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Imagene_v3_3v1.py
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Imagene_v3_3v1.py
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##Imagene.py
##Note: This script is only operable when within the docker container package. Check github for more info on running the package.
##Version 1.02: ##Added if condition to check if the low ratio RMSE plot file exists before writing it to HTML file
##Version 1.03.1: ## a) Changed the conditions for binarization of feature columns in Y_test and Y_pred for AUC calculations. Now, if value<threshold then it gets binarized to 0 else to 1.
## ## b) Changed decision threshold list to [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0].
## ## c) Edited mp.xlim and ylim parameters for AUC v/s decision_threshold plot to include left=0.0 and right=1.0 values (for xlim).
##Version 1.04: ## Changed the decision threshold list to [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9].
## ## Changed the if condition in 1038 to match length of the AUC_value_list and decision_threshold_list to >=8. Made the correspoding change in print statement in line 1046 accordingly.
## ## Changed xlim: now left=0.1 and right=0.9
## ## Reverted the condition for binarization of feature columns in Y_test and Y_pred for AUC calculations. Now, if value<=threshold then it gets binarized to 0 else to 1.
##Version 1.05: ##Using pickle module instead of joblib module to load model.pkl
##Version 1.1: ##Adding multiTask lasso and multitask elastic net models
##Version 2.0: Added Feature importances and permutation tests for R-square and AUC. R-square gets calculated using sklearn.metrics.r2_score method.
##Version 2.2: if pVal_adj_method="none" (i.e. p_adjust=p_value) or corr_threshold (aka correlation coefficient threshold) < 0.0 (for ex: -1.0), then no <0.05 filtering on p_adjust value.
##Note: Please monitor github commits for version updates after Version: 2.2.
##Version 2.3: ##ratio=rmse when stdev=0, changed print statement accordingly.
## ##Introduced RandomForest for binary label classification in "BuildModel", and their AUC score calculation (introduced if condition for the same in "evaluate")
##Version 2.4: ##Added DecisionTreeClassifier
##Version 2.5: Add visualize tree for DecisionTreeClassifier.
##Version 2.6: Moved print tree outside evaluate inside buildModel function.
## Fixed couple print statements to prepend the output file paths with str(save_dir) and tagDir, mainly the AUC files from RandomForest and DecisionTreeClassifier operations.
##Version 2.7: Adding supervised neural networks (MultiLayerPerceptron) Model, silenting feature importance and featureSelection based training specifically for that model_type.
##Version 2.8: Adding LogisticRegression Model
##Version 2.9: For grid_search='True', No feature importances or feature selection executed.
##Version 3.0: Setting min AUC=0.75 for permutation calculation (p-value estimation).
##Version 3.1: Setting min AUC=0.75 for permutation calculation (p-value estimation) for classifier models as well.
##Version 3.2: Introducing SVM.
##Version 3.2v: Added 'grid_search' parameter in evaluate call for mode='validate' in line 1229.
##Version 3.2v2: Line 999, changed and to or condition to allow for AUC>0.75 or r-square>0.25
##Version 3.2v3: Line 994, changed and to or condition to allow for AUC>0.70 or r-square>0.25
##Version 3.2v4: Line 934, changed and to or condition to allow for AUC>0.70 or r-square>0.25
##Version 3.2v5: Silenting Line 918 to 921 to temporarily not do feature importances for model trained using GridSearchCV
##Version 3.2v6: In line 932 added model type =SVC for if condition. In line 522, corrected text to SVC.
##Version 3.3v1: Introducing RobustScaler as a normalization technique. Permutation tests now occur only for labels that satisfy AUC>0.7 and R-square>0.25, it executed previously for either condition - line 1002.
##Author: Shrey Sukhadia
#!/usr/bin/python
import base64
from cProfile import Profile
from graphviz import Source
import matplotlib as mpl
mpl.use('Agg')
import os, re, sys, math
import argparse
import numpy as np
import pandas as pd
import joblib
import pickle
import configparser
import ast
import argparse
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.feature_selection import SelectFromModel
from sklearn import preprocessing
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import binarize
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
from sklearn.metrics import auc
from scipy import stats
from scipy.interpolate import UnivariateSpline
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn import linear_model
from sklearn import tree
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import MultiTaskElasticNet
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
from sklearn.metrics import make_scorer
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
import seaborn as sb
import matplotlib.pyplot as mp
import scikitplot as skplt
from math import sqrt
import base64
from datetime import datetime
from rpy2.robjects.packages import importr
from rpy2.robjects.vectors import FloatVector
##Correlation function establishes correlations between gene and imaging features and calculates FDR adjusted pvalues further to establish statistical significance of those correlations.
def correlation(d_1,d_2,d_1_header,d_2_header, model_type, corr_method, corr_threshold,pVal_adjust_method, tagDir):
pval=dict()
pcorr=dict()
Var1_list=[]; Var2_list=[]; spCorr_list=[]; pvalue_list=[]
for i in d_1_header:
for j in d_2_header:
if(corr_method=="spearman"):
pCorr,pv=stats.spearmanr(d_1[i], d_2[j])
elif(corr_method=="pearson"):
pCorr,pv=stats.pearsonr(d_1[i], d_2[j])
else:
pCorr,pv=stats.spearmanr(d_1[i], d_2[j])
Var1_list.append(i); Var2_list.append(j); spCorr_list.append(pCorr); pvalue_list.append(pv)
rstats = importr('stats')
pAJ_list = rstats.p_adjust(FloatVector(pvalue_list), method = pVal_adjust_method)
Master_df = pd.DataFrame({'Var1': Var1_list, 'Var2': Var2_list, 'correlation': spCorr_list, 'p_value': pvalue_list, 'p_adjust': pAJ_list })
Master_df_sorted=Master_df.sort_values(by=['p_adjust'])
#print(pcorr_PAJ_df_sorted.head(30))
Master_df_sorted.to_csv(str(save_dir)+"All_correlations.csv")
if(pVal_adjust_method=="none" or corr_threshold < 0.0):
Master_df_sorted_sgn=Master_df_sorted
else:
Master_df_sorted_sgn=Master_df_sorted[Master_df_sorted['p_adjust']<0.05]
