svm.py

########################################################################################################################################
# Credits
########################################################################################################################################

# Developed by José Teófilo Moreira Filho, Ph.D.
# teofarma1@gmail.com
# http://lattes.cnpq.br/3464351249761623
# https://www.researchgate.net/profile/Jose-Teofilo-Filho
# https://scholar.google.com/citations?user=0I1GiOsAAAAJ&hl=pt-BR
# https://orcid.org/0000-0002-0777-280X

########################################################################################################################################
# Importing packages
########################################################################################################################################

from st_aggrid import AgGrid
import streamlit as st

import base64
import functools
from io import BytesIO
import os
import warnings
warnings.filterwarnings(action='ignore')

from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))

from rdkit import Chem, DataStructs

import numpy as np
from numpy import sqrt
from numpy import argmax

import pandas as pd
from pandas import DataFrame

import matplotlib.pyplot as plt
#import seaborn as sns

import os

from sklearn.svm import SVC
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn import metrics
from sklearn.metrics import accuracy_score, cohen_kappa_score, matthews_corrcoef, roc_curve, roc_auc_score, make_scorer
from sklearn.metrics import balanced_accuracy_score, recall_score, confusion_matrix
import pickle
from sklearn.calibration import calibration_curve

from imblearn.metrics import geometric_mean_score

import multiprocessing
import utils
from skopt import BayesSearchCV

import plotly.graph_objects as go

def app(df,s_state):
    ########################################################################################################################################
    # Functions
    ########################################################################################################################################
    cc = utils.Custom_Components()
    
    def getNeighborsDitance(trainingSet, testInstance, k):
        neighbors_k=metrics.pairwise.pairwise_distances(trainingSet, Y=testInstance, metric='dice', n_jobs=1)
        neighbors_k.sort(0)
        similarity= 1-neighbors_k
        return similarity[k-1,:]

    #5-fold-cross-val
    def cros_val(x,y,classifier):
        probs_classes = []
        y_test_all = []
        AD_fold =[]
        distance_train_set =[]
        distance_test_set = []
        y_pred_ad=[]
        y_exp_ad =[]
        for train_index, test_index in cv.split(x, y):
            clf = classifier # model with best parameters
            X_train_folds = x[train_index] # descritors train split
            y_train_folds = np.array(y)[train_index.astype(int)] # label train split
            X_test_fold = x[test_index] # descritors test split
            y_test_fold = np.array(y)[test_index.astype(int)] # label test split
            clf.fit(X_train_folds, y_train_folds) # train fold
            y_pred = clf.predict_proba(X_test_fold) # test fold
            probs_classes.append(y_pred) # all predictions for test folds
            y_test_all.append(y_test_fold) # all folds' labels
            k= int(round(pow((len(y)) ,1.0/3), 0))
            distance_train = getNeighborsDitance(X_train_folds, X_train_folds, k)
            distance_train_set.append(distance_train)
            distance_test = getNeighborsDitance(X_train_folds, X_test_fold, k)
            distance_test_set.append(distance_test)
            Dc = np.average(distance_train)-(0.5*np.std(distance_train))
            for i in range(len(X_test_fold)):
                ad=0
                if distance_test_set[0][i] >= Dc:
                    ad = 1
                AD_fold.append(ad)
        probs_classes = np.concatenate(probs_classes)
        y_experimental = np.concatenate(y_test_all)
        # Uncalibrated model predictions
        pred = (probs_classes[:, 1] > 0.5).astype(int)
        for i in range(len(AD_fold)):
            if AD_fold[i] == 1:
                y_pred_ad.append(pred[i])
                y_exp_ad.append(y_experimental[i])

        return(pred, y_experimental, probs_classes, AD_fold, y_pred_ad, y_exp_ad)

    #STATISTICS
    def calc_statistics(y,pred):
        # save confusion matrix and slice into four pieces
        confusion = confusion_matrix(y, pred)
        #[row, column]
        TP = confusion[1, 1]
        TN = confusion[0, 0]
        FP = confusion[0, 1]
        FN = confusion[1, 0]

