added combined model FS code and run script
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scripts/ml/boruta_test_clfs.py
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scripts/ml/boruta_test_clfs.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 Fri Sep 2 16:10:44 2022
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@author: tanu
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"""
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from sklearn.ensemble import VotingClassifier
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from sklearn.ensemble import BaggingClassifier
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from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, ExtraTreesClassifier
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from boruta import BorutaPy
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fooD = combined_DF_OS(combined_df)
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numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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print("\nNo. of numerical indices:", len(numerical_ix))
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categorical_ix = fooD['X'].select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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print("\nNo. of categorical indices:", len(categorical_ix))
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var_type = "mixed"
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if var_type == 'mixed':
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix)]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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#--------------ALEX help
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# col_transform
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# col_transform.fit(X)
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# test = col_transform.transform(X)
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# print(col_transform.get_feature_names_out())
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# foo = col_transform.fit_transform(X)
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Xm_train = col_transform.fit_transform(fooD['X'])
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fooD['X'].shape
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Xm_train.shape
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Xm_test = col_transform.fit_transform(fooD['X_bts'])
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fooD['X_bts'].shape
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Xm_test.shape
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X_train = Xm_train.copy()
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X_test = Xm_test.copy()
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X_train.shape
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X_test.shape
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y_train = fooD['y']
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y_test = fooD['y_bts']
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y_train.shape
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y_test.shape
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# perhaps
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#col_transform.fit(fooD['X'])
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#encoded_colnames = pd.Index(col_transform.get_feature_names_out())
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#======================
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# 1 model
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n_jobs = os.cpu_count()
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njobs = {'n_jobs': n_jobs }
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rs = {'random_state': 42}
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rf_all_features = RandomForestClassifier(n_estimators=1000, max_depth=5
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, **rs, **njobs)
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#rf_all_features = VotingClassifier(estimators=1000)
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rf_all_features = BaggingClassifier(random_state=1, n_estimators=100, verbose = 3, **njobs)
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rf_all_features = AdaBoostClassifier(random_state=1, n_estimators=1000)
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rf_all_features = ExtraTreesClassifier(random_state=1, n_estimators=1000, max_depth=5, verbose = 3)
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rf_all_features = DecisionTreeClassifier(random_state=1, max_depth=5)
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rf_all_features.fit(X_train, np.array(y_train))
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accuracy_score(y_test, rf_all_features.predict(X_test))
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matthews_corrcoef(y_test, rf_all_features.predict(X_test))
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# BORUTA
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boruta_selector = BorutaPy(rf_all_features,**rs, verbose = 3)
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boruta_selector.fit(np.array(X_train), np.array(y_train))
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# Tells you how many features: GOOD
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print("Ranking: ", boruta_selector.ranking_)
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print("No. of significant features: ", boruta_selector.n_features_)
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cm_df = combined_df.drop(['gene_name', 'dst', 'dst_mode'], axis = 1)
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col_transform.fit(cm_df)
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col_transform.get_feature_names_out()
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var_type_colnames = col_transform.get_feature_names_out()
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var_type_colnames = pd.Index(var_type_colnames)
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if var_type == 'mixed':
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print('\nVariable type is:', var_type
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, '\nNo. of columns in input_df:', len(cm_df.columns)
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, '\nNo. of columns post one hot encoder:', len(var_type_colnames))
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else:
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print('\nNo. of columns in input_df:', len(input_df.columns))
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selected_rf_features = pd.DataFrame({'Feature':list(var_type_colnames),
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'Ranking':boruta_selector.ranking_})
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sel_rf_features_sorted = selected_rf_features.sort_values(by='Ranking')
