def CV_Binary_stats(X, y, model,n=10) :
'''
http://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
Note that some of the metrics here ONLY work for BINARY tasks.
This will be VERY slow compared to the built-in, multicore CV implementation. (Unless
used with a classifier that is parallelized anyway, such as RF).
By default, balances weights when fitting
http://scikit-learn.org/stable/modules/cross_validation.html#computing-cross-validated-metrics
'''
from sklearn.metrics import precision_score, accuracy_score, recall_score,precision_recall_fscore_support
mean_auc = 0.0
mean_precision = 0.0
mean_recall = 0.0
mean_accuracy = 0.0
sss = StratifiedShuffleSplit(y, n_iter=n, test_size=0.2, random_state=0)
for train_index, test_index in sss:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# for i in range(n) :
# # for each iteration, randomly hold out 30% of the data as CV set
# X_train, X_cv, y_train, y_cv = cross_validation.train_test_split(X, y,
# test_size=.15,
# random_state=i)
# cv=StratifiedShuffleSplit(y=y_train, n_iter=11, test_size=0.11)
# train model and make predictions
model.fit(X_train, y_train,sample_weight=balance_weights(y_train))
# preds = model.predict(X_cv)
preds = model.predict(X_test)
'''
# ROC_AUC - Restricted to binary (not multiclass) case.
fpr, tpr, thresholds = metrics.roc_curve(y_cv, preds)
roc_auc = metrics.auc(fpr, tpr)
# print("( %d/%d)" % (i + 1, n))
mean_auc += roc_auc
'''
accuracy = accuracy_score(y_cv, preds)
precision = precision_score(y_cv, preds)
recall = recall_score(y_cv, preds)
mean_accuracy += accuracy
mean_precision += precision
mean_recall += recall
mean_accuracy = (mean_accuracy / n)
mean_precision = mean_precision / n
mean_recall = mean_recall / n
# mean_auc = mean_auc / n
print('mean_accuracy: %s ' %(round(mean_accuracy, 3)))
print('mean_precision: %s ' %(round(mean_precision, 3)))
print('mean_recall: %s ' %(round(mean_recall, 3)))
# print('mean_auc: %s ' %(round(mean_auc, 3)))
return (mean_accuracy,mean_precision,mean_recall)
def CV_Binary_stats(X, y, model,n=10) :
'''
http://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
Note that some of the metrics here ONLY work for BINARY tasks.
This will be VERY slow compared to the built-in, multicore CV implementation. (Unless
used with a classifier that is parallelized anyway, such as RF).
By default, balances weights when fitting
http://scikit-learn.org/stable/modules/cross_validation.html#computing-cross-validated-metrics
'''
from sklearn.metrics import precision_score, accuracy_score, recall_score,precision_recall_fscore_support
mean_auc = 0.0
mean_precision = 0.0
mean_recall = 0.0
mean_accuracy = 0.0
sss = StratifiedShuffleSplit(y, n_iter=n, test_size=0.2, random_state=0)
for train_index, test_index in sss:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# for i in range(n) :
# # for each iteration, randomly hold out 30% of the data as CV set
# X_train, X_cv, y_train, y_cv = cross_validation.train_test_split(X, y,
# test_size=.15,
# random_state=i)
# cv=StratifiedShuffleSplit(y=y_train, n_iter=11, test_size=0.11)
# train model and make predictions
model.fit(X_train, y_train,sample_weight=balance_weights(y_train))
# preds = model.predict(X_cv)
preds = model.predict(X_test)
'''
# ROC_AUC - Restricted to binary (not multiclass) case.
fpr, tpr, thresholds = metrics.roc_curve(y_cv, preds)
roc_auc = metrics.auc(fpr, tpr)
# print("( %d/%d)" % (i + 1, n))
mean_auc += roc_auc
'''
accuracy = accuracy_score(y_cv, preds)
precision = precision_score(y_cv, preds)
recall = recall_score(y_cv, preds)
mean_accuracy += accuracy
mean_precision += precision
mean_recall += recall
mean_accuracy = (mean_accuracy / n)
mean_precision = mean_precision / n
mean_recall = mean_recall / n
# mean_auc = mean_auc / n
print('mean_accuracy: %s ' %(round(mean_accuracy, 3)))
print('mean_precision: %s ' %(round(mean_precision, 3)))
print('mean_recall: %s ' %(round(mean_recall, 3)))
# print('mean_auc: %s ' %(round(mean_auc, 3)))
return (mean_accuracy,mean_precision,mean_recall)
def ModelParam_GridSearch(X_train, y_train, cv=3,scoreParam = 'precision'):
'''
Basic grid searchCV for multiple classifiers' perf & parameters.