        # calc statistics
        accuracy = round(accuracy_score(y, pred),2)#accuracy
        mcc = round(matthews_corrcoef(y, pred),2) #mcc
        kappa = round(cohen_kappa_score(y, pred),2) #kappa
        sensitivity = round(recall_score(y, pred),2) #Sensitivity
        specificity = round(TN / (TN + FP),2) #Specificity
        positive_pred_value = round(TP / float(TP + FP),2) #PPV
        negative_pred_value = round(TN / float(TN + FN),2) #NPV
        auc = round(roc_auc_score(y, pred),2) #AUC
        bacc = round(balanced_accuracy_score(y, pred),2) # balanced accuracy

        #converting calculated metrics into a pandas dataframe to compare all models at the final
        statistics = pd.DataFrame({'Bal-acc': bacc, "Sensitivity": sensitivity, "Specificity": specificity,"PPV": positive_pred_value,
               "NPV": negative_pred_value, 'Kappa': kappa, 'AUC': auc, 'MCC': mcc, 'Accuracy': accuracy,}, index=[0])
        return(statistics)


    ########################################################################################################################################
    # Seed
    ########################################################################################################################################

    # Choose the general hyperparameters interval to be tested

    if df is not None:
        with st.sidebar.header('1. Set seed for reprodutivity'):
            parameter_random_state = st.sidebar.number_input('Seed number (random_state)', min_value=None, max_value=None, value=int(42))


        st.sidebar.write('---')

    # Select columns
        with st.sidebar.header('3. Enter column name in modeling set'):
            name_activity = st.sidebar.selectbox('Enter column with activity (e.g., Active and Inactive that should be 1 and 0, respectively)', options=df.columns)

        st.sidebar.write('---')

        ########################################################################################################################################
        # Data splitting
        ########################################################################################################################################
        with st.sidebar.header('4. Select data splitting'):
            # Select fingerprint
            splitting_dict = {'Only k-fold':'kfold',
                    'k-fold and external set':'split_original',
                    'Input your own external set':'input_own',}
            user_splitting  = st.sidebar.selectbox('Choose an splitting', list(splitting_dict.keys()))
            selected_splitting = splitting_dict[user_splitting]
        
        with st.sidebar.subheader('4.1 Number of folds'):
            n_plits = st.sidebar.number_input('Enter the number of folds', min_value=None, max_value=None, value=int(5))

        # Selecting x and y from input file
        if df is not None:

            if selected_splitting == 'kfold':
                x = df.iloc[:, df.columns != name_activity].values  # Using all column except for the last column as X
                y = df[name_activity].values  # Selecting the last column as Y

            if selected_splitting == 'split_original':
                x = df.iloc[:, df.columns != name_activity].values  # Using all column except for the last column as X
                y = df[name_activity].values  # Selecting the last column as Y

                with st.sidebar.header('Test size (%)'):
                    input_test_size = st.sidebar.number_input('Enter the test size (%)', min_value=None, max_value=None, value=(20))
                    test_size = input_test_size/100
                    x, x_ext, y, y_ext = train_test_split(x, y, test_size=test_size, random_state=0, stratify=y)

            if selected_splitting == 'input_own':

                # Upload File
                with st.sidebar.header('4.2 Upload your CSV of external set (calculated descriptors)'):
                    own_external = cc.upload_file(custom_title="Upload your external CSV file",context=st.sidebar,key="own_external", file_type=["csv"])
                    
                # if own_external is None:
                #     st.info('Awaiting for external set be uploaded.')

                    # Read Uploaded file and convert to pandas
                if own_external is not None:
                
                    with st.sidebar.header('4.3 Enter column name'):
                        name_activity_ext = st.sidebar.selectbox('Enter column with activity in externl set (e.g., Active and Inactive that should be 1 and 0, respectively)', options=df.columns)

                    st.sidebar.write('---')