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sel_features = var_type_colnames[boruta_selector.support_]
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# tells you the ranking: GOOD
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#foo2 = selected_rf_features.sort_values(by='Ranking')
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X_important_train = boruta_selector.transform(np.array(X_train))
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X_important_test = boruta_selector.transform(np.array(X_test))
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rf_all_features.fit(X_important_train, y_train)
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accuracy_score(y_test, rf_all_features.predict(X_important_test))
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matthews_corrcoef(y_test, rf_all_features.predict(X_important_test))
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280
scripts/ml/combined_model/cm_logo_skf_FS.py
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scripts/ml/combined_model/cm_logo_skf_FS.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 Wed Jun 29 19:44:06 2022
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@author: tanu
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"""
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import sys, os
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import pandas as pd
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import numpy as np
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import re
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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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import itertools
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from sklearn.model_selection import LeaveOneGroupOut
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from boruta import BorutaPy
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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/ml_functions')
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# sys.path
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###############################################################################
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#outdir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
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#====================
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# Import ML functions
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#====================
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#from ml_data_combined import *
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#from MultClfs import *
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#from GetMLData import *
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#from SplitTTS import *
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#skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True, random_state = 42)
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#logo = LeaveOneGroupOut()
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########################################################################
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# COMPLETE data: No tts_split
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########################################################################
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#%%
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def CMLogoSkf_FS(cm_input_df
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, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
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, bts_genes = ["embb", "katg", "rpob", "pnca", "gid"]
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, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
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, target_var = 'dst_mode'
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, gene_group = 'gene_name'
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, std_gene_omit = []
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, var_type = ['numerical', 'categorical','mixed']
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):
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n_jobs = os.cpu_count()
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njobs = {'n_jobs': n_jobs }
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rs = {'random_state': 42}
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cm_gene_featuresD = {}
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for bts_gene in bts_genes:
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print('\n BTS gene:', bts_gene)
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if not std_gene_omit:
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training_genesL = ['alr']
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else:
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training_genesL = []
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tr_gene_omit = std_gene_omit + [bts_gene]
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n_tr_genes = (len(bts_genes) - (len(std_gene_omit)))
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#n_total_genes = (len(bts_genes) - len(std_gene_omit))
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n_total_genes = len(all_genes)
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training_genesL = training_genesL + list(set(bts_genes) - set(tr_gene_omit))
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#training_genesL = [element for element in bts_genes if element not in tr_gene_omit]
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print('\nTotal genes: ', n_total_genes
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,'\nTraining on:', n_tr_genes
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,'\nTraining on genes:', training_genesL
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, '\nOmitted genes:', tr_gene_omit
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, '\nBlind test gene:', bts_gene)
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print('\nDim of data:', cm_input_df.shape)
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tts_split_type = "logo_skf_BT_" + bts_gene
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#-------
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# training
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#------
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cm_training_df = cm_input_df[~cm_input_df['gene_name'].isin(tr_gene_omit)]
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cm_X = cm_training_df.drop(cols_to_drop, axis=1, inplace=False)
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#cm_y = cm_training_df.loc[:,'dst_mode']
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cm_y = cm_training_df.loc[:, target_var]
