172 lines
No EOL
5 KiB
Python
172 lines
No EOL
5 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 3 17:08:18 2022
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@author: tanu
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"""
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from sklearn.linear_model import LogisticRegression
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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
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.neural_network import MLPClassifier
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from sklearn.pipeline import Pipeline
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from xgboost import XGBClassifier
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from sklearn.preprocessing import StandardScaler
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.model_selection import train_test_split
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import os
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from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
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import pandas as pd
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#%%
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homedir = os.path.expanduser("~")
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os.chdir(homedir + "/git/ML_AI_training/test_data")
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#gene 'pncA'
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#drug = 'pyrazinamide'
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#==============
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# directories
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#==============
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datadir = homedir + '/git/Data/'
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indir = datadir + drug + '/input/'
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outdir = datadir + drug + '/output/'
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#=======
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# input
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#=======
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# this needs to be merged_df2 or merged_df3?
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infile_ml1 = outdir + gene.lower() + '_merged_df3.csv'
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#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
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my_df = pd.read_csv(infile_ml1)
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my_df.dtypes
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my_df_cols = my_df.columns
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gene_baiscL = ['pnca']
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geneL_naL = ['gid', 'rpob']
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geneL_ppi2L = ['alr', 'embb', 'katg', 'rpob']
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#%%============================================================================
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# GET Y
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# Y = my_df.loc[:,drug] #has NA
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dm_om_map = {'DM': 1, 'OM': 0}
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my_df['resistance'] = my_df['mutation_info_labels'].map(dm_om_map)
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# sanity check
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my_df['resistance'].value_counts()
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my_df['mutation_info_labels'].value_counts()
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Y = my_df['resistance']
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# GET X
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cols = my_df.columns
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X_stability = my_df[['ligand_distance'
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, 'ligand_affinity_change'
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, 'duet_stability_change'
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, 'ddg_foldx'
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, 'deepddg'
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, 'ddg_dynamut2']]
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X_evol = my_df[['consurf_score'
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, 'snap2_score'
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, 'snap2_accuracy_pc']]
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X_str = my_df[['asa'
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, 'rsa'
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, 'kd_values'
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, 'rd_values']]
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#%% try combinations
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X_vars = X_stability
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X_vars = X_evol
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X_vars = X_str
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X_vars = pd.concat([X_stability, X_evol, X_str], axis = 1)
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X_vars = pd.concat([X_stability, X_evol], axis = 1)
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X_vars = pd.concat([X_stability, X_str], axis = 1)
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X_vars = pd.concat([X_evol, X_str], axis = 1)
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#%%
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X_vars.shape[1]
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# TODO: stratified cross validate
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# Train-test Split
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rs = {'random_state': 42}
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X_train, X_test, y_train, y_test = train_test_split(X_vars,
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Y,
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test_size = 0.33,
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random_state = 42)
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# Classification - Model Pipeline
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def modelPipeline(X_train, X_test, y_train, y_test):
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log_reg = LogisticRegression(**rs)
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nb = BernoulliNB()
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knn = KNeighborsClassifier()
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svm = SVC(**rs)
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mlp = MLPClassifier(max_iter=500, **rs)
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dt = DecisionTreeClassifier(**rs)
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et = ExtraTreesClassifier(**rs)
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rf = RandomForestClassifier(**rs)
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xgb = XGBClassifier(**rs, verbosity=0)
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clfs = [
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('Logistic Regression', log_reg),
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('Naive Bayes', nb),
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('K-Nearest Neighbors', knn),
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('SVM', svm),
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('MLP', mlp),
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('Decision Tree', dt),
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('Extra Trees', et),
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('Random Forest', rf),
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('XGBoost', xgb)
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]
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pipelines = []
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scores_df = pd.DataFrame(columns=['Model', 'F1_Score', 'Precision', 'Recall', 'Accuracy', 'ROC_AUC'])
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for clf_name, clf in clfs:
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pipeline = Pipeline(steps=[
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('scaler', MinMaxScaler()),
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('classifier', clf)
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]
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)
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pipeline.fit(X_train, y_train)
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# Model predictions
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y_pred = pipeline.predict(X_test)
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# F1-Score
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fscore = f1_score(y_test, y_pred)
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# Precision
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pres = precision_score(y_test, y_pred)
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# Recall
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rcall = recall_score(y_test, y_pred)
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# Accuracy
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accu = accuracy_score(y_test, y_pred)
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# ROC_AUC
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roc_auc = roc_auc_score(y_test, y_pred)
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pipelines.append(pipeline)
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scores_df = scores_df.append({
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'Model' : clf_name,
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'F1_Score' : fscore,
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'Precision' : pres,
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'Recall' : rcall,
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'Accuracy' : accu,
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'ROC_AUC' : roc_auc
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},
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ignore_index = True)
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return pipelines, scores_df
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modelPipeline(X_train, X_test, y_train, y_test) |