tanu/ML_AI_training
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

modified loopity and multclass3 to have skf_cv as a parameters for cv

Browse source
This commit is contained in:
Tanushree Tunstall 2022-03-17 18:17:58 +00:00
parent 97620c1bb0
commit d0c329a1d9
8 changed files with 161 additions and 127 deletions
Download patch file Download diff file Expand all files Collapse all files

125
MultClassPipe3.py
View file

@ -61,23 +61,39 @@ from imblearn.combine import SMOTEENN
from imblearn.under_sampling import EditedNearestNeighbours
#%%
rs = {'random_state': 42}
# Done: add preprocessing step with one hot encoder
# Done: get accuracy and other scores through K-fold stratified cv
# rs = {'random_state': 42}
# njobs = {'n_jobs': 10}
scoring_fn = ({ 'fscore' : make_scorer(f1_score)
, 'mcc' : make_scorer(matthews_corrcoef)
, 'precision' : make_scorer(precision_score)
, 'recall' : make_scorer(recall_score)
, 'accuracy' : make_scorer(accuracy_score)
, 'roc_auc' : make_scorer(roc_auc_score)
#, 'jaccard' : make_scorer(jaccard_score)
scoring_fn = ({ 'fscore' : make_scorer(f1_score)
, 'mcc' : make_scorer(matthews_corrcoef)
, 'precision' : make_scorer(precision_score)
, 'recall' : make_scorer(recall_score)
, 'accuracy' : make_scorer(accuracy_score)
, 'roc_auc' : make_scorer(roc_auc_score)
#, 'jaccard' : make_scorer(jaccard_score)
})
# Multiple Classification - Model Pipeline
def MultClassPipelineCV(X_train, X_test, y_train, y_test, input_df, var_type = ['numerical', 'categorical','mixed']):
def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categorical','mixed']):
'''
@ param input_df: input features
@ type: df with input features WITHOUT the target variable
@param target: target (or output) feature
@type: df or np.array or Series
@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
@type: int or StratifiedKfold()
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
@type: list
returns
Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training
'''
# determine categorical and numerical features
numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
numerical_ix
@ -98,66 +114,61 @@ def MultClassPipelineCV(X_train, X_test, y_train, y_test, input_df, var_type = [
col_transform = ColumnTransformer(transformers = t
, remainder='passthrough')
#%%
#%% Specify multiple Classification models
log_reg = LogisticRegression(**rs)
nb = BernoulliNB()
knn = KNeighborsClassifier()
svm = SVC(**rs)
mlp = MLPClassifier(max_iter=500, **rs)
dt = DecisionTreeClassifier(**rs)
et = ExtraTreesClassifier(**rs)
rf = RandomForestClassifier(**rs)
rf2 = RandomForestClassifier(
min_samples_leaf=50,
n_estimators=150,
bootstrap=True,
oob_score=True,
n_jobs=-1,
random_state=42,
max_features='auto')
xgb = XGBClassifier(**rs, verbosity=0)
nb = BernoulliNB()
knn = KNeighborsClassifier()
svm = SVC(**rs)
mlp = MLPClassifier(max_iter = 500, **rs)
dt = DecisionTreeClassifier(**rs)
et = ExtraTreesClassifier(**rs)
rf = RandomForestClassifier(**rs)
rf2 = RandomForestClassifier(
min_samples_leaf = 50
, n_estimators = 150
, bootstrap = True
, oob_score = True
, **njobs
, **rs
, max_features = 'auto')
xgb = XGBClassifier(**rs
, verbosity = 0, use_label_encoder =False)
models = [
('Logistic Regression', log_reg),
('Naive Bayes', nb),
('K-Nearest Neighbors', knn),
('SVM', svm),
('MLP', mlp),
('Decision Tree', dt),
('Extra Trees', et),
('Random Forest', rf),
('Random Forest2', rf2),
#('XGBoost', xgb)
]
skf_cv_scores = {}
models = [('Logistic Regression', log_reg)
, ('Naive Bayes' , nb)
, ('K-Nearest Neighbors', knn)
, ('SVM' , svm)
, ('MLP' , mlp)
, ('Decision Tree' , dt)
, ('Extra Trees' , et)
, ('Random Forest' , rf)
, ('Naive Bayes' , nb)
, ('Random Forest2' , rf2)
, ('XGBoost' , xgb)]
mm_skf_scoresD = {}
for model_name, model_fn in models:
print('\nModel_name:', model_name
, '\nModel func:' , model_fn
, '\nList of models:', models)
# model_pipeline = Pipeline([
# ('pre' , MinMaxScaler())
# , ('model' , model_fn)])
model_pipeline = Pipeline([
('prep' , col_transform)
, ('model' , model_fn)])
, ('model' , model_fn)])
print('Running model pipeline:', model_pipeline)
skf_cv = cross_validate(model_pipeline
, X_train
, y_train
, cv = 10
skf_cv_mod = cross_validate(model_pipeline
, input_df
, target
, cv = skf_cv
, scoring = scoring_fn
, return_train_score = True)
skf_cv_scores[model_name] = {}
for key, value in skf_cv.items():
mm_skf_scoresD[model_name] = {}
for key, value in skf_cv_mod.items():
print('\nkey:', key, '\nvalue:', value)
print('\nmean value:', mean(value))
skf_cv_scores[model_name][key] = round(mean(value),2)
#pp.pprint(skf_cv_scores)
return(skf_cv_scores)
mm_skf_scoresD[model_name][key] = round(mean(value),2)
#pp.pprint(mm_skf_scoresD)
return(mm_skf_scoresD)

36
MultClassPipe3_CALL.py
View file

@ -5,29 +5,19 @@ Created on Tue Mar 15 11:09:50 2022
@author: tanu
"""
# stratified shuffle split
X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
, num_df_wtgt['mutation_class']
, test_size = 0.33
, **rs
, shuffle = True
, stratify = num_df_wtgt['mutation_class'])
#%% Data
X = all_df_wtgt[numerical_FN+categorical_FN]
y = all_df_wtgt['mutation_class']
#%% variables
y_train.to_frame().value_counts().plot(kind = 'bar')
y_test.to_frame().value_counts().plot(kind = 'bar')
MultClassPipelineCV(X_train, X_test, y_train, y_test
, input_df = num_df_wtgt[numerical_FN]
, var_type = 'numerical')
#%% MultClassPipeSKFCV: function call()
mm_skf_scoresD = MultClassPipeSKFCV(input_df = X
, target = y
, var_type = 'mixed'
, skf_cv = skf_cv)
skf_cv_scores = MultClassPipelineCV(X_train, X_test, y_train, y_test
, input_df = num_df_wtgt[numerical_FN]
, var_type = 'numerical')
pp.pprint(skf_cv_scores)
# construct a df
skf_cv_scores_df = pd.DataFrame(skf_cv_scores)
skf_cv_scores_df
skf_cv_scores_df_test = skf_cv_scores_df.filter(like='test_', axis=0)
skf_cv_scores_df_train = skf_cv_scores_df.filter(like='train_', axis=0)
mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
mm_skf_scores_df_all
mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results

BIN
__pycache__/MultClassPipe3.cpython-37.pyc
View file

Binary file not shown.

BIN
__pycache__/loopity_loop.cpython-37.pyc
View file

Binary file not shown.

8
base_estimator.py
View file

@ -138,6 +138,14 @@ parameters = [
#'tfidf__stop_words': [None],
'clf__estimator__alpha': (1e-2, 1e-3, 1e-1),
},
{
'clf__estimator': [LogisticRegression()],
'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
'penalty': ['none', 'l1', 'l2', 'elasticnet'],
'max_iter': list(range(100,800,100)),
'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
},
]
pipeline = Pipeline([

41
imports.py
View file

@ -17,8 +17,12 @@ from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
from xgboost import XGBClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
@ -52,11 +56,29 @@ from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline
#from sklearn.datasets import make_classification
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_validate, cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
from imblearn.combine import SMOTEENN
from imblearn.under_sampling import EditedNearestNeighbours
from sklearn.model_selection import GridSearchCV
from sklearn.base import BaseEstimator
scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
, 'fscore' : make_scorer(f1_score)
, 'mcc' : make_scorer(matthews_corrcoef)
, 'precision' : make_scorer(precision_score)
, 'recall' : make_scorer(recall_score)
, 'roc_auc' : make_scorer(roc_auc_score)
})
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
skf_cv = StratifiedKFold(n_splits = 10
#, shuffle = False, random_state= None)
, shuffle = True,**rs)
#%%
homedir = os.path.expanduser("~")
os.chdir(homedir + "/git/ML_AI_training/")
@ -64,8 +86,8 @@ os.chdir(homedir + "/git/ML_AI_training/")
# my function
from MultClassPipe import MultClassPipeline
from MultClassPipe2 import MultClassPipeline2
from loopity_loop import MultClassPipeSKF
from MultClassPipe3 import MultClassPipelineCV
from loopity_loop import MultClassPipeSKFLoop
from MultClassPipe3 import MultClassPipeSKFCV
gene = 'pncA'
@ -199,3 +221,16 @@ cat_df_wtgt.shape
all_df_wtgt = my_df[numerical_FN + categorical_FN + ['mutation_class']]
all_df_wtgt.shape
#%%
#%% Get train-test split and scoring functions
X = num_df_wtgt[numerical_FN]
y = num_df_wtgt['mutation_class']
X_train, X_test, y_train, y_test = train_test_split(X
,y
, test_size = 0.33
, random_state = 2
, shuffle = True
, stratify = y)