This is limited as currently implemented, but still computationally expensive.
Not guaranteed to reach even a local optima, but good to get a
rough idea of parameters for the classifiers. (Does not address pre-processing)
More classifiers can be added as desired, and parameters expanded.
Later: Add options for RBM + Logit; PCA; ICA; LDA.
(Further) Feature selection should be implemented within the CV pipeline, if you wish
to avoid overfitting. (Note the effects of )
See also
http://scikit-learn-laboratory.readthedocs.org/en/latest/_modules/skll/learner.html
Possible Scoreparams: scoreParam = 'f1','accuracy', 'precision', 'roc_auc'..
'''
# pipeline1 = Pipeline('clf', RandomForestClassifier() )
pipeline1 = RandomForestClassifier(n_jobs=-1)
pipeline2 = SVC(cache_size=1900)
pipeline3 = GradientBoostingClassifier()
pipeline4 = LogisticRegression()
'RandomForestClassifier:'
parameters1 = {
'n_estimators': [120],
'criterion': ['gini'],
'max_features': ['auto',0.4],
'min_samples_leaf':[1,2],
'min_samples_split':[2,3],
'n_jobs':[-1],
'max_depth': [8, None]
}
'SVC:'
parameters2 = {
'C': [0.2, 1,10,50,100,1000],
# 'kernel': ['linear','rbf'],
'kernel': ['rbf'],
'gamma': [0.1,0.0, 1.0],
'cache_size':[1900],
'class_weight':['auto',None],
}
# , 'poly','sigmoid']
'GradientBoostingClassifier'
parameters3 = {
'max_depth':[5,7],
'n_estimators': [80],
# 'min_samples_leaf':[2],
# 'learning_rate': [0.1, 0.05],
'max_features': ['auto',0.4]
}
# 'min_samples_leaf':[1,2],
'LogisticRegression:'
parameters4 = {
'C': [1.0,10,100],
'penalty': ['l1','l2'],'class_weight':['auto',None]
}
pars = [parameters1, parameters2, parameters3,parameters4]
pips = [pipeline1, pipeline2, pipeline3, pipeline4]
'Store and return the best estimator found (and score)'
bestEst=None
bestScore=0
print ("Starting gridsearch to find best model hyperparameters.")
'Gridsearch done "in bits" due to some classifiers not supporting sample_Weight'
def gs_fit(gs):
nonlocal bestEst
nonlocal bestScore
gs.fit(X_train, y_train)
report(gs.grid_scores_)
# http://stackoverflow.com/questions/18210799/scikit-learn-sample-try-out-with-my-classifier-and-data
if gs.best_score_>bestScore:
bestEst = gs.best_estimator_
bestScore = gs.best_score_
print("Updated best Est, new Best score:",bestScore)
# for i in range(len(pars)): #Orig
for i in range(2):
clf_name = str(pips[i])
print(clf_name[0:clf_name.index("(")])
gs = GridSearchCV(estimator=pips[i], param_grid=pars[i],
verbose=1, refit=True, n_jobs=-1,iid=False,
fit_params={'sample_weight': balance_weights(y_train)},
pre_dispatch='1.5*n_jobs',scoring=scoreParam,
cv=StratifiedKFold(y_train,n_folds=cv,shuffle=True))
#Valid scoring options: ['accuracy', 'average_precision', 'f1', 'precision', 'recall', 'roc_auc']
gs_fit(gs)
i=3 #Logistic Regression
gs = GridSearchCV(estimator=pips[i], param_grid=pars[i],
verbose=0, refit=True, n_jobs=-1,iid=True,
pre_dispatch='1.5*n_jobs',scoring=scoreParam,
cv=StratifiedKFold(y_train,n_folds=cv,shuffle=True))
gs_fit(gs)
# 'http://stackoverflow.com/questions/13051706/scikit-learn-using-sample-weight-in-grid-search?rq=1'
# "http://stackoverflow.com/questions/20082674/unbalanced-classification-using-randomforestclassifier-in-sklearn"
# "Set Class weights (then into sample weights: https://github.com/scikit-learn/scikit-learn/blob/8dab222cfe894126dfb67832da2f4e871b87bce7/sklearn/utils/class_weight.py"
#print (gs.best_score_)
print("Best Predictor:", bestEst, "Score: ",(bestScore))
return(bestEst,bestScore)
def ModelParam_GridSearch(X_train, y_train, cv=3,scoreParam = 'precision'):
'''
Basic grid searchCV for multiple classifiers' perf & parameters.
