94 lines
3.7 KiB
Python
94 lines
3.7 KiB
Python
import pandas as pd
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import numpy as np
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import scipy as sp
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import time
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import sys
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import os
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import re
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import argparse
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from math import sqrt
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from scipy import stats
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import joblib
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# Alogorithm
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from xgboost.sklearn import XGBClassifier
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from sklearn import svm
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier
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from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.neural_network import MLPRegressor
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from sklearn.utils import all_estimators
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# Pre-processing
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from sklearn import preprocessing
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from sklearn.preprocessing import StandardScaler
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from sklearn.datasets import make_classification
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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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#def run_all_ML(input_pd, target_label, bts_input, bts_target, var_type):
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def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'):
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#y = input_pd[target_label]
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#X = input_pd.drop(target_label,axis=1)
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y = target_label
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X = input_pd
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# determine categorical and numerical features
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# Determine preprocessing steps ~ var_type
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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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result_pd = pd.DataFrame()
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for name, algorithm in all_estimators(type_filter="classifier"):
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try:
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estmator = algorithm()
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temp_pd = pd.DataFrame()
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temp_cm = pd.DataFrame()
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# orig
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pipe = Pipeline([
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("model" , algorithm())
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])
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# turn on and off preprocessing
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if preprocess == True:
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pipe = Pipeline([
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('prep' , col_transform),
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("model" , algorithm())
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])
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else:
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pipe = Pipeline([
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("model" , algorithm())
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])
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y_pred = cross_val_predict(pipe, X, y, cv = 10, n_jobs=10)
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_mcc = round(matthews_corrcoef(y_pred, y), 3)
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_bacc = round(balanced_accuracy_score(y_pred, y), 3)
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_f1 = round(f1_score(y_pred, y), 3)
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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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except Exception as e:
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print("Got an error while running {}".format(name))
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print(e)
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return(result_pd)
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