Master_df_sorted_sgn_filtered=Master_df_sorted_sgn[abs(Master_df_sorted_sgn['correlation'])>corr_threshold]
Master_df_sorted_sgn.to_csv(str(save_dir)+"Significant_correlations.csv")
Master_df_sorted_sgn_filtered.to_csv(str(save_dir)+"Significant_correlations_gt_corr_threshold.csv")
#print(Master_df_sorted_sgn)
List_of_Var1=Master_df_sorted_sgn_filtered["Var1"].tolist()
List_of_Var2=Master_df_sorted_sgn_filtered["Var2"].tolist()
fC_List_Var1=[]
for i in List_of_Var1:
fC=i.split("_")[0]
fC_List_Var1.append(fC)
fC_uniq = sorted(set(fC_List_Var1))
#outfileHTML=open(model_type+".output.html",'w')
image_tag_list=[]
#image_count=0
for i in fC_uniq:
print(i)
#fC_df=Master_df_sorted_sgn[Master_df_sorted_sgn['Var1'].str.match(i, case=True, flags=0)].head(50)
#fC_df=Master_df_sorted_sgn[Master_df_sorted_sgn['Var1'].str.contains(i, regex=False, na=False)].head(50)
fC_df=Master_df_sorted_sgn_filtered[Master_df_sorted_sgn_filtered['Var1'].str.contains(i, regex=False, na=False)]
fC_df_pivot=fC_df.pivot_table(index="Var1",columns="Var2",values="correlation",fill_value=0)
#fC_df_pivot = pd.pivot_table(fC_df, index = "Var1", values = ["Var2", "spearman_correlation"]).stack().reset_index(level = 1)
print("For "+i+" features:")
print(fC_df_pivot)
try:
sb.clustermap(fC_df_pivot)
except ValueError as err:
mp.clf()
sb.heatmap(fC_df_pivot)
if(pVal_adjust_method=="none" or corr_threshold < 0.0):
mp.title("Correlations for "+i+" features", size=16)
else:
mp.title("Top significant correlations (FDR_adjusted_pValue<0.05) for "+i+" features", size=16)
mp.xticks(rotation=90)
mp.yticks(size=7)
fig_path = str(save_dir) + tagDir + 'Correlation_for_' + i + '_features.png'
mp.savefig(fig_path, orientation='landscape', dpi=90, bbox_inches='tight')
#mp.savefig(str(save_dir)+''+tagDir+'Correlation_for_'+i+'_features.png',orientation='landscape',dpi=90,bbox_inches='tight')
#data_image = open(str(save_dir)+tagDir+'Correlation_for_'+i+'_features.png', 'rb').read().encode('base64').replace('\n', '')
#image_tag_list.append('<img src="data:image/png;base64,{0}" style="max-width:50%;">'.format(data_image))
mp.clf()
outfileHTML=open(str(save_dir)+tagDir+model_type+".output.html",'a')
#outfileHTML.write("<h1 style=text-align:center;color:red;>"+"Radiogenomics Analysis Report"+"</h1>"+"\n")
outfileHTML.write("<h2 style=text-align:center;>"+"--------------------------Multivariate Correlations ("+corr_method+" based)-----------------------"+"</h2>"+"\n".join(image_tag_list)+"\n")
outfileHTML.close()
return(sorted(set(List_of_Var1)),sorted(set(List_of_Var2)))
##Reading datasets
def read_dataset(dataset):
if os.path.isfile(dataset):
raw_dataframe = pd.read_csv(dataset, sep=',')
else:
print("{} should be csv/tsv file ".format(dataset))
sys.exit()
dataframe = raw_dataframe.copy()
print("Shape of dataframe:{}".format(dataframe.shape))
return dataframe
##Normalization function
def normal_dataframe(dataframe, norm_type, header, normalize = True):
if normalize:
if norm_type =='min_max':
scaler = MinMaxScaler()
dataframe_scaled = scaler.fit_transform(dataframe)
#return dataframe_scaled
elif norm_type == 'Stand_scaler':
scaler = StandardScaler()
dataframe_scaled = scaler.fit_transform(dataframe)
#return dataframe_scaled
##Removing Zscore as it is redundant to StandScaler
#elif norm_type == 'zscore':
# dataframe_scaled = stats.zscore(dataframe)
# #return dataframe_scaled
elif norm_type == 'MaxAbsScaler':
scaler = MaxAbsScaler()
dataframe_scaled = scaler.fit_transform(dataframe)
#return dataframe_scaled
elif norm_type == 'RobustScaler':
scaler = RobustScaler()
dataframe_scaled = scaler.fit_transform(dataframe)
else:
print("Invalid normalization type/method detected. Skipping normalization. If you wish to normalize this dataset, then correct the normalization_method in the config file and rerun. Proceeding with no normalization")
## The dataframe needs to be converted to numpy to be consistent with the return value when normalization method is detected and normalization actually happens.
#d_array = dataframe.to_records(index='True')
#feature_array = d_array.indices()
#return d_array, feature_array
##Switch back to returning dataframe
return dataframe
##SILENTING conversion of numpy into dataframe as we do want to return numpy array now that the calling of normal_dataframe occurs outside the preprocessing function.
##Switch back to converting numpy into dataframe
dataframe_scaled=pd.DataFrame(data=dataframe_scaled,columns=header)
#feature_array=scaler.feature_names_in_
return dataframe_scaled
else:
#d_array = dataframe.to_records(index='True')
#feature_array = d_array.indices()
#return d_array, feature_array
##Switch back to returning dataframe
return dataframe
##Preprocessing of datasets
def preprocessing(dataframe , label, data_type, label_type, mode, tagDir, checkNA = True):
#try:
outfileHTML=open(str(save_dir)+tagDir+model_type+".output.html",'a')
outfileHTML.write("<h3>"+"No. of "+data_type+" features provided: "+str(len(dataframe.columns)-1)+"</h3>")
if(isinstance(label,pd.DataFrame)):
outfileHTML.write("<h3>"+"No. of "+label_type+" features provided:"+str(len(label.columns)-1)+"</h3>")
if checkNA:
dataframe.replace("", np.nan, inplace=True)
if dataframe.isnull().values.any():
dataframe = dataframe.dropna(axis=1, how='any')
if isinstance(label,pd.DataFrame):
label.replace("", np.nan, inplace=True)
if label.isnull().values.any():
label = label.dropna(axis=1, how='any')
#print label
if isinstance(label,pd.DataFrame):
if dataframe['ID'].equals(label['ID']):
outfileHTML.write("<h3>"+"SampleID check results: 'The SampleIDs match for "+data_type+" and "+label_type+" features'"+"</h3>"+"\n")
#outfileHTML.write("<h3>"+"No. of samples: "+"</h3>"+"\n")
print("The SampleIDs match for "+data_type+" and "+label_type+" features")
#dataframe = dataframe.drop(['ID'],axis = 1)
#label = label.drop(['ID'],axis = 1)
else:
sys.exit("The SampleIDs in data and label vary. Cannot proceed further. Please fix and rerun!")
label = label.drop(['ID'],axis = 1)
label_header=list(label.keys())
else:
label_header="NA"
sampleIDs = dataframe['ID']
outfileHTML.write("<h3>"+"No. of samples: "+str(len(sampleIDs))+"</h3>"+"\n")
dataframe = dataframe.drop(['ID'],axis = 1)
dataframe_header=list(dataframe.keys())
## SILENTING normalization on entire dataset as train and test dataset has to undergo normalizations differently later, thereby preventing leakage of test into train.
