added all run scripts for diffferent splits
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6 changed files with 948 additions and 0 deletions
141
scripts/ml/run_8020.py
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141
scripts/ml/run_8020.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Jun 20 13:05:23 2022
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@author: tanu
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"""
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#%%Imports ####################################################################
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import re
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import argparse
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import os, sys
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# gene = 'pncA'
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# drug = 'pyrazinamide'
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#total_mtblineage_uc = 8
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###############################################################################
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#%% command line args: case sensitive
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
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arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
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args = arg_parser.parse_args()
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drug = args.drug
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gene = args.gene
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
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###############################################################################
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#==================
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# Import data
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#==================
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from ml_data_8020 import *
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setvars(gene,drug)
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from ml_data_8020 import *
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# from YC run_all_ML: run locally
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#from UQ_yc_RunAllClfs import run_all_ML
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#====================
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# Import ML functions
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#====================
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from MultClfs import *
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#==================
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# other vars
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#==================
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tts_split_8020 = '80_20'
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OutFile_suffix = '8020'
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#==================
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# Specify outdir
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#==================
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outdir_ml = outdir + 'ml/tts_8020/'
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print('\nOutput directory:', outdir_ml)
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#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
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outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
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#%% Running models ############################################################
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print('\n#####################################################################\n'
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, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
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, '\nGene name:', gene
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, '\nDrug name:', drug
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, '\n#####################################################################\n')
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paramD = {
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'baseline_paramD': { 'input_df' : X
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, 'target' : y
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, 'var_type' : 'mixed'
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, 'resampling_type': 'none'}
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, 'smnc_paramD': { 'input_df' : X_smnc
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, 'target' : y_smnc
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'smnc'}
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, 'ros_paramD': { 'input_df' : X_ros
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, 'target' : y_ros
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'ros'}
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, 'rus_paramD' : { 'input_df' : X_rus
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, 'target' : y_rus
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'rus'}
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, 'rouC_paramD' : { 'input_df' : X_rouC
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, 'target' : y_rouC
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'rouC'}
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}
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##==============================================================================
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## Dict with no CV BT formatted df
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## mmD = {}
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## for k, v in paramD.items():
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## # print(mmD[k])
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## scores_8020D = MultModelsCl(**paramD[k]
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## , tts_split_type = tts_split_8020
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## , skf_cv = skf_cv
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## , blind_test_df = X_bts
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## , blind_test_target = y_bts
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## , add_cm = True
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## , add_yn = True
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## , return_formatted_output = False)
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## mmD[k] = scores_8020D