This is limited as currently implemented, but still computationally expensive.
Not guaranteed to reach even a local optima, but good to get a
rough idea of parameters for the classifiers. (Does not address pre-processing)
More classifiers can be added as desired, and parameters expanded.
Later: Add options for RBM + Logit; PCA; ICA; LDA.
(Further) Feature selection should be implemented within the CV pipeline, if you wish
to avoid overfitting. (Note the effects of )
See also
http://scikit-learn-laboratory.readthedocs.org/en/latest/_modules/skll/learner.html
Possible Scoreparams: scoreParam = 'f1','accuracy', 'precision', 'roc_auc'..
'''
# pipeline1 = Pipeline('clf', RandomForestClassifier() )
pipeline1 = RandomForestClassifier(n_jobs=-1)
pipeline2 = SVC(cache_size=1900)
pipeline3 = GradientBoostingClassifier()
pipeline4 = LogisticRegression()
'RandomForestClassifier:'
parameters1 = {
'n_estimators': [120],
'criterion': ['gini'],
'max_features': ['auto',0.4],
'min_samples_leaf':[1,2],
'min_samples_split':[2,3],
'n_jobs':[-1],
'max_depth': [8, None]
}
'SVC:'
parameters2 = {
'C': [0.2, 1,10,50,100,1000],
# 'kernel': ['linear','rbf'],
'kernel': ['rbf'],
'gamma': [0.1,0.0, 1.0],
'cache_size':[1900],
'class_weight':['auto',None],
}
# , 'poly','sigmoid']
'GradientBoostingClassifier'
parameters3 = {
'max_depth':[5,7],
'n_estimators': [80],
# 'min_samples_leaf':[2],
# 'learning_rate': [0.1, 0.05],
'max_features': ['auto',0.4]
}
# 'min_samples_leaf':[1,2],
'LogisticRegression:'
parameters4 = {
'C': [1.0,10,100],
'penalty': ['l1','l2'],'class_weight':['auto',None]
}
pars = [parameters1, parameters2, parameters3,parameters4]
pips = [pipeline1, pipeline2, pipeline3, pipeline4]
'Store and return the best estimator found (and score)'
bestEst=None
bestScore=0
print ("Starting gridsearch to find best model hyperparameters.")
'Gridsearch done "in bits" due to some classifiers not supporting sample_Weight'
def gs_fit(gs):
nonlocal bestEst
nonlocal bestScore
gs.fit(X_train, y_train)
report(gs.grid_scores_)
# http://stackoverflow.com/questions/18210799/scikit-learn-sample-try-out-with-my-classifier-and-data
if gs.best_score_>bestScore:
bestEst = gs.best_estimator_
bestScore = gs.best_score_
print("Updated best Est, new Best score:",bestScore)
# for i in range(len(pars)): #Orig
for i in range(2):
clf_name = str(pips[i])
print(clf_name[0:clf_name.index("(")])
gs = GridSearchCV(estimator=pips[i], param_grid=pars[i],
verbose=1, refit=True, n_jobs=-1,iid=False,
fit_params={'sample_weight': balance_weights(y_train)},
pre_dispatch='1.5*n_jobs',scoring=scoreParam,
cv=StratifiedKFold(y_train,n_folds=cv,shuffle=True))
#Valid scoring options: ['accuracy', 'average_precision', 'f1', 'precision', 'recall', 'roc_auc']
gs_fit(gs)
i=3 #Logistic Regression
gs = GridSearchCV(estimator=pips[i], param_grid=pars[i],
verbose=0, refit=True, n_jobs=-1,iid=True,
pre_dispatch='1.5*n_jobs',scoring=scoreParam,
cv=StratifiedKFold(y_train,n_folds=cv,shuffle=True))
gs_fit(gs)
# 'http://stackoverflow.com/questions/13051706/scikit-learn-using-sample-weight-in-grid-search?rq=1'
# "http://stackoverflow.com/questions/20082674/unbalanced-classification-using-randomforestclassifier-in-sklearn"
# "Set Class weights (then into sample weights: https://github.com/scikit-learn/scikit-learn/blob/8dab222cfe894126dfb67832da2f4e871b87bce7/sklearn/utils/class_weight.py"
#print (gs.best_score_)
print("Best Predictor:", bestEst, "Score: ",(bestScore))
return(bestEst,bestScore)