#if data_normalize_method != 'none':
# print("performing "+data_normalize_method+" normalization for "+data_type+" features")
# outfileHTML.write("<h3>"+"performing "+data_normalize_method+" normalization for "+data_type+" features"+"</h3>"+"\n")
# dataframe = normal_dataframe(dataframe ,data_normalize_method, dataframe_header)
#print(dataframe)
#if isinstance(label,pd.DataFrame):
# if label_normalize_method != 'none':
# #print(label)
# print("performing "+label_normalize_method+" for "+label_type+" features")
# outfileHTML.write("<h3>"+"performing "+label_normalize_method+" for "+label_type+" features"+"</h3>"+"\n")
# label = normal_dataframe(label ,label_normalize_method, label_header)
outfileHTML.close()
#c_=pd.concat([dataframe1, dataframe2], axis=1)
print(dataframe)
#dataframe = dataframe.loc[:, (dataframe!=0).any(axis=0)]; nan_value = float("NaN"); dataframe.replace("", nan_value, inplace=True); dataframe=dataframe.dropna()
#dataframe_header=list(dataframe.keys())
print(dataframe.shape[1])
if isinstance(label,pd.DataFrame):
#label = label.loc[:, (label!=0).any(axis=0)]; label.replace("", nan_value, inplace=True); label=label.dropna()
print(label)
#label_header=list(label.keys())
print(label.shape[1])
return dataframe , label, sampleIDs, label_header, dataframe_header
##Train-test split function
def splitdata(dataframe , label, t_size, mode_, data_normalize_method, label_normalize_method, data_type, label_type, dataframe_header, label_header, tagDir):
outfileHTML=open(str(save_dir)+tagDir+model_type+".output.html",'a')
train, test , Y_train , Y_test = train_test_split(dataframe, label , test_size = t_size)
##Converting numpy arrays to dataframe to perform normalization on them
train=pd.DataFrame(data=train,columns=dataframe_header)
print(train)
#train= train.loc[:, (train!=0).any(axis=0)]; nan_value = float("NaN"); train.replace("", nan_value, inplace=True); train=train.dropna()
test=pd.DataFrame(data=test,columns=dataframe_header)
print(test)
#test= test.loc[:, (test!=0).any(axis=0)]; nan_value = float("NaN"); test.replace("", nan_value, inplace=True); test=test.dropna()
Y_train=pd.DataFrame(data=Y_train,columns=label_header)
print(Y_train)
#Y_train= Y_train.loc[:, (Y_train!=0).any(axis=0)]; nan_value = float("NaN"); Y_train.replace("", nan_value, inplace=True); Y_train=Y_train.dropna()
Y_test=pd.DataFrame(data=Y_test,columns=label_header)
print(Y_test)
#Y_test= Y_test.loc[:, (Y_test!=0).any(axis=0)]; nan_value = float("NaN"); Y_test.replace("", nan_value, inplace=True); Y_test=Y_test.dropna()
#if mode_=="Train":
##Introducing normalizations for TRAIN and TEST datasets.
if data_normalize_method != 'none':
print("performing "+data_normalize_method+" normalization for "+data_type+" features")
##NORMALIZING TRAIN data
outfileHTML.write("<h3>"+"performing "+data_normalize_method+" normalization for "+data_type+" features for TRAIN set"+"</h3>"+"\n")
train= normal_dataframe(train, data_normalize_method, dataframe_header)
##Convert to dataframe with indices
#train_df = pd.DataFrame(train, index = train_features)
#train=train[:, ~np.isnan(train).any(axis=0)]
#train= train.loc[:, (train!=0).any(axis=0)]; nan_value = float("NaN"); train.replace("", nan_value, inplace=True); train=train.dropna()
print("Printing Normalized Train data:")
print(train)
##NORMALIZING TEST set
outfileHTML.write("<h3>"+"performing "+data_normalize_method+" normalization for "+data_type+" features for TEST set"+"</h3>"+"\n")
test= normal_dataframe(test, data_normalize_method, dataframe_header)
#test_df = pd.DataFrame(test, index = test_features)
#test=test[:, ~np.isnan(test).any(axis=0)]
#test= test.loc[:, (test!=0).any(axis=0)]; nan_value = float("NaN"); test.replace("", nan_value, inplace=True); test=test.dropna()
print("Printing Normalized Test data:")
print(test)
if isinstance(label,pd.DataFrame):
if label_normalize_method != 'none':
print("performing "+label_normalize_method+" for "+label_type+" features")
##NORMALIZING TRAINING label
outfileHTML.write("<h3>"+"performing "+label_normalize_method+" for "+label_type+" features for TRAIN set"+"</h3>"+"\n")
Y_train = normal_dataframe(Y_train, label_normalize_method, label_header)
#Y_train_df = pd.DataFrame(Y_train, index = Y_train_features)
#Y_train=Y_train[:, ~np.isnan(Y_train).any(axis=0)]
#Y_train= Y_train.loc[:, (Y_train!=0).any(axis=0)]; nan_value = float("NaN"); Y_train.replace("", nan_value, inplace=True); Y_train=Y_train.dropna()
print("Printing Normalized Train label:")
print(Y_train)
##NORMALIZING TEST label
outfileHTML.write("<h3>"+"performing "+label_normalize_method+" for "+label_type+" features for TEST set"+"</h3>"+"\n")
Y_test = normal_dataframe(Y_test, label_normalize_method, label_header)
#Y_test_df = pd.DataFrame(Y_test, index = Y_test_features)
#Y_test=Y_test[:, ~np.isnan(Y_test).any(axis=0)]
#Y_test= Y_test.loc[:, (Y_test!=0).any(axis=0)]; nan_value = float("NaN"); Y_test.replace("", nan_value, inplace=True); Y_test=Y_test.dropna()
print("Printing Normalized Test label:")
print(Y_test)
outfileHTML.write("<h1 style=text-align:center;color:purple>"+"-------------------------------Number of Samples for Training and Testing---------------------------------"+"</h1>"+"\n")
outfileHTML.write("<h3>"+"No. of samples for training:{}".format(len(train))+"</h3>"+"\n")
outfileHTML.write("<h3>"+"No. of samples for test:{}".format(len(test))+"</h3>"+"\n")
outfileHTML.close()
print("Trainig data:{} , Testing data:{} ".format(len(train) ,len(test)))
#if mode_=="validate":
# print("Custom test data:{} ".format(len(test)))
test.to_csv(str(save_dir)+""+tagDir+"Test_dataset.csv",sep="\t"); Y_test.to_csv(str(save_dir)+""+tagDir+"Y_test_dataset.csv",sep="\t"); Y_train.to_csv(str(save_dir)+""+tagDir+"Y_train_dataset.csv",sep="\t"); train.to_csv(str(save_dir)+""+tagDir+"Train_dataset.csv",sep="\t");
return train , Y_train , test , Y_test
##Build models, multiple options of modeltypes accepted from user per the method list below