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gene_group = cm_training_df.loc[:,'gene_name']
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print('\nTraining data dim:', cm_X.shape
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, '\nTraining Target dim:', cm_y.shape)
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if all(cm_X.columns.isin(cols_to_drop) == False):
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print('\nChecked training df does NOT have Target var')
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else:
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sys.exit('\nFAIL: training data contains Target var')
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#---------------
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# BTS: genes
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#---------------
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cm_test_df = cm_input_df[cm_input_df['gene_name'].isin([bts_gene])]
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cm_bts_X = cm_test_df.drop(cols_to_drop, axis = 1, inplace = False)
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#cm_bts_y = cm_test_df.loc[:, 'dst_mode']
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cm_bts_y = cm_test_df.loc[:, target_var]
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print('\nTEST data dim:' , cm_bts_X.shape
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, '\nTEST Target dim:' , cm_bts_y.shape)
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print("Running Multiple models on LOGO with SKF")
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# REASSIGN for simplicity
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# X
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X_train = cm_X.copy()
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X_test = cm_bts_X.copy()
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X_train.shape
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X_test.shape
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# Y
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y_train = cm_y.copy()
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y_test = cm_bts_y.copy()
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y_train.shape
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y_test.shape
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##############################################################################
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#PREPROCESS
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numerical_ix = X_train.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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print("\nNo. of numerical indices:", len(numerical_ix))
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categorical_ix = X_train.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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print("\nNo. of categorical indices:", len(categorical_ix))
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#======================================================
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# Determine preprocessing steps ~ var_type
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#======================================================
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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix)]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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col_transform.fit(X_train)
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col_transform.get_feature_names_out()
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var_type_colnames = col_transform.get_feature_names_out()
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var_type_colnames = pd.Index(var_type_colnames)
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if var_type == 'mixed':
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print('\nVariable type is:', var_type
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, '\nNo. of columns in input_df:', len(X_train.columns)
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, '\nNo. of columns post one hot encoder:', len(var_type_colnames))
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else:
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print('\nNo. of columns in input_df:', len(cm_input_df.columns))
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##############################################################################
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# FS: Random Forest + Boruta
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X_train = col_transform.fit_transform(X_train)
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X_test = col_transform.fit_transform(X_test)
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fs_clf = "RandomForestClassifier"
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rf_all_features = RandomForestClassifier(n_estimators=1000, max_depth=5
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, **rs, **njobs)
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# fit
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rf_all_features.fit(np.array(X_train), np.array(y_train))
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print("RF, baseline MCC:", matthews_corrcoef(y_test, rf_all_features.predict(X_test)))
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# BORUTA and fit
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boruta_selector = BorutaPy(rf_all_features,**rs, verbose = 3)
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boruta_selector.fit(np.array(X_train), np.array(y_train))
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# Get chosen features
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print("Ranking: ", boruta_selector.ranking_)
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print("No. of significant features: ", boruta_selector.n_features_)
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X_important_train = boruta_selector.transform(np.array(X_train))
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X_important_test = boruta_selector.transform(np.array(X_test))
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# just retesting with selected features on RF itselfs
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rf_all_features.fit(X_important_train, y_train)
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print("RF, Boruta MCC:", matthews_corrcoef(y_test, rf_all_features.predict(X_important_test)))
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selected_rf_features = pd.DataFrame({'Feature':list(var_type_colnames),
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'Ranking':boruta_selector.ranking_})
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sel_rf_features_sorted = selected_rf_features.sort_values(by='Ranking')
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sel_features = var_type_colnames[boruta_selector.support_]