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##==============================================================================
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## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
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mmDD = {}
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for k, v in paramD.items():
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scores_8020D = MultModelsCl(**paramD[k]
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, tts_split_type = tts_split_8020
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, skf_cv = skf_cv
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, add_cm = True
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, add_yn = True
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, return_formatted_output = True)
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mmDD[k] = scores_8020D
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# Extracting the dfs from within the dict and concatenating to output as one df
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for k, v in mmDD.items():
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out_wf_8020 = pd.concat(mmDD, ignore_index = True)
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out_wf_8020f = out_wf_8020.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
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print('\n######################################################################'
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, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
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, '\nGene:', gene.lower()
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, '\nDrug:', drug
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, '\noutput file:', outFile_wf
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, '\nDim of output:', out_wf_8020f.shape
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, '\n######################################################################')
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###############################################################################
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#====================
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# Write output file
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#====================
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out_wf_8020f.to_csv(outFile_wf, index = False)
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print('\nFile successfully written:', outFile_wf)
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###############################################################################
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242
scripts/ml/run_FS_7030.py
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scripts/ml/run_FS_7030.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue May 24 08:11:05 2022
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@author: tanu
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"""
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#%%
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import os, sys
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import pandas as pd
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import numpy as np
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import pprint as pp
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from copy import deepcopy
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from sklearn import linear_model
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from sklearn import datasets
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from collections import Counter
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from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
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from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier
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from sklearn.naive_bayes import BernoulliNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import SVC
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from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.gaussian_process import GaussianProcessClassifier, kernels
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from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
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from sklearn.neural_network import MLPClassifier
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from sklearn.svm import SVC
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from xgboost import XGBClassifier
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from sklearn.compose import make_column_transformer
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from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
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# added
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
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from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.feature_selection import RFE, RFECV
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import itertools
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import seaborn as sns
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import matplotlib.pyplot as plt
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from statistics import mean, stdev, median, mode
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from imblearn.over_sampling import RandomOverSampler
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from imblearn.under_sampling import RandomUnderSampler
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from imblearn.over_sampling import SMOTE
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from sklearn.datasets import make_classification
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from imblearn.combine import SMOTEENN
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from imblearn.combine import SMOTETomek
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from imblearn.over_sampling import SMOTENC
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from imblearn.under_sampling import EditedNearestNeighbours
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from imblearn.under_sampling import RepeatedEditedNearestNeighbours
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from sklearn.model_selection import GridSearchCV
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from sklearn.base import BaseEstimator
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from sklearn.impute import KNNImputer as KNN
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import json