57
loopity_loop.py
View file

@ -33,23 +33,30 @@ from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoe
from statistics import mean, stdev, median, mode
#%%
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
# Done: add preprocessing step with one hot encoder
# TODO: supply stratified K-fold cv train and test data
# TODO: supply stratified K-fold cv train and test dataskf
# TODO: get accuracy and other scores through K-fold cv
# Multiple Classification - Model Pipeline
def MultClassPipeSKF(input_df, y_targetF, var_type = ['numerical', 'categorical','mixed'], skf_splits = 10):
def MultClassPipeSKFLoop(input_df, target, skf_cv, var_type = ['numerical','categorical','mixed']):
'''
@ param input_df: input features
@ type: df (gets converted to np.array for stratified Kfold, and helps identify names to apply column transformation)
@ type: df with input features WITHOUT the target variable
@param y_outputF: target (or output) feature
@type: df or np.array
@param target: target (or output) feature
@type: df or np.array or Series
@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
@type: int or StratifiedKfold()
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-hot encoder)
@type: list
returns
multiple classification model scores
Dict containing multiple classification scores for each model and each Stratified Kfold
'''
# Determine categorical and numerical features
@ -86,17 +93,17 @@ def MultClassPipeSKF(input_df, y_targetF, var_type = ['numerical', 'categorical'
, n_estimators = 150
, bootstrap = True
, oob_score = True
, n_jobs = -1
, **njobs
, **rs
, max_features = 'auto')
xgb = XGBClassifier(**rs, verbosity = 0)
xgb = XGBClassifier(**rs, verbosity = 0, use_label_encoder = False)
classification_metrics = {
'F1_score': []
,'MCC': []
,'Precision': []
,'Recall': []
,'Accuracy': []
, 'Accuracy': []
,'ROC_AUC': []
}
models = [
@ -109,33 +116,29 @@ def MultClassPipeSKF(input_df, y_targetF, var_type = ['numerical', 'categorical'
, ('Extra Trees' , et)
, ('Random Forest' , rf)
, ('Naive Bayes' , nb)
, ('Random Forest2' , rf2)
#, ('XGBoost' , xgb)
, ('Random Forest2' , rf2)
, ('XGBoost' , xgb)
]
skf = StratifiedKFold(n_splits = skf_splits
, shuffle = True
, **rs)
# skf = StratifiedKFold(n_splits = 10
# #, shuffle = False, random_state= None)
# , shuffle = True,**rs)
# skf_dict = {}
fold_no = 1
fold_dict={}
for model_name, model in models:
fold_dict.update({ model_name: {}})
#scores_df = pd.DataFrame()
for train_index, test_index in skf.split(input_df, y_targetF):
for train_index, test_index in skf_cv.split(input_df, target):
x_train_fold, x_test_fold = input_df.iloc[train_index], input_df.iloc[test_index]
y_train_fold, y_test_fold = y_targetF.iloc[train_index], y_targetF.iloc[test_index]
y_train_fold, y_test_fold = target.iloc[train_index], target.iloc[test_index]
#print("Fold: ", fold_no, len(train_index), len(test_index))
for model_name, model in models:
print("\nStart of model", model_name, "\nLoop no.", fold_no)
#skf_dict.update({model_name: classification_metrics })
model_pipeline = Pipeline(steps=[('prep' , col_transform)
model_pipeline = Pipeline(steps=[('prep' , col_transform)
, ('classifier' , model)])
model_pipeline.fit(x_train_fold, y_train_fold)
y_pred_fold = model_pipeline.predict(x_test_fold)
@ -168,14 +171,4 @@ def MultClassPipeSKF(input_df, y_targetF, var_type = ['numerical', 'categorical'
fold_dict[model_name][fold].update({'ROC_AUC' : roc_auc})
fold_no +=1
#pp.pprint(skf_dict)
return(fold_dict)
#%% CAll function
# t3_res = MultClassPipeSKF(input_df = numerical_features_df
# , y_targetF = target1
# , var_type = 'numerical'
# , skf_splits = 10)
# pp.pprint(t3_res)
# #print(t3_res)
return(fold_dict)

21
loopity_loop_CALL.py
View file

@ -5,22 +5,19 @@ Created on Fri Mar 11 11:15:50 2022
@author: tanu
"""
#%%
del(t3_res)
# t3_res = MultClassPipeSKF(input_df = numerical_features_df
# , y_targetF = target1
# , var_type = 'numerical'
# , skf_splits = 10)
# pp.pprint(t3_res)
# #print(t3_res)
#%% variables
rs = {'random_state': 42}
t3_res = MultClassPipeSKF(input_df = num_df_wtgt[numerical_FN]
, y_targetF = num_df_wtgt['mutation_class']
skf_cv = StratifiedKFold(n_splits = 10
#, shuffle = False, random_state= None)
, shuffle = True,**rs)
#%% MultClassPipeSKFLoop: function call()
t3_res = MultClassPipeSKFLoop(input_df = num_df_wtgt[numerical_FN]
, target = num_df_wtgt['mutation_class']
, var_type = 'numerical'
, skf_splits = 10)
, skf_cv = skf_cv)
pp.pprint(t3_res)
#print(t3_res)
################################################################
# extract items from wwithin a nested dict
#%% Classification Metrics we need to mean()