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cm_gene_featuresD.update({bts_gene: {
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"sel_features": sel_features
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, "fs_ranking" : sel_rf_features_sorted
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, "fs_model_name": fs_clf
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}
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}
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)
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return(cm_gene_featuresD)
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32
scripts/ml/combined_model/run_cm_logo.py
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scripts/ml/combined_model/run_cm_logo.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 Sat Sep 3 09:43:22 2022
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@author: tanu
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"""
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/home/tanu/git/LSHTM_analysis/scripts/ml/combined_model')
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sys.path.append(homedir + '/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions')
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sys.path.append(homedir + '/home/tanu/git/LSHTM_analysis/scripts/ml')
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from MultClfs import *
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###############################################################################
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#%% RUN: Combined model Baseline
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outdir_cg = "/home/tanu/git/LSHTM_ML/output/combined/"
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#===============
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# Complete Data
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#===============
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CombinedModelML(cm_input_df = combined_df, outdir = outdir_cg, file_suffix = "complete")
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CombinedModelML(cm_input_df = combined_df, outdir = outdir_cg, std_gene_omit=['alr'], file_suffix = "complete")
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#===============
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# Actual Data
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#===============
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CombinedModelML(cm_input_df = combined_df_actual, outdir = outdir_cg, file_suffix = "actual")
|
||||
CombinedModelML(cm_input_df = combined_df_actual, outdir = outdir_cg, std_gene_omit=['alr'], file_suffix = "actual")
|
||||
|
||||
|
||||
204
scripts/ml/combined_model/run_cm_logo_FS.py
Normal file
204
scripts/ml/combined_model/run_cm_logo_FS.py
Normal file
|
|
@ -0,0 +1,204 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Sep 2 19:17:46 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
###############################################################################
|
||||
homedir = os.path.expanduser("~")
|
||||
sys.path.append(homedir + '/home/tanu/git/LSHTM_analysis/scripts/ml/combined_model')
|
||||
sys.path.append(homedir + '/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions')
|
||||
sys.path.append(homedir + '/home/tanu/git/LSHTM_analysis/scripts/ml')
|
||||
|
||||
from MultClfs import *
|
||||
from cm_logo_skf_FS import *
|
||||
|
||||
###############################################################################
|
||||
#%% FS with all genes in training
|
||||
###############################################################################
|
||||
|
||||
# 1. Select Features
|
||||
boruta_features = CMLogoSkf_FS(cm_input_df = combined_df,var_type = 'mixed', file_suffix = "complete")
|
||||
|
||||
# 2. Find original column names of features
|
||||
# if it starts with num__, get rid of num__
|
||||
# if it starts with cat__, get rid of cat__ and the _<number> at the end
|
||||
for i in boruta_features:
|
||||
print(i)
|
||||
boruta_features[i]['sel_features']=[re.sub('^num__|cat__(.*)_\d*$',r'\1', current_thing) for current_thing in boruta_features[i]['sel_features']]
|
||||
boruta_features[i]['sel_features'] = list(set(boruta_features[i]['sel_features']))
|
||||
|
||||
# write json
|
||||
OutFile_6Tgenes = "/home/tanu/git/LSHTM_ML/output/feature_selection/boruta_features_6_Tgenes.json"
|
||||
pd.DataFrame(boruta_features).to_json(path_or_buf=OutFile_6Tgenes)
|
||||
|
||||
# 3. Run all classification models using original column names from (2)
|
||||
combined_df_embb=combined_df[boruta_features['embb']['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_katg=combined_df[boruta_features['katg']['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_pnca=combined_df[boruta_features['pnca']['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_gid= combined_df[boruta_features['gid' ]['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_rpob= combined_df[boruta_features['rpob' ]['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
|
||||
|
||||
# from /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClf.py
|
||||
CombinedModelML(combined_df_embb
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["embb"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = []
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS"
|
||||
)
|
||||
|
||||
|
||||
CombinedModelML(combined_df_katg
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["katg"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = []
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS"
|
||||
)
|
||||
|
||||
|
||||
CombinedModelML(combined_df_pnca
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["pnca"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = []
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS"
|
||||
)
|
||||
|
||||
CombinedModelML(combined_df_gid
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["gid"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = []
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS"
|
||||
)
|
||||
|
||||
CombinedModelML(combined_df_rpob
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["rpob"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = []
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS"
|
||||
)
|
||||
|
||||
|
||||
# write all feature rankings
|
||||
for i in boruta_features:
|
||||
print (i)
|
||||
gene_fs_ranking = boruta_features[i]['fs_ranking']
|
||||
gene_fs_ranking.to_csv("/home/tanu/git/LSHTM_ML/output/feature_selection/"+ i + "_boruta_featues_6Tgenes.csv")
|
||||
|
||||
|
||||
###############################################################################
|
||||
#%% FS withour training including ALR
|
||||
###############################################################################
|
||||
# With training omitting alr
|
||||
boruta_features_omit_alr = CMLogoSkf_FS(cm_input_df = combined_df