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import argparse
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import re
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###############################################################################
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#gene = 'pncA'
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#drug = 'pyrazinamide'
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#total_mtblineage_uc = 8
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#%% command line args: case sensitive
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
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arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
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args = arg_parser.parse_args()
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drug = args.drug
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gene = args.gene
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###############################################################################
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#==================
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# other vars
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#==================
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tts_split = '70_30'
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OutFile_suffix = '7030_FS'
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
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###############################################################################
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#==================
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# Import data
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#==================
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from ml_data_7030 import *
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setvars(gene,drug)
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from ml_data_7030 import *
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# from YC run_all_ML: run locally
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#from UQ_yc_RunAllClfs import run_all_ML
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#==========================================
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# Import ML functions:
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# fsgs_rfecv(): RFECV for Feature selection
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#==========================================
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from MultClfs import *
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#==================
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# Specify outdir
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#==================
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outdir_ml = outdir + 'ml/tts_7030/fs/'
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print('\nOutput directory:', outdir_ml)
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#OutFileFS = outdir_ml + gene.lower() + '_FS' + OutFile_suffix + '.json'
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OutFileFS = outdir_ml + gene.lower() + '_FS_noOR' + OutFile_suffix + '.json'
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############################################################################
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###############################################################################
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#====================
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# single model CALL
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#====================
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# aFS = fsgs(input_df = X
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# , target = y
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# , param_gridLd = [{'fs__min_features_to_select': [1]}]
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# , blind_test_df = X_bts
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# , blind_test_target = y_bts
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# , estimator = LogisticRegression(**rs)
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# , use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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# , custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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# , cv_method = skf_cv
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# , var_type = 'mixed'
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# )
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#############
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# Loop
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############
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#models_fs = [('Decision Tree' , DecisionTreeClassifier(**rs)) ]
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models_fs = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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, ('Decision Tree' , DecisionTreeClassifier(**rs) )
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, ('Extra Tree' , ExtraTreeClassifier(**rs) )
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, ('Extra Trees' , ExtraTreesClassifier(**rs) )
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, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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, ('LDA' , LinearDiscriminantAnalysis() )
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, ('Logistic Regression' , LogisticRegression(**rs) )
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, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
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, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
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, ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
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, n_estimators = 1000
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, bootstrap = True
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, oob_score = True
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, **njobs
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, **rs
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, max_features = 'auto') )
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, ('Ridge Classifier' , RidgeClassifier(**rs) )
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, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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## , ('XGBoost' , XGBClassifier(**rs, **njobs, verbosity = 3 , use_label_encoder = False) )
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]
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print('\n#####################################################################'
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, '\nRunning Feature Selection using classfication models_fs (n):', len(models_fs)
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, '\nGene:' , gene.lower()
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, '\nDrug:' , drug
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, '\nSplit:' , tts_split
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,'\n####################################################################')
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for m in models_fs:
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print(m)
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print('\n====================================================================\n')
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out_fsD = {}
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index = 1
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for model_name, model_fn in models_fs:
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print('\nRunning classifier with FS:', index
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, '\nModel_name:' , model_name
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, '\nModel func:' , model_fn)
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#, '\nList of models_fs:', models_fs)
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index = index+1
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out_fsD[model_name] = fsgs_rfecv(input_df = X
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, target = y
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, param_gridLd = [{'fs__min_features_to_select': [1]}]
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, estimator = model_fn
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, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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, var_type = 'mixed'
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)
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out_fsD
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#%% Checking results dict
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tot_Ditems = sum(len(v) for v in out_fsD.values())
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checkL = []
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for k, v in out_fsD.items():
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l = [len(out_fsD[k])]
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checkL = checkL + l
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n_sD = len(checkL) # no. of subDicts
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l_sD = list(set(checkL)) # length of each subDict
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print('\nTotal no.of subdicts:', n_sD)
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if len(l_sD) == 1 and tot_Ditems == n_sD*l_sD[0]:
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print('\nPASS: successful run for all Classifiers'
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, '\nLength of each subdict:', l_sD)
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print('\nSuccessfully ran Feature selection on', len(models_fs), 'classifiers'
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, '\nGene:', gene.lower()
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, '\nDrug:', drug
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, '\nSplit type:', tts_split
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, '\nTotal fs models results:', len(out_fsD)
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, '\nTotal items in output:', sum(len(v) for v in out_fsD.values()) )
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##############################################################################
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#%% json output
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#========================================
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# Write final output file
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# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
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#========================================
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# Output final dict as a json
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print('\nWriting Final output file (json):', OutFileFS)
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with open(OutFileFS, 'w') as f:
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f.write(json.dumps(out_fsD
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# , cls = NpEncoder
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))
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# read json
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with open(OutFileFS, 'r') as f:data = json.load(f)
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#############################################################################
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142
scripts/ml/run_cd_7030.py
Executable file
142
scripts/ml/run_cd_7030.py
Executable file
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Jun 20 13:05:23 2022
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@author: tanu
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"""
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#%%Imports ####################################################################
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import re
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import argparse
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import os, sys
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# gene = 'pncA'
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# drug = 'pyrazinamide'
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#total_mtblineage_uc = 8
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###############################################################################
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#%% command line args: case sensitive
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
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arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
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args = arg_parser.parse_args()
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drug = args.drug
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gene = args.gene
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
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###############################################################################
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#==================
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# Import data
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#==================
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from ml_data_cd_7030 import *
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setvars(gene,drug)
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from ml_data_cd_7030 import *
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# from YC run_all_ML: run locally
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#from UQ_yc_RunAllClfs import run_all_ML
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#====================
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# Import ML functions
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#====================
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from MultClfs import *
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#==================
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# other vars
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#==================
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tts_split_cd_7030 = 'cd_7030'
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OutFile_suffix = '_cd_7030'
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#==================
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# Specify outdir
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#==================
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outdir_ml = outdir + 'ml/tts_cd_7030/'
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print('\nOutput directory:', outdir_ml)
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#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
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outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
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|
||||
#%% Running models ############################################################
|
||||
print('\n#####################################################################\n'
|
||||
, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\n#####################################################################\n')
|
||||
|
||||
paramD = {
|
||||
'baseline_paramD': { 'input_df' : X
|
||||
, 'target' : y
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type': 'none'}
|
||||
|
||||
, 'smnc_paramD': { 'input_df' : X_smnc
|
||||
, 'target' : y_smnc
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'smnc'}
|
||||
|
||||
, 'ros_paramD': { 'input_df' : X_ros
|
||||
, 'target' : y_ros
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'ros'}
|
||||
|
||||
, 'rus_paramD' : { 'input_df' : X_rus
|
||||
, 'target' : y_rus
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rus'}
|
||||
|
||||
, 'rouC_paramD' : { 'input_df' : X_rouC
|
||||
, 'target' : y_rouC
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rouC'}
|
||||
}
|
||||
|
||||
##==============================================================================
|
||||
## Dict with no CV BT formatted df
|
||||
## mmD = {}
|
||||
## for k, v in paramD.items():
|
||||
## # print(mmD[k])
|
||||
## scores_cd_7030D = MultModelsCl(**paramD[k]
|
||||
## , tts_split_type = tts_split_cd_7030
|
||||
## , skf_cv = skf_cv
|
||||
## , blind_test_df = X_bts
|
||||
## , blind_test_target = y_bts
|
||||
## , add_cm = True
|
||||
## , add_yn = True
|
||||
## , return_formatted_output = False)
|
||||
## mmD[k] = scores_cd_7030D
|
||||
##==============================================================================
|
||||
## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
|
||||
mmDD = {}
|
||||
for k, v in paramD.items():
|
||||
scores_cd_7030D = MultModelsCl(**paramD[k]
|
||||
, tts_split_type = tts_split_cd_7030
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_df = X_bts
|
||||
, blind_test_target = y_bts
|
||||
, add_cm = True
|
||||
, add_yn = True
|
||||
, return_formatted_output = True)
|
||||
mmDD[k] = scores_cd_7030D
|
||||
|
||||
# Extracting the dfs from within the dict and concatenating to output as one df
|
||||
for k, v in mmDD.items():
|
||||
out_wf_cd_7030 = pd.concat(mmDD, ignore_index = True)
|
||||
|
||||
out_wf_cd_7030f = out_wf_cd_7030.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
|
||||
|
||||
print('\n######################################################################'
|
||||
, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
|
||||
, '\nGene:', gene.lower()
|
||||
, '\nDrug:', drug
|
||||
, '\noutput file:', outFile_wf
|
||||
, '\nDim of output:', out_wf_cd_7030f.shape
|
||||
, '\n######################################################################')
|
||||
###############################################################################
|
||||
#====================
|
||||
# Write output file
|
||||
#====================
|
||||
out_wf_cd_7030f.to_csv(outFile_wf, index = False)
|
||||
print('\nFile successfully written:', outFile_wf)
|
||||
###############################################################################
|
||||
141
scripts/ml/run_cd_8020.py
Executable file
141
scripts/ml/run_cd_8020.py
Executable file
|
|
@ -0,0 +1,141 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon Jun 20 13:05:23 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%%Imports ####################################################################
|
||||
import re
|
||||
import argparse
|
||||
import os, sys
|
||||
|
||||
# gene = 'pncA'
|
||||
# drug = 'pyrazinamide'
|
||||
#total_mtblineage_uc = 8
|
||||
###############################################################################
|
||||
#%% command line args: case sensitive
|
||||
arg_parser = argparse.ArgumentParser()
|
||||
arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
|
||||
arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
|
||||
args = arg_parser.parse_args()
|
||||
|
||||
drug = args.drug
|
||||
gene = args.gene
|
||||
|
||||
###############################################################################
|
||||
homedir = os.path.expanduser("~")
|
||||
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
|
||||
|
||||
###############################################################################
|
||||
#==================
|
||||
# Import data
|
||||
#==================
|
||||
from ml_data_cd_8020 import *
|
||||
setvars(gene,drug)
|
||||
from ml_data_cd_8020 import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
#====================
|
||||
# Import ML functions
|
||||
#====================
|
||||
from MultClfs import *
|
||||
|
||||
#==================
|
||||
# other vars
|
||||
#==================
|
||||
tts_split_cd_8020 = 'cd_80_20'
|
||||
OutFile_suffix = '_cd_8020'
|
||||
|
||||
#==================
|
||||
# Specify outdir
|
||||
#==================
|
||||
outdir_ml = outdir + 'ml/tts_cd_8020/'
|
||||
print('\nOutput directory:', outdir_ml)
|
||||
|
||||
#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
|
||||
outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
|
||||
#%% Running models ############################################################
|
||||
print('\n#####################################################################\n'
|
||||
, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\n#####################################################################\n')
|
||||
|
||||
paramD = {
|
||||
'baseline_paramD': { 'input_df' : X
|
||||
, 'target' : y
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type': 'none'}
|
||||
|
||||
, 'smnc_paramD': { 'input_df' : X_smnc
|
||||
, 'target' : y_smnc
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'smnc'}
|
||||
|
||||
, 'ros_paramD': { 'input_df' : X_ros
|
||||
, 'target' : y_ros
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'ros'}
|
||||
|
||||
, 'rus_paramD' : { 'input_df' : X_rus
|
||||
, 'target' : y_rus
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rus'}
|
||||
|
||||
, 'rouC_paramD' : { 'input_df' : X_rouC
|
||||
, 'target' : y_rouC
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rouC'}
|
||||
}
|
||||
|
||||
##==============================================================================
|
||||
## Dict with no CV BT formatted df
|
||||
## mmD = {}
|
||||
## for k, v in paramD.items():
|
||||
## # print(mmD[k])
|
||||
## scores_cd_8020D = MultModelsCl(**paramD[k]
|
||||
## , tts_split_type = tts_split_cd_8020
|
||||
## , skf_cv = skf_cv
|
||||
## , blind_test_df = X_bts
|
||||
## , blind_test_target = y_bts
|
||||
## , add_cm = True
|
||||
## , add_yn = True
|
||||
## , return_formatted_output = False)
|
||||
## mmD[k] = scores_cd_8020D
|
||||
##==============================================================================
|
||||
## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
|
||||
mmDD = {}
|
||||
for k, v in paramD.items():
|
||||
scores_cd_8020D = MultModelsCl(**paramD[k]
|
||||
, tts_split_type = tts_split_cd_8020
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_df = X_bts
|
||||
, blind_test_target = y_bts
|
||||
, add_cm = True
|
||||
, add_yn = True
|
||||
, return_formatted_output = True)
|
||||
mmDD[k] = scores_cd_8020D
|
||||
|
||||
# Extracting the dfs from within the dict and concatenating to output as one df
|
||||
for k, v in mmDD.items():
|
||||
out_wf_cd_8020 = pd.concat(mmDD, ignore_index = True)
|
||||
|
||||
out_wf_cd_8020f = out_wf_cd_8020.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
|
||||
|
||||
print('\n######################################################################'
|
||||
, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
|
||||
, '\nGene:', gene.lower()
|
||||
, '\nDrug:', drug
|
||||
, '\noutput file:', outFile_wf
|
||||
, '\nDim of output:', out_wf_cd_8020f.shape
|
||||
, '\n######################################################################')
|
||||
###############################################################################
|
||||
#====================
|
||||
# Write output file
|
||||
#====================
|
||||
out_wf_cd_8020f.to_csv(outFile_wf, index = False)
|
||||
print('\nFile successfully written:', outFile_wf)
|
||||
###############################################################################
|
||||
141
scripts/ml/run_cd_sl.py
Executable file
141
scripts/ml/run_cd_sl.py
Executable file
|
|
@ -0,0 +1,141 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon Jun 20 13:05:23 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%%Imports ####################################################################
|
||||
import re
|
||||
import argparse
|
||||
import os, sys
|
||||
|
||||
# gene = 'pncA'
|
||||
# drug = 'pyrazinamide'
|
||||
#total_mtblineage_uc = 8
|
||||
###############################################################################
|
||||
#%% command line args: case sensitive
|
||||
arg_parser = argparse.ArgumentParser()
|
||||
arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
|
||||
arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
|
||||
args = arg_parser.parse_args()
|
||||
|
||||
drug = args.drug
|
||||
gene = args.gene
|
||||
|
||||
###############################################################################
|
||||
homedir = os.path.expanduser("~")
|
||||
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
|
||||
|
||||
###############################################################################
|
||||
#==================
|
||||
# Import data
|
||||
#==================
|
||||
from ml_data_cd_sl import *
|
||||
setvars(gene,drug)
|
||||
from ml_data_cd_sl import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
#====================
|
||||
# Import ML functions
|
||||
#====================
|
||||
from MultClfs import *
|
||||
|
||||
#==================
|
||||
# other vars
|
||||
#==================
|
||||
tts_split_cd_sl = 'cd_sl'
|
||||
OutFile_suffix = '_cd_sl'
|
||||
|
||||
#==================
|
||||
# Specify outdir
|
||||
#==================
|
||||
outdir_ml = outdir + 'ml/tts_cd_sl/'
|
||||
print('\nOutput directory:', outdir_ml)
|
||||
|
||||
#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
|
||||
outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
|
||||
#%% Running models ############################################################
|
||||
print('\n#####################################################################\n'
|
||||
, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\n#####################################################################\n')
|
||||
|
||||
paramD = {
|
||||
'baseline_paramD': { 'input_df' : X
|
||||
, 'target' : y
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type': 'none'}
|
||||
|
||||
, 'smnc_paramD': { 'input_df' : X_smnc
|
||||
, 'target' : y_smnc
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'smnc'}
|
||||
|
||||
, 'ros_paramD': { 'input_df' : X_ros
|
||||
, 'target' : y_ros
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'ros'}
|
||||
|
||||
, 'rus_paramD' : { 'input_df' : X_rus
|
||||
, 'target' : y_rus
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rus'}
|
||||
|
||||
, 'rouC_paramD' : { 'input_df' : X_rouC
|
||||
, 'target' : y_rouC
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rouC'}
|
||||
}
|
||||
|
||||
##==============================================================================
|
||||
## Dict with no CV BT formatted df
|
||||
## mmD = {}
|
||||
## for k, v in paramD.items():
|
||||
## # print(mmD[k])
|
||||
## scores_cd_slD = MultModelsCl(**paramD[k]
|
||||
## , tts_split_type = tts_split_cd_sl
|
||||