def BuildModel(train , Y_train , test , Y_test , method, params, cv_par, scoring_par, gridsearch, param_grid, select_label_var_list, select_data_var_list, data_type, label_type, featureSelFrmModel_flag, tagDir, trainmodel):
'''
initializing model and training the model
'''
outfileHTML=open(str(save_dir)+tagDir+model_type+".output.html",'a')
outfileHTML.write("<h2 style=text-align:center;color:blue>"+"--------------------------Model Summary-----------------------"+"</h2>"+"\n")
if method in ['DecisionTree','LinearRegression', 'LinearModel' , 'LASSO', 'multiTaskLASSO', 'multiTaskLinearModel', 'RandomForest', 'DecisionTreeClassifier', 'MLPClassifier', 'LogisticRegression', 'SVC']:
if gridsearch == 'True':
#param_grid_keys=list(param_grid.keys())
if 'cv' in param_grid.keys():
cv_grid=param_grid['cv']
del param_grid['cv']
else:
cv_grid=None
if 'scoring' in param_grid.keys():
scoring_grid=param_grid['scoring']
del param_grid['scoring']
else:
scoring_grid=None
#for i in param_grid_keys:
# if re.search('cv',i):
# cv_grid = param_grid[i]
# del param_grid[i]
# elif re.search('scoring',i):
# scoring_grid = param_grid[i]
# del param_grid[i]
# else:
# continue
if method == 'LASSO':
if gridsearch == 'True':
try:
print(" Starting grid search for LASSO")
model = GridSearchCV(linear_model.Lasso(), param_grid=param_grid,cv=cv_grid,scoring=scoring_grid)
#print(model.get_params)
except:
print("Grid search status:{}".format(grid_search))
else:
#model = linear_model.Lasso(alpha=alpha_lasso)
model = linear_model.Lasso(**params)
#print(model.get_params)
if method == 'DecisionTree':
if gridsearch == 'True':
try:
print("starting grid search for Decision Tress")
model = GridSearchCV(tree.DecisionTreeRegressor(), param_grid=param_grid, scoring=scoring_grid, cv=cv_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
model = tree.DecisionTreeRegressor(**params)
#print(model.get_params)
if method == 'LinearRegression':
if gridsearch == 'True':
try:
print("starting grid search for Linear Regression")
model = GridSearchCV(LinearRegression(), param_grid=param_grid, scoring=scoring_grid, cv=cv_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
model = LinearRegression(**params)
#print(model.get_params)
if method == 'LinearModel':
if gridsearch == 'True':
#random_state=param_grid['random_state']
#del new_param_grid['random_state']
try:
print("Starting grid search for ElasticNet")
model = GridSearchCV(ElasticNet(), param_grid=param_grid , cv = cv_grid , scoring=scoring_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
model = ElasticNet(**params)
#print(model.get_params)
if method == 'multiTaskLASSO':
if gridsearch == 'True':
try:
print(" Starting grid search for MultiTaskLASSO")
model = GridSearchCV(linear_model.MultiTaskLasso(), param_grid=param_grid,cv=cv_grid,scoring=scoring_grid)
#print(model.get_params)
except:
print("Grid search status:{}".format(grid_search))
else:
#model = linear_model.Lasso(alpha=alpha_lasso)
model = linear_model.MultiTaskLasso(**params)
#print(model.get_params)
if method == 'multiTaskLinearModel':
if gridsearch == 'True':
#random_state=param_grid['random_state']
#del new_param_grid['random_state']
try:
print("Starting grid search for MultiTaskElasticNet")
model = GridSearchCV(MultiTaskElasticNet(), param_grid=param_grid , cv = cv_grid , scoring=scoring_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
model = MultiTaskElasticNet(**params)
if method == 'RandomForest':
if gridsearch == 'True':
#random_state=param_grid['random_state']
#del new_param_grid['random_state']
try:
print("Starting grid search for RandomForest")
model = GridSearchCV(RandomForestClassifier(), param_grid=param_grid , cv = cv_grid , scoring=scoring_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
#model = MultiTaskElasticNet(**params)
model = RandomForestClassifier(**params)
if method == 'DecisionTreeClassifier':
if gridsearch == 'True':
#random_state=param_grid['random_state']
#del new_param_grid['random_state']
try:
print("Starting grid search for DecisionTreeClassifier")
model = GridSearchCV(DecisionTreeClassifier(), param_grid=param_grid , cv = cv_grid , scoring=scoring_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
#model = MultiTaskElasticNet(**params)
model = DecisionTreeClassifier(**params)
if method == 'MLPClassifier':
if gridsearch == 'True':
#random_state=param_grid['random_state']
#del new_param_grid['random_state']
try:
print("Starting grid search for MLPClassifier")
model = GridSearchCV(MLPClassifier(), param_grid=param_grid , cv = cv_grid , scoring=scoring_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
#model = MultiTaskElasticNet(**params)
model = MLPClassifier(**params)
if method == 'LogisticRegression':
if gridsearch == 'True':
try:
print("starting grid search for Logistic Regression")
model = GridSearchCV(LogisticRegression(), param_grid=param_grid, scoring=scoring_grid, cv=cv_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
model = LogisticRegression(**params)
if method == 'SVC':
if gridsearch == 'True':
try:
print("starting grid search for SVC")
model = GridSearchCV(SVC(), param_grid=param_grid, scoring=scoring_grid, cv=cv_grid)
except:
print("Grid search status:{}".format(grid_search))
else:
model = SVC(**params)
else:
print("options are :DecisionTree, LinearRegression, LinearModel, LASSO, LinearModel (aka ElasticNet), multiTaskLinearModel, multiTaskLASSO, RandomForest, DecisionTreeClassifier, MLPClassifier, LogisticRegression, SVC")
if trainmodel:
#try:
'''
performing training
'''
## ADDING SelectFromModel for Feature Selection using model and using only those features for training and testing further (change featureSelFrmModel_flag to sfm_flag later)
if(featureSelFrmModel_flag==1):
column_headers__=train.columns
selector = SelectFromModel(estimator=model).fit(train, Y_train)
##Skipping the transform as it yields numpy array. Rather getting an array of features with "True or False" for selection. Extracting headers and then selecting only those features from train and test dataframes below.
