bin' in actual_label:
if '__' in predicted_label and '__' in actual_label:
predicted_label = predicted_label.split('__')[1]
actual_label = actual_label.split('__')[1]
if 'bin' in predicted_label and predicted_label.split('-')[0] == actual_label.split('-')[0]:
#get the bin number
actual_bin = int(actual_label.split('-')[-1])
predicted_bin = int(predicted_label.split('-')[-1])
#check that the predicted bin is within 1 of the actual bin
if abs(actual_bin - predicted_bin) <= 1:
corrected_labels.append(actual_label)
else:
corrected_labels.append(predicted_label)
else:
corrected_labels.append(predicted_label)
else:
corrected_labels.append(predicted_label)
return corrected_labels
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