renamed function file to UQ_FS_fn.py and added new file to call this function
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2 changed files with 92 additions and 28 deletions
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@ -5,7 +5,7 @@ Created on Mon May 23 23:25:26 2022
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@author: tanu
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"""
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##################################
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#####################################
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def fsgs(input_df
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, target
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@ -13,7 +13,6 @@ def fsgs(input_df
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#, y_trueS = pd.Series()
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, estimator = LogisticRegression(**rs)
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, param_gridLd = {}
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#, pipelineO
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, cv_method = 10
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, var_type = ['numerical'
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, 'categorical'
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@ -25,34 +24,56 @@ def fsgs(input_df
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returns
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Dict containing results from FS and hyperparam tuning
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'''
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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 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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# 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 == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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# if var_type == 'mixed':
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# t = [('cat', OneHotEncoder(), categorical_ix)
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# , ('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'mixed':
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t = [('cat', OneHotEncoder(), categorical_ix)
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, ('num', MinMaxScaler(), numerical_ix)]
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# col_transform = ColumnTransformer(transformers = t
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# , remainder='passthrough')
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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###########################################################################
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#=================
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# Create var_type ~ column names
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# using one hot encoder with RFECV means the names internally are lost
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# Hence fit col_transformeer to my input_df and get all the column names
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# out and stored in a var to allow the 'selected features' to be subsetted
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# from the numpy boolean array
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#=================
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col_transform.fit(input_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(input_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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############################################################################
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# Create Pipeline object
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pipe = Pipeline([
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('pre', MinMaxScaler()),
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#('pre', col_transform),
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#('pre', MinMaxScaler()),
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('pre', col_transform),
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('fs', fs),
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#('clf', LogisticRegression(**rs))])
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('clf', estimator)])
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############################################################################
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# Define GridSearchCV
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gscv_fs = GridSearchCV(pipe
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, param_gridLd
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@ -65,7 +86,8 @@ def fsgs(input_df
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gscv_fs.fit(input_df, target)
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###############################################################
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###########################################################################
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# Get best param and scores out
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gscv_fs.best_params_
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gscv_fs.best_score_
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@ -91,16 +113,18 @@ def fsgs(input_df
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else:
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print('\nTraining score could not be internatlly verified. Please check training results dict')
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#-------------------------
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# Blind test: REAL check!
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#-------------------------
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#tp = gscv_fs.predict(X_bts)
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tp = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
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############
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#=================
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# info extraction
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############
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#=================
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# gives input vals??
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gscv_fs._check_n_features
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@ -118,20 +142,31 @@ def fsgs(input_df
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gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
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#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
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###############################################################################
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############################################################################
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#============
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# FS results
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#============
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# Now get the features out
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#--------------
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# All features
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#--------------
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all_features = gscv_fs.feature_names_in_
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n_all_features = gscv_fs.n_features_in_
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#all_features = gsfit.feature_names_in_
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sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
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#--------------
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# Selected features by the classifier
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# Important to have var_type_colnames here
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#----------------
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#sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()] 3 only for numerical df
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sel_features = var_type_colnames[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
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n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
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# get model name
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model_name = gscv_fs.best_estimator_.named_steps['clf']
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#--------------
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# Get model name
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#--------------
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model_name = gscv_fs.best_estimator_.named_steps['clf']
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b_model_params = gscv_fs.best_params_
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print('\n========================================'
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29
UQ_FS_fn_CALL.py
Normal file
29
UQ_FS_fn_CALL.py
Normal file
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@ -0,0 +1,29 @@
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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 May 24 08:11:05 2022
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@author: tanu
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"""
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import fsgs from
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fsgs(X,y,param_gridLd=param_grid_abc, blind_test_df = X_bts, estimator=AdaBoostClassifier(**rs), var_type = 'mixed')
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##############################################################################
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#========================================
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# Write final output file
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# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
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#========================================
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#output final dict as a json
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outFile = 'LR_FS.json'
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with open(outFile, 'w') as f:
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f.write(json.dumps(output_modelD,cls=NpEncoder))
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# read json
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file = 'LR_FS.json'
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with open(file, 'r') as f:
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data = json.load(f)
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##############################################################################
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