#train=selector.transform(train)##The output is numpy array without headers.
##Get an array of "True or False" for features. True means selected, False means not-selected. Selection happens through feature importances yield by the model using SelectFromModel function above.
feature_selected_or_not_=selector.get_support()
##Make a dataframe of that array which has the header as names of each feature.
fsd=pd.DataFrame(data=feature_selected_or_not_)
fsd=pd.DataFrame.transpose(fsd)
fsd.columns=column_headers__
print(fsd)
##Drop the features that are "False", i.e. not selected.
fsd=fsd.drop(columns=fsd.columns[(fsd == False).any()])
print("These are the features selected by SelectFromModel function")
print(fsd)
fsd.to_csv(str(save_dir)+""+tagDir+'_'+model_type+'_features_selected.txt')
feature_headers__=fsd.columns
##Converting numpy array to dataframe with headers for selected features.
#train=pd.DataFrame(data=train,columns=feature_headers__)##no need to generate dataframe from numpy array anymore
train=train[feature_headers__]
print("This is the train set post feature selection")
print(train)
##Selecting same features in test as well.
print("This is the test set post feature selection")
test=test[feature_headers__]
print(test)
#outfileHTML=open(model_type+".output.html",'a')
#outfileHTML.write("<h1>"+"--------------------------Model Summary-----------------------"+"</h1>"+"\n")
outfileHTML.write("<h3>"+"Model Type : "+method+"</h3>"+"\n")
if gridsearch == 'True':
grid_result = model.fit(train ,Y_train)
outfileHTML.write("<h4>"+"Grid Search Metrics"+"</h4>"+"\n")
#outfileHTML.write("<h3>"+"Best Parameters: "+"</h3>"+"\n")
#outfileHTML.write("<h3>"+ "scoring="+str(scoring_grid)+"</h3>"+"\n")
outfileHTML.write("\n"+"<h5>"+'Best Score : '+str(grid_result.best_score_)+"</h5>"+"\n")
else:
scores = cross_val_score(model.fit(train ,Y_train), train , Y_train, cv=cv_par , scoring = scoring_par)
outfileHTML.write("<h4>"+"Cross Validation Metrics:"+"</h4>"+"\n")
outfileHTML.write("<h5>"+"Parameters: cv="+str(cv_par)+" scoring="+str(scoring_par)+"</h5>"+"\n")
outfileHTML.write("<h5>"+"Cross validation score:{}".format(-1*scores.mean())+"</h5>"+"\n")
store_params=model.get_params();
outfileHTML.write("<h3>"+"Model Parameters:"+"</h3>"+"\n")
for i in store_params.keys():
outfileHTML.write("<h4>"+str(i)+":"+str(store_params[i])+"</h4>")
#Y_pred_train = model.predict(train)
#print("MSE of Train set:{}".format(metrics.mean_squared_error(Y_train, Y_pred_train)))
#Y_pred = model.predict(test)
#print("MSE of Test set:{}".format(metrics.mean_squared_error(Y_test, Y_pred)))
outfileHTML.close()
evaluate(model,train,Y_train,select_label_var_list,'train_eval',model_type, data_type, label_type, tagDir, gridsearch)
print(Y_test)
returned_label_resulted=evaluate(model,test,Y_test,select_label_var_list,'test_eval',model_type, data_type, label_type, tagDir, gridsearch)
##Permut each label coulmn of the test dataset 100 times and validate the model
for l in returned_label_resulted:
for n in range(1,21):
Y_test[[l]]=np.random.permutation(Y_test[[l]])
evaluate(model, test, Y_test , select_label_var_list, 'validation'+'_permut_'+str(n)+'_'+str(l), model_type, data_type, label_type, tagDir, gridsearch)
#except:
#print(" Issue with model training")
##Print tree for train
return model
##Evaluate the models for test or validation.
def evaluate(model , test , Y_test, select_label_var_list, prefix, model_type, data_type, label_type, tagDir, gridsearch):
'''
evalauting the model preformance
'''
outfileHTML=open(str(save_dir)+tagDir+model_type+".output.html",'a')
Y_pred = model.predict(test)
print(Y_test)
print(Y_pred)
if(prefix=="train_eval"):
heading="Model evaluation for Train data"
color="red"
elif(prefix=="test_eval"):
heading="Model evaluation for Test data"
color="green"
elif(prefix=="validation" or re.search("validation",prefix)):
heading="Model evaluation for Validation data"
color="black"
outfileHTML.write("<h2 style=text-align:center;color:"+color+">"+"----------------"+heading+"--------------------"+"</h2>"+"\n")
outfileHTML.write("<h3>"+"Min Square Error for the Model"+"</h3>"+"\n")
outfileHTML.write("<h4>"+"MSE of "+prefix+" set:{}".format(metrics.mean_squared_error(Y_test, Y_pred))+"</h4>")
#Converting numpy.ndarray to dataframes
column_dict_Y_test=dict()
column_dict_Y_predict=dict()
#for i in label_header:
# column_dict_Y_test.update({i: Y_test[:, label_header.index(i)]})
# column_dict_Y_predict.update({i: Y_predict[:, label_header.index(i)]})
#Y_test_df=pd.DataFrame(column_dict_Y_test)
#Y_pred_df=pd.DataFrame(column_dict_Y_predict)
Y_test_df = pd.DataFrame(data=Y_test, columns=select_label_var_list)
#print Y_test_df
Y_pred_df = pd.DataFrame(data=Y_pred, columns=select_label_var_list)
#print Y_pred_df
##CALCULATE the RATIO of RMSE to Orignal Stdev for each label-feature
ratio_low_dict=dict()
ratio_high_dict=dict()
ratio_dict=dict()
mean_dict=dict()
rmse_dict=dict()
r2_score_dict=dict()
std_dict=dict()
n_rows=(len(select_label_var_list)/5)+1
n_cols=5
n_plots=len(select_label_var_list)
print("performing further calculations for "+prefix)
for i in select_label_var_list:
Y_test_c=pd.DataFrame.to_numpy(Y_test_df[[i]])
Y_pred_c=pd.DataFrame.to_numpy(Y_pred_df[[i]])
#if(i=='module1'):