                    # Read CSV data
                    df_own = own_external
                    #st.header('**Molecular descriptors of external set**')
                    #st.write(df_own)

                    x = df.iloc[:, df.columns != name_activity].values  # Using all column except for the last column as X
                    y = df[name_activity].values  # Selecting the last column as Y

                    x_ext = df_own.iloc[:, df_own.columns != name_activity_ext].values  # Using all column except for the last column as X
                    y_ext = df_own[name_activity_ext].values  # Selecting the last column as Y


        ########################################################################################################################################
        # Sidebar - Specify parameter settings
        ########################################################################################################################################

        st.sidebar.header('5. Set Parameters - Bayesian hyperparameter search')

        # Choose the general hyperparameters
        st.sidebar.subheader('General Parameters')

        parameter_n_iter = st.sidebar.slider('Number of iterations (n_iter)', 1, 1000, 3, 1)
        st.sidebar.write('---')
        parameter_n_jobs = st.sidebar.select_slider('Number of jobs to run in parallel (n_jobs)', options=[-1, 1])

        # Select the hyperparameters to be optimized
        st.sidebar.subheader('Select the hyperparameters to be optimized')

        container = st.sidebar.container()
        slc_all = st.sidebar.checkbox("Select all")
        #cleaning some code
        svc_hyperparams=['C','gamma', 'Kernel']

        if slc_all:
            selected_options = container.multiselect("Select one or more options:", svc_hyperparams, svc_hyperparams)
        else:
            selected_options = container.multiselect("Select one or more options:", svc_hyperparams)

        #st.write(selected_options)

        st.sidebar.write('---')

        # Choose the hyperparameters intervals to be tested
        st.sidebar.subheader('Learning Hyperparameters')

        if selected_options is None:
            st.sidebar.write('Select hyperparameters')

        selected_hyperparameters = {}

        if svc_hyperparams[0] in selected_options:
            c_container=st.sidebar.container()
            try:
                min_parameter_c = c_container.number_input('Minimal value of C', 0.000001, 99.99,)
                max_parameter_c = c_container.number_input('Maximum value of C', 0.000002, 100,)
            except:
                c_container.write("First value (minimum) must be smaller than second(maximum) value")
            c = {'C': (min_parameter_c, max_parameter_c)}
            selected_hyperparameters.update(c)
            #st.write(selected_hyperparameters)
            st.sidebar.write('---')

        if svc_hyperparams[1] in selected_options:
            min_parameter_gamma = st.sidebar.number_input('Minimum value of gamma', 0.000001, 99.99)
            max_parameter_gamma = st.sidebar.number_input('Maximum value of gamma', 0.000002, 100)
            gamma = {"gamma": (min_parameter_gamma, max_parameter_gamma)}
            selected_hyperparameters.update(gamma)
            #st.write(selected_hyperparameters)
            st.sidebar.write('---')

        if svc_hyperparams[2] in selected_options:
            kernels_dict = {'Radial basis function':'rbf',
                    'Linear':'linear',
                    'Polinomial':'poly',
                    'Sigmoid': 'sigmoid'}
            user_kernel  = st.sidebar.selectbox('Choose an Kernel', list(kernels_dict.keys()))

            selected_kernel = kernels_dict[user_kernel]
            kernel_svc = {"kernel": [selected_kernel]}
            selected_hyperparameters.update(kernel_svc)
            #st.write(selected_hyperparameters)
            st.sidebar.write('---')        

        else:
            st.sidebar.write('Please, select the hyperparameters to be optimized!')

        ########################################################################################################################################
        # Modeling
        ########################################################################################################################################

        if st.sidebar.button('Run Modeling'):

        #---------------------------------#
        #Create folds for cross-validation
            cv = StratifiedKFold(n_splits = n_plits, shuffle=False,)

        #---------------------------------#
        # Run SVC Model building - Bayesian hyperparameter search
            scorer = make_scorer(geometric_mean_score)

            # log-uniform: understand as search over p = exp(x) by varying x
            opt_svc = BayesSearchCV(
                SVC(probability=True),
                selected_hyperparameters,
                n_iter=parameter_n_iter, # Number of parameter settings that are sampled
                cv=cv,
                scoring = scorer,
                verbose=0,
                refit= True, # Refit the best estimator with the entire dataset.
                random_state=parameter_random_state,
                n_jobs = parameter_n_jobs
            )

            opt_svc.fit(x, y)

            st.write("Best parameters: %s" % opt_svc.best_params_)

        #---------------------------------#
        # k-fold cross-validation
            pred_svc, y_experimental, probs_classes, AD_fold, y_pred_ad, y_exp_ad = cros_val(x,y, SVC(**opt_svc.best_params_, probability=True))
        #---------------------------------#
        # Statistics k-fold cross-validation
            statistics = calc_statistics(y_experimental, pred_svc)
        #---------------------------------#
        # coverage
            coverage = round((len(y_exp_ad)/len(y_experimental)),2)

        #---------------------------------#
        #converting calculated metrics into a pandas dataframe to save a xls
            model_type = "SVC"

            result_type = "uncalibrated"

            metrics_svc_uncalibrated = statistics
            metrics_svc_uncalibrated['model'] = model_type
            metrics_svc_uncalibrated['result_type'] = result_type
            metrics_svc_uncalibrated['coverage'] = coverage

            st.header('**Metrics of uncalibrated model on the K-fold cross validation**')
            
            #---------------------------------#
            # Bar chart Statistics k-fold cross-validation

            metrics_svc_uncalibrated_graph = metrics_svc_uncalibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC", "coverage"])
            
            x = metrics_svc_uncalibrated_graph.columns
            y = metrics_svc_uncalibrated_graph.loc[0].values
            
            colors = ["red", "orange", "green", 'yellow', "pink", 'blue', "purple", "cyan", "teal"]

            fig = go.Figure(data=[go.Bar(
                x=x, y=y,
                text=y,
                textposition='auto',
                marker_color = colors
            )])

            st.plotly_chart(fig)   
            
            ########################################################################################################################################
            # External set uncalibrated
            ########################################################################################################################################
            if selected_splitting == 'split_original' or selected_splitting == 'input_own':
                            
                # Predict probabilities for the external set
                probs_external = opt_svc.predict_proba(x_ext)
                # Making classes
                pred_svc = (probs_external[:, 1] > 0.5).astype(int)
                # Statistics external set uncalibrated
                statistics = calc_statistics(y_ext, pred_svc)
                
                #---------------------------------#
                #converting calculated metrics into a pandas dataframe to save a xls
                model_type = "SVC"
                
                result_type = "uncalibrated_external_set"

                metrics_svc_external_set_uncalibrated = statistics
                metrics_svc_external_set_uncalibrated['model'] = model_type
                metrics_svc_external_set_uncalibrated['result_type'] = result_type
                

                st.header('**Metrics of uncalibrated model on the external set**') 
                #---------------------------------#
                # Bar chart Statistics k-fold cross-validation

                metrics_svc_external_set_uncalibrated_graph = metrics_svc_external_set_uncalibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC", "coverage"])
                
                x = metrics_svc_external_set_uncalibrated_graph.columns
                y = metrics_svc_external_set_uncalibrated_graph.loc[0].values
                
                colors = ["red", "orange", "green", 'yellow', "pink", 'blue', "purple", "cyan", "teal"]

                fig = go.Figure(data=[go.Bar(
                    x=x, y=y,
                    text=y,
                    textposition='auto',
                    marker_color = colors
                )])

                st.plotly_chart(fig)                          


            ########################################################################################################################################
            # Model Calibration
            ########################################################################################################################################        
            #---------------------------------#
            # Check model calibatrion
            # keep probabilities for the positive outcome only
            probs = probs_classes[:, 1]
            # reliability diagram
            fop, mpv = calibration_curve(y_experimental, probs, n_bins=10)
            # plot perfectly calibrated
            fig = plt.figure()
            plt.plot([0, 1], [0, 1], linestyle='--')
            # plot model reliability
            plt.plot(mpv, fop, marker='.')
            