|
||||
, std_gene_omit = ['alr']
|
||||
, var_type = 'mixed')
|
||||
|
||||
# 2. Find original column names of features
|
||||
# if it starts with num__, get rid of num__
|
||||
# if it starts with cat__, get rid of cat__ and the _<number> at the end
|
||||
for i in boruta_features_omit_alr:
|
||||
print(i)
|
||||
boruta_features_omit_alr[i]['sel_features']=[re.sub('^num__|cat__(.*)_\d*$',r'\1', current_thing) for current_thing in boruta_features[i]['sel_features']]
|
||||
boruta_features_omit_alr[i]['sel_features'] = list(set(boruta_features_omit_alr[i]['sel_features']))
|
||||
|
||||
# write json
|
||||
OutFile_5Tgenes = "/home/tanu/git/LSHTM_ML/output/feature_selection/boruta_features_5_Tgenes.json"
|
||||
pd.DataFrame(boruta_features_omit_alr).to_json(path_or_buf=OutFile_5Tgenes)
|
||||
|
||||
# 3. Run all classification models using original column names from (2)
|
||||
cm_input_df5 = combined_df[~combined_df['gene_name'].isin(omit_gene_alr)]
|
||||
|
||||
combined_df_embb_no_alr = cm_input_df5[boruta_features_omit_alr['embb']['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_katg_no_alr = cm_input_df5[boruta_features_omit_alr['katg']['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_pnca_no_alr = cm_input_df5[boruta_features_omit_alr['pnca']['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_gid_no_alr = cm_input_df5[boruta_features_omit_alr['gid' ]['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
combined_df_rpob_no_alr = cm_input_df5[boruta_features_omit_alr['rpob' ]['sel_features']+['dst', 'dst_mode', 'gene_name']]
|
||||
|
||||
|
||||
CombinedModelML(combined_df_embb_no_alr
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["embb"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = ["alr"]
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS_no_Talr"
|
||||
)
|
||||
|
||||
|
||||
CombinedModelML(combined_df_katg_no_alr
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["katg"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = ["alr"]
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS_no_Talr"
|
||||
)
|
||||
|
||||
|
||||
CombinedModelML(combined_df_pnca_no_alr
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["pnca"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = ["alr"]
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS_no_Talr"
|
||||
)
|
||||
|
||||
CombinedModelML(combined_df_gid_no_alr
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["gid"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = ["alr"]
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS_no_Talr"
|
||||
)
|
||||
|
||||
CombinedModelML(combined_df_rpob_no_alr
|
||||
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
|
||||
, bts_genes = ["rpob"]
|
||||
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
|
||||
, target_var = 'dst_mode'
|
||||
, gene_group = 'gene_name'
|
||||
, std_gene_omit = ["alr"]
|
||||
, output_dir = "/home/tanu/git/LSHTM_ML/output/feature_selection/"
|
||||
, file_suffix = "FS_no_Talr"
|
||||
)
|
||||
|
||||
|
||||
# write all feature rankings
|
||||
for i in boruta_features_omit_alr:
|
||||
print (i)
|
||||
gene_fs_ranking_no_alr = boruta_features_omit_alr[i]['fs_ranking']
|
||||
gene_fs_ranking_no_alr.to_csv("/home/tanu/git/LSHTM_ML/output/feature_selection/"+ i + "_boruta_featues_5Tgenes.csv")
|
||||
|
||||
|
||||
52
scripts/ml/untitled5.py
Normal file
52
scripts/ml/untitled5.py
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Sep 2 11:11:49 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
# https://towardsdatascience.com/explain-feature-variation-employing-pca-in-scikit-learn-6711e0a5c0b7
|
||||
from sklearn.decomposition import PCA
|
||||
#import tensorflow as tf
|
||||
#from tensorflow import keras
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
from sklearn.metrics import matthews_corrcoef
|
||||
|
||||
# pca = PCA().fit(X)
|
||||
# plt.plot(np.cumsum(pca.explained_variance_ratio_))
|
||||
# plt.xlabel(‘number of components’)
|
||||
# plt.ylabel(‘cumulative explained variance’)
|
||||
|
||||
# from old scripts
|
||||
fooD = combined_DF_OS(combined_df)
|
||||
|
||||
numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_ix
|
||||
num_featuresL = list(numerical_ix)
|
||||
numerical_colind = fooD['X'].columns.get_indexer(list(numerical_ix) )
|
||||
numerical_colind
|
||||
|
||||
numF = fooD['X'][numerical_ix]
|
||||
|
||||
categorical_ix = fooD['X'].select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_ix
|
||||
categorical_colind = fooD['X'].columns.get_indexer(list(categorical_ix))
|
||||
categorical_colind
|
||||
|
||||
##############
|
||||
|
||||
X_train,X_test,y_train,y_test=train_test_split(numF,fooD['y'],test_size=0.2)
|
||||
|
||||
pca=PCA(n_components=50)
|
||||
X_train_new=pca.fit_transform(X_train)
|
||||
X_test_new=pca.transform(X_test)
|
||||
print(X_train.shape)
|
||||
print(X_train_new.shape)
|
||||
|
||||
pca.explained_variance_ratio_
|
||||
clf=KNeighborsClassifier(n_neighbors=5)
|
||||
clf.fit(X_train_new,y_train)
|
||||
y_pred_new=clf.predict(X_test_new)
|
||||
matthews_corrcoef(y_test,y_pred_new)
|
||||
136
scripts/ml/untitled6.py
Normal file
136
scripts/ml/untitled6.py
Normal file
|
|
@ -0,0 +1,136 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Sep 2 11:30:18 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#https://github.com/yuneeham/PCA-and-feature-selection_sklearn/blob/main/Report%20-%20PCA%20and%20Feature%20Selection.pdf
|
||||
|
||||
#Load Libraries
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn import datasets
|
||||
from sklearn.preprocessing import scale
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.ensemble import RandomForestRegressor
|
||||
from sklearn import metrics
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.metrics import classification_report
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.feature_selection import SelectFromModel
|
||||
|
||||
#Load Data
|
||||
df = pd.read_csv("/home/tanu/Downloads/data.csv")
|
||||
X = df.loc[:, ' ROA(C) before interest and depreciation before interest':' Equity to Liability'].values
|
||||
y = df.loc[:,['Bankrupt?']].values
|
||||
|
||||
fn = df.loc[:, ' ROA(C) before interest and depreciation before interest':' Equity to Liability'].keys()
|
||||
|
||||
#Scaler/normalize
|
||||
scaler = StandardScaler()
|
||||
Xn = scaler.fit_transform(X)
|
||||
|
||||
#PCA
|
||||
pca_prep = PCA().fit(Xn)
|
||||
pca_prep.n_components_
|
||||
|
||||
|
||||
#PCA Explained Variance
|
||||
pca_prep.explained_variance_
|
||||
plt.plot(pca_prep.explained_variance_ratio_)
|
||||
|
||||
#Graph plot - PCA components
|
||||
plt.plot(pca_prep.explained_variance_ratio_)
|
||||
plt.xlabel('k number of components')
|
||||
plt.ylabel('Explained variance')
|
||||
plt.grid(True)
|
||||
plt.show()
|
||||
|
||||
#Number of components
|
||||
n_pc = 17
|
||||
pca = PCA(n_components = n_pc).fit(Xn)
|
||||
Xp = pca.transform(Xn)
|
||||
print(f'After PCA, we use {pca.n_components_} components. \n')
