def fit_logistic_GLM(X, y,
C_value = np.array([-4,5]),
num_cv = 5,
verbose = False,
intercept_scaling = 10,
penalty = 'l1',
reg_strength = None,
plot_results = False
):
scores_to_return = []
loss_score = []
X = pp.scale(X)
# If regularization strength isn't specified, CV to find it
if reg_strength == None:
kf = SKFold(y = y, n_folds = num_cv)
C_values = np.logspace(C_value[0], C_value[1], 10)
C_dict = {"C": C_values}
best_param = []
#------------------------------------------------------------------------------
for train, test in kf:
X_train, X_test, y_train, y_test = X[train], X[test], y[train], y[test]
# Do grid search for regularization parameter
clf = GSCV(
LR(C=1, penalty=penalty, dual=False,intercept_scaling=intercept_scaling),
C_dict,
cv=num_cv
)
# Fit model
clf.fit(X_train,y_train)
best_param.append(clf.best_params_['C'])
if verbose:
for params, mean_score, scores in clf.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() / 2, params))
if verbose:
print np.mean(np.asarray(scores))
reg_strength = gmean(best_param)
#------------------------------------------------------------------------------
kf2 = SKFold(y = y, n_folds = num_cv)
clf = []
clf_temp = LR(
penalty=penalty,
dual=False,
C = reg_strength,
intercept_scaling = intercept_scaling
)
for train, test in kf2:
X_train, X_test, y_train, y_test = X[train], X[test], y[train], y[test]
clf_temp.fit(X_train, y_train)
scores_to_return.append(clf_temp.score(X_test, y_test))
clf.append(clf_temp)
pred = clf_temp.predict_proba(X_test)[:,1]
loss_score.append(lossFx(y_test,pred))
#------------------------------------------------------------------------------
# Plot results
if plot_results:
plot_logistic_fit(clf,X,kf2)
# Returns model, scores of each CV, best C parameter, CV fold indices
return clf, scores_to_return, loss_score, reg_strength, kf2
def fit_RF(X,y,
num_estimators = None,
verbose = False,
plot_importance = False,
num_cv = 5):
X = pp.scale(X)
# If num_estimators not provided, CV to estimate it
if num_estimators == None:
kf = SKFold(y = y, n_folds = num_cv)
estimator_nums = np.logspace(0, np.size(X,axis=1), 10)
estimator_nums = estimator_nums.astype(int)
est_dict = {"n_estimators": estimator_nums}
best_param = []
#------------------------------------------------------------------------------
for train, test in kf:
X_train, X_test, y_train, y_test = X[train], X[test], y[train], y[test]
# Do grid search for num_estimators parameter
clf = GSCV(
RF(n_estimators=1),
est_dict,
cv=num_cv
)
# Fit model
clf.fit(X_train,y_train)
best_param.append(clf.best_params_['n_estimators'])
if verbose:
for params, mean_score, scores in clf.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() / 2, params))
if verbose:
print np.mean(np.asarray(scores))
num_estimators = gmean(best_param)
#------------------------------------------------------------------------------
# Measure accuracy
kf2 = SKFold(y = y, n_folds = num_cv)
clf = []
accuracy = []
importances = []
loss_score = []
clf_temp = RF(n_estimators = num_estimators,
n_jobs = 1,
verbose = verbose)#,
# compute_importances = True)
for train, test in kf2:
X_train, X_test, y_train, y_test = X[train], X[test], y[train], y[test]
clf_temp.fit(X_train,y_train)
clf.append(clf_temp)
pred = clf_temp.predict_proba(X_test)[:,1]
loss_score.append(lossFx(y_test,pred))
accuracy.append(clf_temp.score(X_test, y_test))
importances.append(clf_temp.feature_importances_)
std = np.std([tree.feature_importances_ for tree in clf_temp.estimators_],axis=0)
indices = np.argsort(importances)[::-1]
#------------------------------------------------------------------------------
if verbose:
print("Feature ranking:")
for f in range(5):
print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
#------------------------------------------------------------------------------
if plot_importance:
plot.figure()
plot.title("Feature importances")
plot.bar(range(10), importances[indices],
color="r", yerr=std[indices], align="center")
plot.xticks(range(10), indices)
plot.xlim([-1, 10])
plot.show()
return clf, accuracy, loss_score, num_estimators, kf2