# print Y_test_c
# print Y_pred_c
rmse=sqrt(metrics.mean_squared_error(Y_test_c, Y_pred_c))
r2score=r2_score(Y_test_c, Y_pred_c)
mean=np.mean(Y_test_c)
stdev=np.std(Y_test_c)
if(stdev==0):
if(mean==0):
print(" The feature column "+i+" has mean and stdev values as zero. Its rmse is "+ str(rmse) + ". Not considering it for rmse:stdev ratio calculation")
ratio="NA"
else:
if(rmse!=0):
#print("Note:The feature column "+i+" has a non-zero mean and stdev is zero. It seems the feature column is monotonic. Its rmse is "+str(rmse)+" .Not considering the feature for rmse:stdev ratio calculation")
#ratio="NA"
print("Note:The feature column "+i+" has a non-zero mean and stdev is zero. It seems the feature column is monotonic. Its rmse is "+str(rmse)+" .Considering rmse:stdev ratio = rmse")
ratio=rmse
elif(rmse==0):
#print("Note:The feature column "+i+" has mean, stdev and rmse as zero. Hence excluding it from rmse:stdev ratio calculation")
#ratio="NA"
print("Note:The feature column "+i+" has mean, stdev and rmse as zero. Hence rmse:stdev ratio =rmse")
ratio=rmse
else:
ratio=abs(rmse)/abs(stdev)
#print i+"\t"+str(mean)
#rmse_n_mean_df.update()
mean_dict.update({i:mean})
rmse_dict.update({i:rmse})
r2_score_dict.update({i:r2score})
std_dict.update({i:stdev})
if(ratio=="NA"):
ratio=-1.0
elif(ratio <= 1.0):
ratio_low_dict.update({i:ratio})
elif(ratio > 1.0):
ratio_high_dict.update({i:ratio})
#ratio_dict.update({i:ratio})
#mean_for_rmse_high_dict.update({i:mean})
#print("HIGH RMSE for "+i+" :{}".format(rmse))
#mse_high_file.write("HIGH MSE for "+i+" :{}".format(mse))
#mse_high_file.write("\n")
ratio_dict.update({i:ratio})
label_header_low_ratio=list(ratio_low_dict.keys())
mean_header=list(mean_dict.keys())
mean_df=pd.DataFrame.from_dict(mean_dict,orient='index',columns=['Observed Mean'])
#mean_df.to_csv(str(save_dir)+prefix+"_"+model_type+"_mean.csv")
std_header=list(std_dict.keys())
std_df=pd.DataFrame.from_dict(std_dict,orient='index',columns=['Observed Stdev'])
#std_df.to_csv(str(save_dir)+prefix+"_"+model_type+"_std.csv")
rmse_header=list(rmse_dict.keys())
rmse_df=pd.DataFrame.from_dict(rmse_dict,orient='index',columns=['RMSE between observed and predicted values'])
ratio_header=list(ratio_dict.keys())
ratio_df=pd.DataFrame.from_dict(ratio_dict,orient='index',columns=['Ratio_of_RMSE_and_Stdev'])
r2_score_header=list(r2_score_dict.keys())
r2_score_df=pd.DataFrame.from_dict(r2_score_dict,orient='index',columns=['r2_score'])
#rmse_df.to_csv(str(save_dir)+prefix+"_"+model_type+"_rmse.csv")
#print rmse_header
#print mean_header
rmse_n_mean_df = rmse_df.merge(mean_df, how='outer', left_index=True, right_index=True)
rmse_n_mean_n_std_df = rmse_n_mean_df.merge(std_df,how='outer', left_index=True, right_index=True)
rmse_n_mean_n_std_n_ratio_df = rmse_n_mean_n_std_df.merge(ratio_df,how='outer', left_index=True, right_index=True)
rmse_n_mean_n_std_n_ratio_n_r2_score_df = rmse_n_mean_n_std_n_ratio_df.merge(r2_score_df,how='outer', left_index=True, right_index=True)
count=0
Only_ratio_n_mean_df=rmse_n_mean_n_std_n_ratio_df[["Observed Mean","Ratio_of_RMSE_and_Stdev"]]
#Only_ratio_n_mean_df.plot.scatter("Observed Mean","Ratio_of_RMSE_and_Stdev")
Only_ratio_n_mean_df.plot.bar()
#for e in list(rmse_n_mean_n_std_n_ratio_df.index.values):
# mp.scatter(rmse_n_mean_n_std_n_ratio_df.loc(e,"Observed Mean"), rmse_n_mean_n_std_n_ratio_df.loc(e,"Ratio_of_RMSE_and_Stdev"), label=e, marker=count)
# count=count+1
# if(count==11):
# count=0
rmse_n_mean_n_std_n_ratio_df.to_csv(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_rmse_mean_std_and_ratio.csv')
rmse_n_mean_n_std_n_ratio_n_r2_score_df.to_csv(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_rmse_mean_std_and_ratio_and_r2_score.csv')
#if len(list(rmse_n_mean_n_std_n_ratio_df.index.values)) <= 40:
# mp.legend(loc=(1.04,0))
#else:
# mp.legend(bbox_to_anchor=(1.04, 1.04, 2.04, 2.04), loc='upper left', ncol=2, mode="expand")
mp.xticks(rotation=90)
mp.ylabel('Ratio_of_RMSE_and_Stdev')
mp.xlabel(label_type+" Features")
mp.title("Observed Mean and Ratio_of_RMSE_and_Stdev",size=12)
mp.legend(loc=(1.04,0.5))
#mp.savefig(str(save_dir)+'test_result_plots_high_rmse.png',orientation='landscape',dpi=200,bbox_inches='tight')
mp.savefig(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_rmse_mean_std_and_ratio.png',bbox_inches='tight')
mp.clf()
Only_ratio_n_r2_score_df=rmse_n_mean_n_std_n_ratio_n_r2_score_df[["Ratio_of_RMSE_and_Stdev","r2_score"]]
Only_ratio_n_r2_score_df.plot.bar()
mp.xticks(rotation=90)
mp.ylabel('r2_score')
mp.xlabel(label_type+" Features")
mp.title("Ratio_of_RMSE_and_Stdev and r2_score",size=12)
mp.legend(loc=(1.04,0.5))
#mp.savefig(str(save_dir)+'test_result_plots_high_rmse.png',orientation='landscape',dpi=200,bbox_inches='tight')
mp.savefig(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_rmse_std_and_ratio_and_r2_score.png',bbox_inches='tight')
mp.clf()
Y_test_low_ratio_df=Y_test_df[label_header_low_ratio]
Y_pred_low_ratio_df=Y_pred_df[label_header_low_ratio]
count=0
for c in Y_pred_low_ratio_df.columns:
mp.scatter(Y_test_low_ratio_df[c], Y_pred_low_ratio_df[c], label=c, marker=count)
count=count+1
if(count==11):
count=0
mp.xlabel('Actual_values')
mp.ylabel('Predicted_values')
mp.title("Actual_values v/s Predicted Values - for features with Low RMSE:Actual_Stdev",size=9)