|
||||
|
||||
|
||||
# Split the data into training and testing subsets.
|
||||
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size =.2,random_state=1234,stratify=y)
|
||||
Xp_train, Xp_test, yp_train, yp_test = train_test_split(Xp,y,test_size =.2,random_state=1234,stratify=y)
|
||||
|
||||
|
||||
#Random Forest Model
|
||||
rfcm = RandomForestClassifier().fit(X_train, y_train) #Original Data
|
||||
rfcm_p = RandomForestClassifier().fit(Xp_train, yp_train) #Reduced Data
|
||||
|
||||
|
||||
#Prediction
|
||||
y_pred = rfcm.predict(X_test)
|
||||
y_pred_p = rfcm_p.predict(Xp_test)
|
||||
|
||||
|
||||
# Compare the performance of each mode
|
||||
report_original = classification_report(y_test, y_pred)
|
||||
report_pca = classification_report(yp_test, y_pred_p)
|
||||
print(f'Classification Report - original\n{report_original}')
|
||||
print(f'Classification Report - pca\n{report_pca}')
|
||||
|
||||
|
||||
|
||||
## Feature selection and performance comparison
|
||||
|
||||
# Draw a bar chart to see the sorted importance values with feature names.
|
||||
# Horizontal Bar Chart
|
||||
# %matplotlib auto
|
||||
# %matplotlib inline
|
||||
|
||||
importances = rfcm.feature_importances_
|
||||
np.sum(importances)
|
||||
plt.barh(fn,importances)
|
||||
|
||||
df_importances = pd.DataFrame(data=importances, index=fn,
|
||||
columns=['importance_value'])
|
||||
df_importances.sort_values(by = 'importance_value', ascending=True,
|
||||
inplace=True)
|
||||
|
||||
plt.barh(df_importances.index,df_importances.importance_value)
|
||||
|
||||
|
||||
# Build a model with a subset of those features.
|
||||
selector = SelectFromModel(estimator=RandomForestClassifier(),threshold=0.015)
|
||||
X_reduced = selector.fit_transform(X,y)
|
||||
selector.threshold_
|
||||
selected_TF = selector.get_support()
|
||||
print(f'\n** {selected_TF.sum()} features are selected.')
|
||||
X_reduced.shape
|
||||
|
||||
|
||||
# Show those selected features.
|
||||
selected_features = []
|
||||
for i,j in zip(selected_TF, fn):
|
||||
if i: selected_features.append(j)
|
||||
print(f'Selected Features: {selected_features}')
|
||||
|
||||
|
||||
# First 5 features
|
||||
print(selected_features[0:5])
|
||||
|
||||
|
||||
# Build a model using those reduced number of features.
|
||||
X_reduced_train, X_reduced_test, y_reduced_train, y_reduced_test \
|
||||
= train_test_split(X_reduced,y,test_size =.3, stratify=y)