            st.header('**Check model calibatrion**')
            st.pyplot(fig)
            
            #---------------------------------#
            # Use ROC-Curve and Gmean to select a threshold for calibration
            # keep probabilities for the positive outcome only
            yhat = probs_classes[:, 1]
            # calculate roc curves
            fpr, tpr, thresholds = roc_curve(y_experimental, yhat)
            # calculate the g-mean for each threshold
            gmeans = sqrt(tpr * (1-fpr))
            # locate the index of the largest g-mean
            ix = argmax(gmeans)
            # plot the roc curve for the model
            fig = plt.figure()
            plt.plot([0,1], [0,1], linestyle='--', label='No Skill')
            plt.plot(fpr, tpr, marker='.', label='RF')
            plt.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
            # axis labels
            plt.xlabel('False Positive Rate')
            plt.ylabel('True Positive Rate')
            plt.legend()

            st.header('**Use ROC-Curve and Gmean to select a threshold for calibration**')

            st.pyplot(fig)
        
            st.write('Best Threshold= %.2f, G-Mean= %.2f' % (round(thresholds[ix], 2), round(gmeans[ix], 2)))
            
            #---------------------------------#
            # Record the threshold in a variable
            threshold_roc = round(thresholds[ix], 2)

            #---------------------------------#
            # Select the best threshold to distinguishthe classes
            pred_svc = (probs_classes[:, 1] > threshold_roc).astype(int)
            
            #---------------------------------#
            # Statistics Statistics k-fold cross-validation calibrated
            statistics = calc_statistics(y_experimental, pred_svc)
            
            #---------------------------------#
            # Coverage
            coverage = round((len(y_exp_ad)/len(y_experimental)),2)

            #---------------------------------#
            #converting calculated metrics into a pandas dataframe to save a xls
            model_type = "SVC"

            result_type = "calibrated"

            metrics_svc_calibrated = statistics
            metrics_svc_calibrated['model'] = model_type
            metrics_svc_calibrated['result_type'] = result_type
            metrics_svc_calibrated['calibration_threshold'] = threshold_roc
            metrics_svc_calibrated['coverage'] = coverage

            st.header('**Metrics of calibrated model on the K-fold cross validation**')

            #---------------------------------#
            # Bar chart Statistics k-fold cross-validation calibrated
        
            metrics_svc_calibrated_graph = metrics_svc_calibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC", "coverage"])
            x = metrics_svc_calibrated_graph.columns
            y = metrics_svc_calibrated_graph.loc[0].values
        
            colors = ["red", "orange", "green", 'yellow', "pink", 'blue', "purple", "cyan", "teal"]

            fig = go.Figure(data=[go.Bar(
                x=x, y=y,
                text=y,
                textposition='auto',
                marker_color = colors
            )])

            st.plotly_chart(fig)

            ########################################################################################################################################
            # External set calibrated
            ########################################################################################################################################
            if selected_splitting == 'split_original' or selected_splitting == 'input_own':
                            
                # Predict probabilities for the external set
                probs_external = opt_svc.predict_proba(x_ext)
                # Making classes
                pred_svc = (probs_external[:, 1] > threshold_roc).astype(int)
                # Statistics external set uncalibrated
                statistics = calc_statistics(y_ext, pred_svc)
                
                #---------------------------------#
                #converting calculated metrics into a pandas dataframe to save a xls
                model_type = "SVC"
                
                result_type = "calibrated_external_set"

                metrics_svc_external_set_calibrated = statistics
                metrics_svc_external_set_calibrated['model'] = model_type
                metrics_svc_external_set_calibrated['result_type'] = result_type
                

                st.header('**Metrics of calibrated model on the external set**') 
                #---------------------------------#
                # Bar chart Statistics k-fold cross-validation

                metrics_svc_external_set_calibrated_graph = metrics_svc_external_set_calibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC", "coverage"])
                
                x = metrics_svc_external_set_calibrated_graph.columns
                y = metrics_svc_external_set_calibrated_graph.loc[0].values
                
                colors = ["red", "orange", "green", 'yellow', "pink", 'blue', "purple", "cyan", "teal"]

                fig = go.Figure(data=[go.Bar(
                    x=x, y=y,
                    text=y,
                    textposition='auto',
                    marker_color = colors
                )])

                st.plotly_chart(fig)                          