#mp.legend(loc=(1.04,0))
#mp.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc='lower left',
# ncol=2, mode="expand")
if len(label_header_low_ratio) <= 40:
mp.legend(loc=(1.04,0))
else:
mp.legend(bbox_to_anchor=(1.04, 1.04, 2.04, 2.04), loc='upper left', ncol=2, mode="expand")
#mp.savefig(str(save_dir)+'test_result_plots_low_rmse.png',orientation='landscape',dpi=200,bbox_inches='tight')
mp.savefig(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_test_result_plots_low_ratio.png',bbox_inches='tight')
mp.clf()
label_header_high_ratio=list(ratio_high_dict.keys())
Y_test_high_ratio_df=Y_test_df[label_header_high_ratio]
Y_pred_high_ratio_df=Y_pred_df[label_header_high_ratio]
count=0
for d in Y_pred_high_ratio_df.columns:
mp.scatter(Y_test_high_ratio_df[d], Y_pred_high_ratio_df[d], label=d, marker=count)
count=count+1
if(count==11):
count=0
mp.xlabel('Actual_values')
mp.ylabel('Predicted_values')
mp.title("Actual_values v/s Predicted Values - for features with high RMSE:Actual_Stdev",size=9)
mp.legend(loc=(1.04,0))
#mp.savefig(str(save_dir)+'test_result_plots_high_rmse.png',orientation='landscape',dpi=200,bbox_inches='tight')
mp.savefig(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_test_result_plots_high_ratio.png',bbox_inches='tight')
mp.clf()
#Merging mean_df and rmse_dict
if(len(ratio_low_dict)>1):
ratio_low_df=pd.DataFrame.from_dict(ratio_low_dict,orient='index',columns=['RMSE/Stdev'])
#mean_for_rmse_low_df=pd.DataFrame.from_dict(mean_for_rmse_low_dict,orient='index',columns=['Mean'])
#mse_df=pd.DataFrame({'Label_features':label_list, 'MSE':mse_val})
#print(mse_df)
#mse_df.set_index('Label_features')
ratio_low_df.to_csv(str(save_dir)+""+tagDir+prefix+"_"+model_type+"_Labels_with_Low_Ratio.csv")
#mean_for_rmse_low_df.to_csv(str(save_dir)+prefix+"_"+model_type+"_Original_Mean_for_Labels_with_Low_RMSE.csv")
#rmse_low_n_mean_df = rmse_low_df.merge(mean_for_rmse_low_df, how='outer', left_index=True, right_index=True)
#mp.plot(rmse_low_df)
ratio_low_df.plot(kind='bar')
mp.ylabel('RMSE/Stdev')
mp.xlabel('Label Features')
mp.xticks(rotation=90)
if len(label_header_low_ratio) <= 40:
mp.xticks(size=5)
else:
mp.xticks(size=3)
##mp.title(i,size=1)
mp.title("Low RMSE/Stdev for the label features",size=12)
mp.savefig(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_Low_Ratio_plot.png',orientation='landscape',dpi=100,bbox_inches='tight')
mp.clf()
#pd.plotting.scatter_matrix(rmse_low_n_mean_df)
#mp.title("Comparing labels with Low RMSEs against their actual Mean values")
#mp.savefig(str(save_dir)+prefix+'_'+model_type+'_Labels_w_Low_RMSE_and_actual_Means_plot.png',orientation='landscape',dpi=100,bbox_inches='tight')
#mp.clf()
else:
print("Only 1 key:value pair in ratio_low_dict, so not proceeding with its plotting")
if(len(ratio_high_dict)>1):
ratio_high_df=pd.DataFrame.from_dict(ratio_high_dict,orient='index',columns=['RMSE/Stdev'])
#mean_for_rmse_high_df=pd.DataFrame.from_dict(mean_for_rmse_high_dict,orient='index',columns=['RMSE'])
#mse_df=pd.DataFrame({'Label_features':label_list, 'MSE':mse_val})
#print(mse_df)
#mse_df.set_index('Label_features')
ratio_high_df.to_csv(str(save_dir)+""+tagDir+prefix+"_"+model_type+"_Labels_with_High_Ratio.csv")
#mean_for_rmse_high_df.to_csv(str(save_dir)+prefix+"_"+model_type+"_Original_Mean_for_Labels_with_High_RMSE.csv")
#rmse_high_n_mean_df = rmse_high_df.merge(mean_for_rmse_high_df, how='outer', left_index=True, right_index=True)
mp.plot(ratio_high_df)
mp.ylabel('RMSE/Stdev')
mp.xlabel('Label Features')
mp.xticks(rotation=90)
mp.xticks(size=4)
mp.title("High RMSE/Stdev for the label features",size=12)
mp.savefig(str(save_dir)+""+tagDir+prefix+'_'+model_type+'_High_Ratio_plot.png',orientation='landscape',dpi=100,bbox_inches='tight')
mp.clf()
#pd.plotting.scatter_matrix(rmse_high_n_mean_df)
#mp.title("Comparing labels with High RMSEs against their actual Mean values")
#mp.savefig(str(save_dir)+prefix+'_'+model_type+'_Labels_w_High_RMSE_and_actual_Means_plot.png',orientation='landscape',dpi=100,bbox_inches='tight')
#mp.clf()
else:
print("Only 1 key:value pair in ratio_high_dict, so not proceeding with its plotting")
if(prefix=="train_eval"):
heading="Model evaluation for Train data"
elif(prefix=="test_eval"):
heading="Model evaluation for Test data"
elif(prefix=="validation"):
heading="Model evaluation for Validation data"
outfileHTML.write("<h4 stype=text-align:center;color:brown>"+"No. of features showing LOW 'RMSE/Stdev' (<=1.0): "+"\n"+str(len(label_header_low_ratio))+"</h4>")
outfileHTML.write("<h5>"+"All such features with their Low 'RMSE/Stdev' values could be found in output file: "+prefix+"_"+model_type+"_Labels_with_Low_Ratio.csv"+"</h4>"+"\n")
outfileHTML.write("<h4 stype=text-align:center;color:brown>"+"No. of features showing HIGH 'RMSE/Stdev' (>1.0): "+"\n"+str(len(label_header_high_ratio))+"</h4>")
outfileHTML.write("<h5>"+"All such features with their High 'RMSE/Stdev' values could be found in output file: "+prefix+"_"+model_type+"_Labels_with_High_Ratio.csv"+"</h4>"+"\n"+"\n")
outfileHTML.write("<h3>"+heading+" for label features showing Low 'RMSE/Stdev' (<=1.0)"+"</h3>"+"\n")
dmg1 = f"{save_dir}{tagDir}{prefix}_{model_type}_test_result_plots_low_ratio.png"
with open(dmg1, 'rb') as image_file:
data_image1 = base64.b64encode(image_file.read()).decode('utf-8').replace('\n', '')
#data_image1 = open(str(save_dir)+tagDir+prefix+'_'+model_type+'_test_result_plots_low_ratio.png', 'rb').read().encode('base64').replace('\n', '')
img_tag1 = '<img src="data:image/png;base64,{0}">'.format(data_image1)
outfileHTML.write(img_tag1+"\n")
if(os.path.exists(""+tagDir+prefix+'_'+model_type+'_Low_Ratio_plot.png')):
data_image2 = open(str(save_dir)+tagDir+prefix+'_'+model_type+'_Low_Ratio_plot.png', 'rb').read().encode('base64').replace('\n', '')
img_tag2 = '<img src="data:image/png;base64,{0}">'.format(data_image2)
outfileHTML.write(img_tag2+"\n")
dmg3 = f"{save_dir}{tagDir}{prefix}_{model_type}_rmse_mean_std_and_ratio.png"
with open(dmg3, 'rb') as image_file:
# Base64 encode and then decode to string to remove newlines if necessary
data_image3 = base64.b64encode(image_file.read()).decode('utf-8').replace('\n', '')
#data_image3 = open(str(save_dir)+tagDir+prefix+'_'+model_type+'_rmse_mean_std_and_ratio.png', 'rb').read().encode('base64').replace('\n', '')
img_tag3 = '<img src="data:image/png;base64,{0}">'.format(data_image3)
outfileHTML.write(img_tag3+"\n")
outfileHTML.write('Content-type: text/html\n\n'+""+"\n"+'<link href="default.css" rel="stylesheet" type="text/css" />')
decision_thresholds=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
##Adding feature-importance calculations
FI_handle=open(str(save_dir)+tagDir+prefix+"_"+"FeatureImportances.txt",'w+')
X_features=list(test.columns)
Y_features=list(Y_test.columns)
if(gridsearch == 'True'):
FI_handle.close()
elif(model_type == "MLPClassifier" or model_type == "SVC"):
FI_handle.close()
elif(model_type!="DecisionTree" and model_type!="RandomForest" and model_type!="DecisionTreeClassifier"):
importance = model.coef_
FI_handle.write("Y_Features, X_features_scored:"+str(X_features)+"\n")
for u,k in enumerate(importance):
FI_handle.write(str(Y_features[u])+","+str(k)+"\n")
FI_handle.close()
else:
#importance = model.feature_importances_
#FI_handle.write("Features, Score"+"\n")
#for u,k in enumerate(importance):
# FI_handle.write(str(X_features[u])+","+str(k)+"\n")
FI_handle.close()
##CALCULATE AUCs BASED on the decision thresholds list above.
AUC_fH=open(str(save_dir)+tagDir+prefix+"_"+"AUC_values.txt",'w+')
AUC_fH.write("label"+"\t"+"AUC_value"+"\t"+"decision_threshold"+"\n")
label_resulted=[]
if(model_type == 'RandomForest' or model_type == 'DecisionTreeClassifier' or model_type == 'MLPClassifier' or model_type == 'LogisticRegression' or model_type == "SVC"):
for l in select_label_var_list:
print (l)
fpr, tpr, thresholds = roc_curve(Y_test_df[[l]],Y_pred_df[[l]])
AUC_value=roc_auc_score(Y_test_df[[l]],Y_pred_df[[l]])
if(AUC_value>0.70 and r2_score_dict[l]>0.25):
if l not in label_resulted:
label_resulted.append(l)
AUC_fH.write(l+"\t"+str(AUC_value)+"\n")
if(math.isnan(float(AUC_value))):
print("###AUC_value is nan#######")
print (fpr); print (tpr); print("#################")
else:
mp.xlabel('False Positive Rate')
mp.ylabel('True Positive Rate')
#mp.ylabel('AUC')
mp.ylim([0.0, 1.05])
mp.xlim([0.0,1.0])
#mp.xticks([0.0,0.2,0.4,0.6,0.8,1.0])
mp.plot(fpr,tpr,color="darkorange",lw=2,label="ROC curve (area = %0.2f)" % AUC_value)
mp.title(' Receiver Operating Characteristic curve for'+l,fontsize=15)
mp.legend(loc="lower right")
mp.savefig(str(save_dir)+tagDir+prefix+'_'+model_type+'_AUC'+l+'.png',orientation='landscape',dpi=100,bbox_inches='tight')
mp.clf()
dmg4 = f"{save_dir}{tagDir}{prefix}_{model_type}_AUC{l}.png"
with open(dmg4, 'rb') as image_file:
# Base64 encode the binary data and then decode to a string, removing newlines if necessary
data_image4 = base64.b64encode(image_file.read()).decode('utf-8').replace('\n', '')
#data_image4 = open(str(save_dir)+tagDir+prefix+'_'+model_type+'_AUC'+l+'.png', 'rb').read().encode('base64').replace('\n', '')
img_tag4 = '<img src="data:image/png;base64,{0}">'.format(data_image4)
outfileHTML.write(img_tag4+"\n")
AUC_fH.close()
outfileHTML.write('Content-type: text/html\n\n'+""+"\n"+'<link href="default.css" rel="stylesheet" type="text/css" />')
#fig = mp.figure(figsize=(25,20))
#tree_= plot_tree(model,
# feature_names=X_features,
# class_names=Y_features,
# filled=True)
#fig.savefig(prefix+'_'+model_type+"decistion_tree.png")
#dotfile = open(str(save_dir)+tagDir+prefix+"_dtree.dot", 'w')
#tree.export_graphviz(model, out_file = dotfile, feature_names = test.columns)
#dotfile.close()
#os.system("dot -Tpng "+str(save_dir)+tagDir+prefix+"_dtree.dot"+" -o "+str(save_dir)+str(tagDir)+str(prefix)+"_dtree.png")
#tree.export_graphviz(model, out_file=prefix+'_'+model_type+'_tree.dot')
return label_resulted
for l in select_label_var_list:
print (l)
#fig, ax_=mp.subplots(1,1,figsize=(9,9))
decision_threshold_list=[]
AUC_value_list=[]
#print Y_test_df[l]
#print Y_test_df[[l]]
for i in decision_thresholds:
print (i)
Y_test_df_n=binarize(pd.DataFrame.to_numpy(abs(Y_test_df[[l]])),threshold=i)
#Y_pred_df_n=binarize(pd.DataFrame.to_numpy(abs(Y_pred_df[[l]])),threshold=i)
Y_pred_df_n=Y_pred_df[[l]]##Keeping Ypredict to be continuous,i.e. as it is.
if(np.all((Y_test_df_n==0))):
print("For decision threshold "+str(i)+":")
print("Seems all values for label column "+l+" are zero. Hence not considering it for decision_threshold vs AUC plot")
else: