saving work with scripts for feature selection
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UQ_LR.py
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UQ_LR.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 May 16 05:59:12 2022
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@author: tanu
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"""
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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 Mar 15 11:09:50 2022
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@author: tanu
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"""
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#%% Import libs
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import numpy as np
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import pandas as pd
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from sklearn.model_selection import GridSearchCV
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from sklearn import datasets
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.svm import SVC
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from sklearn.base import BaseEstimator
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.linear_model import SGDClassifier
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import GridSearchCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from xgboost import XGBClassifier
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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#%% Get train-test split and scoring functions
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# X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
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# , num_df_wtgt['mutation_class']
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# , test_size = 0.33
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# , random_state = 2
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# , shuffle = True
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# , stratify = num_df_wtgt['mutation_class'])
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y.to_frame().value_counts().plot(kind = 'bar')
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blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
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scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jaccard' : make_scorer(jaccard_score)
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})
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
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class ClfSwitcher(BaseEstimator):
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def __init__(
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self,
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estimator = SGDClassifier(),
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):
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"""
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A Custom BaseEstimator that can switch between classifiers.
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:param estimator: sklearn object - The classifier
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"""
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self.estimator = estimator
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def fit(self, X, y=None, **kwargs):
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self.estimator.fit(X, y)
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return self
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def predict(self, X, y=None):
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return self.estimator.predict(X)
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def predict_proba(self, X):
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return self.estimator.predict_proba(X)
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def score(self, X, y):
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return self.estimator.score(X, y)
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parameters = [
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{
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'clf__estimator': [LogisticRegression(**rs)],
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#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'clf__estimator__C': np.logspace(0, 4, 10),
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'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__estimator__max_iter': list(range(100,800,100)),
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'clf__estimator__solver': ['saga']
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},
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{
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'clf__estimator': [LogisticRegression(**rs)],
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#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'clf__estimator__C': np.logspace(0, 4, 10),
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'clf__estimator__penalty': ['l2', 'none'],
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'clf__estimator__max_iter': list(range(100,800,100)),
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'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
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},
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{
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'clf__estimator': [LogisticRegression(**rs)],
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#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'clf__estimator__C': np.logspace(0, 4, 10),
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'clf__estimator__penalty': ['l1', 'l2'],
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'clf__estimator__max_iter': list(range(100,800,100)),
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'clf__estimator__solver': ['liblinear']
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}
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]
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# Create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler()),
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('clf', ClfSwitcher()),
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])
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# Grid search i.e hyperparameter tuning and refitting on mcc
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gscv_lr = GridSearchCV(pipeline
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, parameters
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#, scoring = 'f1', refit = 'f1'
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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, **njobs
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, return_train_score = False
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, verbose = 3)
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# Fit
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gscv_lr_fit = gscv_lr.fit(X, y)
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gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
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gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
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print('Best model:\n', gscv_lr_fit_be_mod)
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print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
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#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
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print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
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######################################
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# Blind test
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######################################
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# See how it does on the BLIND test
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#print('\nBlind test score, mcc:', ))
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test_predict = gscv_lr_fit.predict(X_bts)
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print(test_predict)
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print(np.array(y_bts))
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y_btsf = np.array(y_bts)
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print(accuracy_score(y_bts, test_predict))
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print(matthews_corrcoef(y_bts, test_predict))
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# create a dict with all scores
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lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
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'bts_fscore':None
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, 'bts_mcc':None
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, 'bts_precision':None
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, 'bts_recall':None
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, 'bts_accuracy':None
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, 'bts_roc_auc':None
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, 'bts_jaccard':None }
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lr_bts_dict
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lr_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
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lr_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
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lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
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lr_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
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lr_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
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lr_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
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lr_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
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lr_bts_dict
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# Create a df from dict with all scores
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pd.DataFrame.from_dict(lr_bts_dict, orient = 'index', columns = 'best_model')
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lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
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lr_bts_df.columns = ['Logistic_Regression']
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print(lr_bts_df)
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# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
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# d2
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# def Merge(dict1, dict2):
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# res = {**dict1, **dict2}
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# return res
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# d3 = Merge(d2, lr_bts_dict)
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# d3
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# Create df with best model params
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model_params = pd.Series(['best_model_params', list(gscv_lr_fit_be_mod.items() )])
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model_params_df = model_params.to_frame()
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model_params_df
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model_params_df.columns = ['Logistic_Regression']
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model_params_df.columns
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# Combine the df of scores and the best model params
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lr_bts_df.columns
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lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
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lr_output
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# Format the combined df
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# Drop the best_model_params row from lr_output
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lr_df = lr_output.drop([0], axis = 0)
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lr_df
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#FIXME: tidy the index of the formatted df
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###############################################################################
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4
UQ_RF.py
4
UQ_RF.py
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'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
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'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
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, 'clf__estimator__class_weight':['balanced','balanced_subsample']
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, 'clf__estimator__class_weight':['balanced','balanced_subsample']
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, 'clf__estimator__n_estimators': [10, 25, 50, 100]
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, 'clf__estimator__n_estimators': [10, 25, 50, 100]
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, 'clf__estimator__criterion': ['gini', 'entropy']#, 'log_loss']
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, 'clf__estimator__criterion': ['gini', 'entropy', 'log_loss']
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#, 'clf__estimator__max_features': ['auto', 'sqrt']
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, 'clf__estimator__max_features': ['sqrt', 'log2', None] #deafult is sqrt
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, 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
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, 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
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, 'clf__estimator__min_samples_split': [2, 5, 15, 20]
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, 'clf__estimator__min_samples_split': [2, 5, 15, 20]
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}
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}
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@ -207,8 +207,8 @@ X_genomicFN = ['maf'
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# , 'or_fisher'
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# , 'or_fisher'
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# , 'pval_fisher'
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# , 'pval_fisher'
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#, 'lineage'
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#, 'lineage'
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, 'lineage_count_all'
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#, 'lineage_count_all'
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, 'lineage_count_unique'
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#, 'lineage_count_unique'
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]
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]
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#%% Construct numerical and categorical column names
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#%% Construct numerical and categorical column names
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@ -256,7 +256,7 @@ all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
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all_df_wtgt.shape
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all_df_wtgt.shape
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#%%================================================================
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#%%================================================================
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#%% Apply ML
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#%% Apply ML
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#TODO: Apply oversampling!
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#TODO: A
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#%% Data
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#%% Data
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#X = all_df_wtgt[numerical_FN+categorical_FN]
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#X = all_df_wtgt[numerical_FN+categorical_FN]
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@ -272,16 +272,16 @@ X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
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# Quick check
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# Quick check
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(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
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(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
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#%% MultClassPipeSKFCV: function call()
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#%% MultClassPipeSKFCV: function call()
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mm_skf_scoresD = MultClassPipeSKFCV(input_df = X
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# mm_skf_scoresD = MultClassPipeSKFCV(input_df = X
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, target = y
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# , target = y
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, var_type = 'numerical'
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# , var_type = 'numerical'
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, skf_cv = skf_cv)
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# , skf_cv = skf_cv)
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mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
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# mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
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mm_skf_scores_df_all
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# mm_skf_scores_df_all
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mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
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# mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
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mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
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# mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
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print(mm_skf_scores_df_train)
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# print(mm_skf_scores_df_train)
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print(mm_skf_scores_df_test)
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# print(mm_skf_scores_df_test)
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