|
||||
|
||||
|
||||
# Build a model with the reduced number of features.
|
||||
rfcm2 = RandomForestClassifier().fit(X_reduced_train, y_reduced_train)
|
||||
y_reduced_pred = rfcm2.predict(X_reduced_test)
|
||||
|
||||
|
||||
#Classification for Reduced Data
|
||||
print('\nClassification Report after feature reduction\n')
|
||||
print(metrics.classification_report(y_reduced_test,y_reduced_pred))
|
||||
|
||||
|
||||
|
||||
221
scripts/ml/untitled7_boruta.py
Normal file
221
scripts/ml/untitled7_boruta.py
Normal file
|
|
@ -0,0 +1,221 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Sep 2 12:13:53 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
# https://analyticsindiamag.com/hands-on-guide-to-automated-feature-selection-using-boruta/
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from boruta import BorutaPy
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.metrics import accuracy_score
|
||||
from sklearn.metrics import matthews_corrcoef
|
||||
|
||||
|
||||
URL = "https://raw.githubusercontent.com/Aditya1001001/English-Premier-League/master/pos_modelling_data.csv"
|
||||
data = pd.read_csv(URL)
|
||||
data.info()
|
||||
X = data.drop('Position', axis = 1)
|
||||
y = data['Position']
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 1)
|
||||
|
||||
rf_all_features = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
rf_all_features.fit(X_train, y_train)
|
||||
|
||||
|
||||
y_pred = rf_all_features.predict(X_test)
|
||||
|
||||
accuracy_score(y_test, rf_all_features.predict(X_test))
|
||||
accuracy_score(y_test, y_pred)
|
||||
matthews_corrcoef(y_test, rf_all_features.predict(X_test))
|
||||
|
||||
# BORUTA
|
||||
rfc = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
boruta_selector = BorutaPy(rfc, n_estimators='auto', verbose=2, random_state=1)
|
||||
boruta_selector.fit(np.array(X_train), np.array(y_train))
|
||||
|
||||
# Tells you how many features: GOOD
|
||||
print("Ranking: ",boruta_selector.ranking_)
|
||||
print("No. of significant features: ", boruta_selector.n_features_)
|
||||
|
||||
selected_rf_features = pd.DataFrame({'Feature':list(X_train.columns),
|
||||
'Ranking':boruta_selector.ranking_})
|
||||
|
||||
# tells you the ranking: GOOD
|
||||
selected_rf_features.sort_values(by='Ranking')
|
||||
|
||||
X_important_train = boruta_selector.transform(np.array(X_train))
|
||||
X_important_test = boruta_selector.transform(np.array(X_test))
|
||||
|
||||
rf_boruta = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
rf_boruta.fit(X_important_train, y_train)
|
||||
accuracy_score(y_test, rf_boruta.predict(X_important_test))
|
||||
matthews_corrcoef(y_test, rf_boruta.predict(X_important_test))
|
||||
|
||||
|
||||
|
||||
##############################################################################
|
||||
# my data : ONLY numerical values
|
||||
# from old scripts (cm_logo_skf_v2.py)
|
||||
fooD = combined_DF_OS(combined_df)
|
||||
|
||||
allF = fooD['X']
|
||||
numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_ix
|
||||
# just numerical for X_train and X_test
|
||||
X_train_numF = fooD['X'][numerical_ix]
|
||||
X_test_numF = fooD['X_bts'][numerical_ix]
|
||||
#X_train = allF
|
||||
|
||||
X_train = X_train_numF
|
||||
X_test = X_test_numF
|
||||
|
||||
y_train = fooD['y']
|
||||
y_test = fooD['y_bts']
|
||||
|
||||
# 1 model
|
||||
rf_all_features = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
rf_all_features.fit(X_train, y_train)
|
||||
|
||||
accuracy_score(y_test, rf_all_features.predict(X_test))
|
||||
matthews_corrcoef(y_test, rf_all_features.predict(X_test))
|
||||
|
||||
|
||||
# BORUTA
|
||||
rfc = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
boruta_selector = BorutaPy(rfc, n_estimators='auto', verbose=2, random_state=1)
|
||||
boruta_selector.fit(np.array(X_train), np.array(y_train))
|
||||
|
||||
# Tells you how many features: GOOD
|
||||
print("Ranking: ",boruta_selector.ranking_)
|
||||
print("No. of significant features: ", boruta_selector.n_features_)
|
||||
|
||||
selected_rf_features = pd.DataFrame({'Feature':list(X_train.columns),
|
||||
'Ranking':boruta_selector.ranking_})
|
||||
|
||||
# tells you the ranking: GOOD
|
||||
selected_rf_features.sort_values(by='Ranking')
|
||||
|
||||
X_important_train = boruta_selector.transform(np.array(X_train))
|
||||
X_important_test = boruta_selector.transform(np.array(X_test))
|
||||
|
||||
