314 lines
10 KiB
Python
Executable file
314 lines
10 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
Created on Sat May 21 02:52:36 2022
|
|
|
|
@author: tanu
|
|
"""
|
|
#######################################################
|
|
# determine categorical and numerical features
|
|
numerical_ix = X.select_dtypes(include=['int64', 'float64']).columns
|
|
numerical_ix
|
|
categorical_ix = X.select_dtypes(include=['object', 'bool']).columns
|
|
categorical_ix
|
|
|
|
# Determine preprocessing steps ~ var_type
|
|
var_type = 'mixed'
|
|
|
|
if var_type == 'numerical':
|
|
t = [('num', MinMaxScaler(), numerical_ix)]
|
|
|
|
if var_type == 'categorical':
|
|
t = [('cat', OneHotEncoder(), categorical_ix)]
|
|
|
|
if var_type == 'mixed':
|
|
t = [('cat', OneHotEncoder(), categorical_ix)
|
|
, ('num', MinMaxScaler(), numerical_ix)]
|
|
|
|
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 = col_transform.fit_transform(X)
|
|
# (foo == test).all()
|
|
#-----------------------
|
|
|
|
col_transform.fit(X)
|
|
col_transform.get_feature_names_out()
|
|
|
|
var_type_colnames = col_transform.get_feature_names_out()
|
|
var_type_colnames = pd.Index(var_type_colnames)
|
|
|
|
if var_type == 'mixed':
|
|
print('\nVariable type is:', var_type
|
|
, '\nNo. of columns in input_df:', len(input_df.columns)
|
|
, '\nNo. of columns post one hot encoder:', len(var_type_colnames))
|
|
else:
|
|
print('\nNo. of columns in input_df:', len(input_df.columns))
|
|
|
|
|
|
# %% begin stupid
|
|
# stupid = OneHotEncoder()
|
|
# stupid.fit(X[categorical_ix])
|
|
# stupid_thing = stupid.get_feature_names()
|
|
# print(len(stupid_thing))
|
|
# horrid = (list(stupid_thing) + list(numerical_ix))
|
|
# print(horrid)
|
|
|
|
# print(len(horrid))
|
|
# asdfasdf = pd.Index(horrid)
|
|
|
|
# asdfasdf[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
|
|
|
# col_transform.get_param_names()['transformers']
|
|
|
|
# len(stupid.get_feature_names())
|
|
# len(numerical_ix)
|
|
|
|
# end stupid
|
|
#%%
|
|
pipe = Pipeline([
|
|
#('pre', MinMaxScaler())
|
|
('pre', col_transform)
|
|
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = 10, scoring = 'matthews_corrcoef'))
|
|
, ('clf', LogisticRegression(**rs))])
|
|
|
|
#########################################################
|
|
#cv = rskf_cv
|
|
cv = skf_cv
|
|
|
|
# LR: Feature Selelction + GridSearch CV + Pipeline
|
|
search_space = [
|
|
{ 'fs__estimator': [LogisticRegression(**rs)]
|
|
, 'fs__min_features_to_select': [1]
|
|
,'fs__cv': [skf_cv]
|
|
},
|
|
{
|
|
#'clf': [LogisticRegression()],
|
|
#'clf__C': np.logspace(0, 4, 10),
|
|
'clf__C': [1],
|
|
'clf__max_iter': [100],
|
|
'clf__penalty': ['l1', 'l2'],
|
|
'clf__solver': ['saga']
|
|
},
|
|
|
|
{
|
|
#'clf': [LogisticRegression()],
|
|
#'clf__C': np.logspace(0, 4, 10),
|
|
'clf__C': [2, 2.5],
|
|
'clf__max_iter': [100],
|
|
'clf__penalty': ['l1', 'l2'],
|
|
'clf__solver': ['saga']
|
|
},
|
|
|
|
#{'clf': [RandomForestclf(n_estimators=100)],
|
|
# 'clf__max_depth': [5, 10, None]},
|
|
#{'clf': [KNeighborsclf()],
|
|
# 'clf__n_neighbors': [3, 7, 11],
|
|
# 'clf__weights': ['uniform', 'distance']
|
|
#}
|
|
]
|
|
|
|
gscv_fs = GridSearchCV(pipe
|
|
, search_space
|
|
, cv = cv
|
|
, scoring = mcc_score_fn
|
|
, refit = 'mcc'
|
|
, verbose = 1
|
|
, return_train_score = True
|
|
, **njobs)
|
|
gscv_fs.fit(X, y)
|
|
#Fitting 10 folds for each of 8 candidates, totalling 80 fits
|
|
# QUESTION: HOW??
|
|
gscv_fs.best_params_
|
|
gscv_fs.best_score_
|
|
|
|
# Training best score corresponds to the max of the mean_test<score>
|
|
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
|
print('\nTraining best score (MCC):', train_bscore)
|
|
gscv_fs.cv_results_['mean_test_mcc']
|
|
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
|
round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)
|
|
|
|
check_train_score = [round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
|
, round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)]
|
|
|
|
check_train_score = np.nanmax(check_train_score)
|
|
|
|
# Training results
|
|
gscv_tr_resD = gscv_fs.cv_results_
|
|
mod_refit_param = gscv_fs.refit
|
|
|
|
# sanity check
|
|
if train_bscore == check_train_score:
|
|
print('\nVerified training score (MCC):', train_bscore )
|
|
else:
|
|
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
|
|
|
# Blind test: REAL check!
|
|
tp = gscv_fs.predict(X_bts)
|
|
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
|
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
|
|
|
############
|
|
# info extraction
|
|
############
|
|
# gives input vals??
|
|
gscv_fs._check_n_features
|
|
|
|
# gives gscv params used
|
|
gscv_fs._get_param_names()
|
|
|
|
# gives ??
|
|
gscv_fs.best_estimator_
|
|
gscv_fs.best_params_ # gives best estimator params as a dict
|
|
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
|
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
|
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
|
|
|
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
|
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
|
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
|
|
|
###############################################################################
|
|
#============
|
|
# FS results
|
|
#============
|
|
# Now get the features out
|
|
|
|
all_features = gscv_fs.feature_names_in_
|
|
n_all_features = gscv_fs.n_features_in_
|
|
#all_features = gsfit.feature_names_in_
|
|
|
|
#sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
|
#n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
|
|
|
#---------------<<<< HERE
|
|
#if var_type == 'mixed'
|
|
sel_features = var_type_colnames[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
|
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
|
#---------------<<<< HERE
|
|
|
|
# get model name
|
|
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
|
b_model_params = gscv_fs.best_params_
|
|
|
|
print('\n========================================'
|
|
, '\nRunning model:'
|
|
, '\nModel name:', model_name
|
|
, '\n==============================================='
|
|
, '\nRunning feature selection with RFECV for model'
|
|
, '\nTotal no. of features in model:', len(all_features)
|
|
, '\nThese are:\n', all_features, '\n\n'
|
|
, '\nNo of features for best model: ', n_sf
|
|
, '\nThese are:', sel_features, '\n\n'
|
|
, '\nBest Model hyperparams:', b_model_params
|
|
)
|
|
|
|
###############################################################################
|
|
############################## OUTPUT #########################################
|
|
###############################################################################
|
|
#=========================
|
|
# Blind test: BTS results
|
|
#=========================
|
|
# Build the final results with all scores for a feature selected model
|
|
bts_predict = gscv_fs.predict(X_bts)
|
|
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
|
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
|
bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
|
|
|
|
# Diff b/w train and bts test scores
|
|
train_test_diff = round(train_bscore - bts_mcc_score,2)
|
|
print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
|
|
|
|
# create a dict with all scores
|
|
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
|
#'bts_mcc':None
|
|
'bts_fscore':None
|
|
, 'bts_precision':None
|
|
, 'bts_recall':None
|
|
, 'bts_accuracy':None
|
|
, 'bts_roc_auc':None
|
|
, 'bts_jaccard':None}
|
|
|
|
|
|
lr_btsD
|
|
#lr_btsD['bts_mcc'] = bts_mcc_score
|
|
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
|
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
|
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
|
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
|
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
|
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
|
lr_btsD
|
|
|
|
#===========================
|
|
# Add FS related model info
|
|
#===========================
|
|
model_namef = str(model_name)
|
|
# FIXME: doesn't tell you which it has chosen
|
|
fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
|
|
all_featuresL = list(all_features)
|
|
fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
|
|
fs_res_array_rankf = list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)
|
|
sel_featuresf = list(sel_features)
|
|
n_sf = int(n_sf)
|
|
|
|
output_modelD = {'model_name': model_namef
|
|
, 'model_refit_param': mod_refit_param
|
|
, 'Best_model_params': b_model_params
|
|
, 'n_all_features': n_all_features
|
|
, 'fs_method': fs_methodf
|
|
, 'fs_res_array': fs_res_arrayf
|
|
, 'fs_res_array_rank': fs_res_array_rankf
|
|
, 'all_feature_names': all_featuresL
|
|
, 'n_sel_features': n_sf
|
|
, 'sel_features_names': sel_featuresf}
|
|
output_modelD
|
|
|
|
#========================================
|
|
# Update output_modelD with bts_results
|
|
#========================================
|
|
output_modelD.update(lr_btsD)
|
|
output_modelD
|
|
|
|
output_modelD['train_score (MCC)'] = train_bscore
|
|
output_modelD['bts_mcc'] = bts_mcc_score
|
|
output_modelD['train_bts_diff'] = round(train_test_diff,2)
|
|
output_modelD
|
|
|
|
class NpEncoder(json.JSONEncoder):
|
|
def default(self, obj):
|
|
if isinstance(obj, np.integer):
|
|
return int(obj)
|
|
if isinstance(obj, np.floating):
|
|
return float(obj)
|
|
if isinstance(obj, np.ndarray):
|
|
return obj.tolist()
|
|
return super(NpEncoder, self).default(obj)
|
|
|
|
json.dumps(output_modelD, cls=NpEncoder)
|
|
|
|
#========================================
|
|
# Write final output file
|
|
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
|
#========================================
|
|
#output final dict as a json
|
|
outFile = 'LR_FS.json'
|
|
with open(outFile, 'w') as f:
|
|
f.write(json.dumps(output_modelD,cls=NpEncoder))
|
|
|
|
# read json
|
|
file = 'LR_FS.json'
|
|
with open(file, 'r') as f:
|
|
data = json.load(f)
|
|
##############################################################################
|
|
|