adding prints to pnca_config file
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693a5324c1
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2 changed files with 132 additions and 20 deletions
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@ -10,7 +10,7 @@ import os
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gene = 'pncA'
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drug = 'pyrazinamide'
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total_mtblineage_u = 8
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#total_mtblineage_u = 8
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homedir = os.path.expanduser("~")
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@ -22,5 +22,29 @@ from UQ_ML_data import *
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# from YC run_all_ML: run locally
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from UQ_MultModelsCl import MultModelsCl
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print('TESTING cmd:'
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, '\nGene name:', gene
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, '\nDrug name:', drug
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, '\nTotal input features:', X.shape
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, '\n', Counter(y))
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print('TESTING cmd:', Counter(y))
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print('Strucutral features (n):'
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, len(common_cols_stabiltyN) + len(foldX_cols) + len(X_strFN)
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, '\nThese are:'
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, '\nCommon stablity features:', common_cols_stabiltyN
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, '\nFoldX columns:', foldX_cols
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, '\nOther struc columns:', X_strFN
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, '\n================================================================\n')
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print('Evolutionary features (n):'
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, len(X_evolFN)
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, '\nThese are:\n'
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, X_evolFN
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, '\n================================================================\n')
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print('Genomic features (n):'
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, len(X_genomicFN)
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, '\nThese are:\n'
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, X_genomic_mafor, '\n'
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, X_genomic_linegae
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, '\n================================================================\n')
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@ -26,6 +26,72 @@ from sklearn.pipeline import Pipeline, make_pipeline
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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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# Metric
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from sklearn.metrics import mean_squared_error, make_scorer, roc_auc_score, f1_score, matthews_corrcoef, accuracy_score, balanced_accuracy_score, confusion_matrix, classification_report
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###############################################################################
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# TT imports
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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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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 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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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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##############################################################################
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# other vars
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rs = {'random_state': 42}
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@ -119,10 +185,25 @@ def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, p
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_roc_auc = round(roc_auc_score(y_pred, y), 3)
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_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel()
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result_pd = result_pd.append(pd.DataFrame(np.column_stack([name, _tp, _tn, _fp, _fn, _roc_auc, _mcc, _bacc, _f1]),\
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columns=['estimator', 'TP', 'TN', 'FP', 'FN',
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'roc_auc', 'matthew', 'bacc', 'f1']),\
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ignore_index=True)
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# result_pd = result_pd.append(pd.DataFrame(np.column_stack([name
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# , _tp, _tn
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# , _fp , _fn
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# , _roc_auc
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# , _mcc
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# , _bacc, _f1]),\
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# columns=['estimator', 'TP', 'TN', 'FP', 'FN',
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# 'roc_auc', 'matthew', 'bacc', 'f1']),\
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# ignore_index=True)
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result_pd = result_pd.append(pd.DataFrame(np.column_stack([name
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, _mcc
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, _roc_auc
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, _bacc, _f1
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, _tp, _tn
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, _fp , _fn]),\
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columns=['estimator', 'matthew', 'roc_auc', 'bacc', 'f1',\
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'TP', 'TN', 'FP', 'FN']),\
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ignore_index=True)
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#=========================
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# Blind test: BTS results
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#=========================
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@ -140,15 +221,24 @@ def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, p
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_roc_aucBTS = round(roc_auc_score(bts_predict, blind_test_target), 3)
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_tnBTS, _fpBTS, _fnBTS, _tpBTS = confusion_matrix(bts_predict, blind_test_target).ravel()
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# result_bts_pd = result_bts_pd.append(pd.DataFrame(np.column_stack([name
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# , _tpBTS, _tnBTS
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# , _fpBTS, _fnBTS
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# , _roc_aucBTS
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# , _mccBTS
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# , _baccBTS, _f1BTS]),\
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# columns=['estimator', 'TP', 'TN', 'FP', 'FN',
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# 'roc_auc', 'matthew', 'bacc', 'f1']),\
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# ignore_index=True)
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result_bts_pd = result_bts_pd.append(pd.DataFrame(np.column_stack([name
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, _tpBTS, _tnBTS
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, _fpBTS, _fnBTS
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, _roc_aucBTS
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, _mccBTS
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, _baccBTS, _f1BTS]),\
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columns=['estimator', 'TP', 'TN', 'FP', 'FN',
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'roc_auc', 'matthew', 'bacc', 'f1']),\
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ignore_index=True)
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, _roc_aucBTS
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, _baccBTS, _f1BTS
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, _tpBTS, _tnBTS
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, _fpBTS, _fnBTS]),\
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columns=['estimator','matthew', 'roc_auc', 'bacc', 'f1',\
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'TP', 'TN', 'FP', 'FN']),\
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ignore_index=True)
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results_all['CrossValResultsDF'] = result_pd
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@ -165,15 +255,13 @@ def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, p
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#%% CALL function
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#run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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# Baseline_data
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YC_resD2 = run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF = YC_resD2['CrossValResultsDF']
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CVResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF = YC_resD2['BlindTestResultsDF']
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BTSResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
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CVResultsDF_baseline = YC_resD2['CrossValResultsDF']
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CVResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF']
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BTSResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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# from sklearn.utils import all_estimators
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# for name, algorithm in all_estimators(type_filter="classifier"):
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