        ########################################################################################################################################
        # Compare models
        ########################################################################################################################################

            # Only K-fold
            st.header('**Compare metrics of calibrated and uncalibrated models on the k-fold cross validation**')

            metrics_svc_uncalibrated_graph = metrics_svc_uncalibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC"])
            metrics_svc_calibrated_graph = metrics_svc_calibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC"])

            fig = go.Figure()

            fig.add_trace(go.Scatterpolar(
                r=metrics_svc_uncalibrated_graph.loc[0].values,
                theta=metrics_svc_uncalibrated_graph.columns,
                fill='toself',
                name='Uncalibrated'
            ))
            fig.add_trace(go.Scatterpolar(
                r=metrics_svc_calibrated_graph.loc[0].values,
                theta=metrics_svc_uncalibrated_graph.columns,
                fill='toself',
                name='Calibrated'
            ))

            fig.update_layout(
            polar=dict(
                radialaxis=dict(
                visible=True,
                range=[0, 1]
                )),
            showlegend=True
            )

            st.plotly_chart(fig)

            #---------------------------------#
            # External set

            if selected_splitting == 'split_original' or selected_splitting == 'input_own':
                
                st.header('**Compare metrics of calibrated and uncalibrated models on the external set**')

                metrics_svc_external_set_uncalibrated_graph = metrics_svc_external_set_uncalibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC"])
                metrics_svc_external_set_calibrated_graph = metrics_svc_external_set_calibrated.filter(items=['Bal-acc', "Sensitivity", "Specificity", "PPV", "NPV", "Kappa", "MCC", "AUC"])

                fig = go.Figure()

                fig.add_trace(go.Scatterpolar(
                    r=metrics_svc_external_set_uncalibrated_graph.loc[0].values,
                    theta=metrics_svc_external_set_uncalibrated_graph.columns,
                    fill='toself',
                    name='Uncalibrated'
                ))
                fig.add_trace(go.Scatterpolar(
                    r=metrics_svc_external_set_calibrated_graph.loc[0].values,
                    theta=metrics_svc_external_set_calibrated_graph.columns,
                    fill='toself',
                    name='Calibrated'
                ))

                fig.update_layout(
                polar=dict(
                    radialaxis=dict(
                    visible=True,
                    range=[0, 1]
                    )),
                showlegend=True
                )

                st.plotly_chart(fig)

        ########################################################################################################################################
        # Download files
        ########################################################################################################################################

            st.header('**Download files**')
                    
            if selected_splitting == 'split_original' or selected_splitting == 'input_own':
                frames = [metrics_svc_uncalibrated, metrics_svc_calibrated, 
                metrics_svc_external_set_uncalibrated, metrics_svc_external_set_calibrated]

            else:
                frames = [metrics_svc_uncalibrated, metrics_svc_calibrated,]

        
            result = pd.concat(frames)

            result = result.round(2)

            # File download
            def filedownload(df):
                csv = df.to_csv(index=False)
                b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
                href = f'<a href="data:file/csv;base64,{b64}" download="metrics_svc.csv">Download CSV File - metrics</a>'
                st.markdown(href, unsafe_allow_html=True)

            filedownload(result)

            def download_model(model):
                output_model = pickle.dumps(model)
                b64 = base64.b64encode(output_model).decode()
                href = f'<a href="data:file/output_model;base64,{b64}" download= model_svc.pkl >Download generated model (PKL File)</a>'
                st.markdown(href, unsafe_allow_html=True)

            download_model(opt_svc)