## , skf_cv = skf_cv
|
||||
## , blind_test_df = X_bts
|
||||
## , blind_test_target = y_bts
|
||||
## , add_cm = True
|
||||
## , add_yn = True
|
||||
## , return_formatted_output = False)
|
||||
## mmD[k] = scores_cd_slD
|
||||
##==============================================================================
|
||||
## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
|
||||
mmDD = {}
|
||||
for k, v in paramD.items():
|
||||
scores_cd_slD = MultModelsCl(**paramD[k]
|
||||
, tts_split_type = tts_split_cd_sl
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_df = X_bts
|
||||
, blind_test_target = y_bts
|
||||
, add_cm = True
|
||||
, add_yn = True
|
||||
, return_formatted_output = True)
|
||||
mmDD[k] = scores_cd_slD
|
||||
|
||||
# Extracting the dfs from within the dict and concatenating to output as one df
|
||||
for k, v in mmDD.items():
|
||||
out_wf_cd_sl = pd.concat(mmDD, ignore_index = True)
|
||||
|
||||
out_wf_cd_slf = out_wf_cd_sl.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
|
||||
|
||||
print('\n######################################################################'
|
||||
, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
|
||||
, '\nGene:', gene.lower()
|
||||
, '\nDrug:', drug
|
||||
, '\noutput file:', outFile_wf
|
||||
, '\nDim of output:', out_wf_cd_slf.shape
|
||||
, '\n######################################################################')
|
||||
###############################################################################
|
||||
#====================
|
||||
# Write output file
|
||||
#====================
|
||||
out_wf_cd_slf.to_csv(outFile_wf, index = False)
|
||||
print('\nFile successfully written:', outFile_wf)
|
||||
###############################################################################
|
||||
141
scripts/ml/run_sl.py
Executable file
141
scripts/ml/run_sl.py
Executable file
|
|
@ -0,0 +1,141 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon Jun 20 13:05:23 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%%Imports ####################################################################
|
||||
import re
|
||||
import argparse
|
||||
import os, sys
|
||||
|
||||
# gene = 'pncA'
|
||||
# drug = 'pyrazinamide'
|
||||
#total_mtblineage_uc = 8
|
||||
###############################################################################
|
||||
#%% command line args: case sensitive
|
||||
arg_parser = argparse.ArgumentParser()
|
||||
arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
|
||||
arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
|
||||
args = arg_parser.parse_args()
|
||||
|
||||
drug = args.drug
|
||||
gene = args.gene
|
||||
|
||||
###############################################################################
|
||||
homedir = os.path.expanduser("~")
|
||||
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml')
|
||||
|
||||
###############################################################################
|
||||
#==================
|
||||
# Import data
|
||||
#==================
|
||||
from ml_data_sl import *
|
||||
setvars(gene,drug)
|
||||
from ml_data_sl import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
#====================
|
||||
# Import ML functions
|
||||
#====================
|
||||
from MultClfs import *
|
||||
|
||||
#==================
|
||||
# other vars
|
||||
#==================
|
||||
tts_split_sl = 'sl'
|
||||
OutFile_suffix = 'sl'
|
||||
|
||||
#==================
|
||||
# Specify outdir
|
||||
#==================
|
||||
outdir_ml = outdir + 'ml/tts_sl/'
|
||||
print('\nOutput directory:', outdir_ml)
|
||||
|
||||
#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
|
||||
outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
|
||||
#%% Running models ############################################################
|
||||
print('\n#####################################################################\n'
|
||||
, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\n#####################################################################\n')
|
||||
|
||||
paramD = {
|
||||
'baseline_paramD': { 'input_df' : X
|
||||
, 'target' : y
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type': 'none'}
|
||||
|
||||
, 'smnc_paramD': { 'input_df' : X_smnc
|
||||
, 'target' : y_smnc
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'smnc'}
|
||||
|
||||
, 'ros_paramD': { 'input_df' : X_ros
|
||||
, 'target' : y_ros
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'ros'}
|
||||
|
||||
, 'rus_paramD' : { 'input_df' : X_rus
|
||||
, 'target' : y_rus
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rus'}
|
||||
|
||||
, 'rouC_paramD' : { 'input_df' : X_rouC
|
||||
, 'target' : y_rouC
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'rouC'}
|
||||
}
|
||||
|
||||
##==============================================================================
|
||||
## Dict with no CV BT formatted df
|
||||
## mmD = {}
|
||||
## for k, v in paramD.items():
|
||||
## # print(mmD[k])
|
||||
## scores_slD = MultModelsCl(**paramD[k]
|
||||
## , tts_split_type = tts_split_sl
|
||||
## , skf_cv = skf_cv
|
||||
## , blind_test_df = X_bts
|
||||
## , blind_test_target = y_bts
|
||||
## , add_cm = True
|
||||
## , add_yn = True
|
||||
## , return_formatted_output = False)
|
||||
## mmD[k] = scores_slD
|
||||
##==============================================================================
|
||||
## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
|
||||
mmDD = {}
|
||||
for k, v in paramD.items():
|
||||
scores_slD = MultModelsCl(**paramD[k]
|
||||
, tts_split_type = tts_split_sl
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_df = X_bts
|
||||
, blind_test_target = y_bts
|
||||
, add_cm = True
|
||||
, add_yn = True
|
||||
, return_formatted_output = True)
|
||||
mmDD[k] = scores_slD
|
||||
|
||||
# Extracting the dfs from within the dict and concatenating to output as one df
|
||||
for k, v in mmDD.items():
|
||||
out_wf_sl = pd.concat(mmDD, ignore_index = True)
|
||||
|
||||
out_wf_slf = out_wf_sl.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
|
||||
|
||||
print('\n######################################################################'
|
||||
, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
|
||||
, '\nGene:', gene.lower()
|
||||
, '\nDrug:', drug
|
||||
, '\noutput file:', outFile_wf
|
||||
, '\nDim of output:', out_wf_slf.shape
|
||||
, '\n######################################################################')
|
||||
###############################################################################
|
||||
#====================
|
||||
# Write output file
|
||||
#====================
|
||||
out_wf_slf.to_csv(outFile_wf, index = False)
|
||||
print('\nFile successfully written:', outFile_wf)
|
||||
###############################################################################
|
||||
Loading…
Add table
Add a link
Reference in a new issue