rf_boruta = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
rf_boruta.fit(X_important_train, y_train)
|
||||
accuracy_score(y_test, rf_boruta.predict(X_important_test))
|
||||
matthews_corrcoef(y_test, rf_boruta.predict(X_important_test))
|
||||
|
||||
##############################################################################
|
||||
# my data : using both numerical and categorical
|
||||
# from old scripts (cm_logo_skf_v2.py)
|
||||
fooD = combined_DF_OS(combined_df)
|
||||
|
||||
numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_ix
|
||||
print("\nNo. of numerical indices:", len(numerical_ix))
|
||||
|
||||
categorical_ix = fooD['X'].select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_ix
|
||||
print("\nNo. of categorical indices:", len(categorical_ix))
|
||||
|
||||
|
||||
var_type = "mixeds"
|
||||
|
||||
if var_type == 'mixed':
|
||||
|
||||
t = [('num', MinMaxScaler(), numerical_ix)
|
||||
, ('cat', OneHotEncoder(), categorical_ix)]
|
||||
|
||||
col_transform = ColumnTransformer(transformers = t
|
||||
, remainder='passthrough')
|
||||
#--------------ALEX help
|
||||
# col_transform
|
||||
# col_transform.fit(X)
|
||||
# test = col_transform.transform(X)
|
||||
# print(col_transform.get_feature_names_out())
|
||||
|
||||
# foo = col_transform.fit_transform(X)
|
||||
Xm_train = col_transform.fit_transform(fooD['X'])
|
||||
fooD['X'].shape
|
||||
Xm_train.shape
|
||||
|
||||
Xm_test = col_transform.fit_transform(fooD['X_bts'])
|
||||
fooD['X_bts'].shape
|
||||
Xm_test.shape
|
||||
|
||||
X_train = Xm_train.copy()
|
||||
X_test = Xm_test.copy()
|
||||
X_train.shape
|
||||
X_test.shape
|
||||
|
||||
y_train = fooD['y']
|
||||
y_test = fooD['y_bts']
|
||||
y_train.shape
|
||||
y_test.shape
|
||||
|
||||
# perhaps
|
||||
#col_transform.fit(fooD['X'])
|
||||
#encoded_colnames = pd.Index(col_transform.get_feature_names_out())
|
||||
#======================
|
||||
# 1 model
|
||||
rf_all_features = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
rf_all_features.fit(X_train, y_train)
|
||||
|
||||
accuracy_score(y_test, rf_all_features.predict(X_test))
|
||||
matthews_corrcoef(y_test, rf_all_features.predict(X_test))
|
||||
|
||||
|
||||
# BORUTA
|
||||
rfc = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
boruta_selector = BorutaPy(rfc, n_estimators='auto', verbose=2, random_state=1)
|
||||
boruta_selector.fit(np.array(X_train), np.array(y_train))
|
||||
|
||||
# Tells you how many features: GOOD
|
||||
print("Ranking: ",boruta_selector.ranking_)
|
||||
print("No. of significant features: ", boruta_selector.n_features_)
|
||||
|
||||
#selected_rf_features = pd.DataFrame({'Feature':list(X_train.columns),
|
||||
# 'Ranking':boruta_selector.ranking_})
|
||||
|
||||
# tells you the ranking: GOOD
|
||||
foo2 = selected_rf_features.sort_values(by='Ranking')
|
||||
|
||||
X_important_train = boruta_selector.transform(np.array(X_train))
|
||||
X_important_test = boruta_selector.transform(np.array(X_test))
|
||||
|
||||
rf_boruta = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)
|
||||
rf_boruta.fit(X_important_train, y_train)
|
||||
accuracy_score(y_test, rf_boruta.predict(X_important_test))
|
||||
matthews_corrcoef(y_test, rf_boruta.predict(X_important_test))
|
||||
##################################
|
||||
# trying to one hot encode at start
|
||||
# perhaps
|
||||
#col_transform.fit(fooD['X'])
|
||||
#encoded_colnames = pd.Index(col_transform.get_feature_names_out())
|
||||
|
||||
# def encode_and_bind(original_dataframe, feature_to_encode):
|
||||
# dummies = pd.get_dummies(original_dataframe[[feature_to_encode]])
|
||||
# res = pd.concat([original_dataframe, dummies], axis=1)
|
||||
# res = res.drop([feature_to_encode], axis=1)
|
||||
# return(res)
|
||||
|
||||
# features_to_encode = ['feature_1', 'feature_2', 'feature_3',
|
||||
# 'feature_4']
|
||||
|
||||
# features_to_encode = list(categorical_ix.copy())
|
||||
|
||||
# for feature in features_to_encode:
|
||||
# X_train_enc = encode_and_bind(fooD['X'], feature)
|
||||
# X_test_enc = encode_and_bind(fooD['X_bts'], feature)
|
||||
|
||||
# c1 = X_train_enc.columns
|
||||
# c2 = X_test_enc.columns
|
||||
# X_train_enc.shape
|
||||
# X_test_enc.shape
|
||||
|
||||
# This one is better!
|
||||
a = pd.get_dummies(combined_df, columns=features_to_encode)
|
||||
a1=a.columns
|
||||
a2 = a.drop(['gene_name', 'dst', 'dst_mode'])
|
||||
Loading…
Add table
Add a link
Reference in a new issue