def eval_model():
comments, labels = load_extended_data()
clf1 = build_base_model()
clf2 = build_elasticnet_model()
clf3 = build_stacked_model()
clf4 = build_nltk_model()
models = [clf1, clf2, clf3, clf4]
#models = [clf1]
cv = ShuffleSplit(len(comments),
n_iterations=5,
test_size=0.2,
indices=True)
scores = []
for train, test in cv:
probs_common = np.zeros((len(test), 2))
for clf in models:
X_train, y_train = comments[train], labels[train]
X_test, y_test = comments[test], labels[test]
clf.fit(X_train, y_train)
probs = clf.predict_proba(X_test)
print("score: %f" % auc_score(y_test, probs[:, 1]))
probs_common += probs
probs_common /= 4.
scores.append(auc_score(y_test, probs_common[:, 1]))
print("combined score: %f" % scores[-1])
print(np.mean(scores), np.std(scores))
def grid_search():
comments, labels = load_data()
param_grid = dict(logr__C=np.arange(1, 20, 5))
clf = build_nltk_model()
cv = ShuffleSplit(len(comments), n_iterations=10, test_size=0.2)
grid = GridSearchCV(clf,
cv=cv,
param_grid=param_grid,
verbose=4,
n_jobs=12,
score_func=auc_score)
grid.fit(comments, labels)
print(grid.best_score_)
print(grid.best_params_)
tracer()
cv_scores = grid.scores_
for param in cv_scores.params:
means, errors = cv_scores.accumulated(param, 'max')
plt.errorbar(cv_scores.values[param], means, yerr=errors)
plt.xlabel(param)
plt.ylim((0.85, 0.93))
plt.savefig("grid_plot_%s.png" % param)
plt.close()
comments_test, dates_test = load_test()
prob_pred = grid.best_estimator_.predict_proba(comments_test)
write_test(prob_pred[:, 1])
def eval_model():
comments, labels = load_extended_data()
clf1 = build_base_model()
clf2 = build_elasticnet_model()
clf3 = build_stacked_model()
clf4 = build_nltk_model()
models = [clf1, clf2, clf3, clf4]
#models = [clf1]
cv = ShuffleSplit(len(comments), n_iterations=5, test_size=0.2,
indices=True)
scores = []
for train, test in cv:
probs_common = np.zeros((len(test), 2))
for clf in models:
X_train, y_train = comments[train], labels[train]
X_test, y_test = comments[test], labels[test]
clf.fit(X_train, y_train)
probs = clf.predict_proba(X_test)
print("score: %f" % auc_score(y_test, probs[:, 1]))
probs_common += probs
probs_common /= 4.
scores.append(auc_score(y_test, probs_common[:, 1]))
print("combined score: %f" % scores[-1])
print(np.mean(scores), np.std(scores))
def apply_models():
comments, labels = load_extended_data()
comments_test = load_test("impermium_verification_set_.csv")
clf1 = build_base_model()
clf2 = build_elasticnet_model()
clf3 = build_stacked_model()
clf4 = build_nltk_model()
models = [clf1, clf2, clf3, clf4]
probs_common = np.zeros((len(comments_test), 2))
for i, clf in enumerate(models):
clf.fit(comments, labels)
probs = clf.predict_proba(comments_test)
#print("score: %f" % auc_score(labels_test, probs[:, 1]))
probs_common += probs
write_test(probs[:, 1], "test_prediction_model_%d.csv" % i,
ds="impermium_verification_set_.csv")
probs_common /= 4.
#score = auc_score(labels_test, probs_common[:, 1])
#print("combined score: %f" % score)
write_test(probs_common[:, 1], "test_prediction_combined.csv",
ds="impermium_verification_set_.csv")
def apply_models():
comments, labels = load_extended_data()
comments_test = load_test("impermium_verification_set_.csv")
clf1 = build_base_model()
clf2 = build_elasticnet_model()
clf3 = build_stacked_model()
clf4 = build_nltk_model()
models = [clf1, clf2, clf3, clf4]
probs_common = np.zeros((len(comments_test), 2))
for i, clf in enumerate(models):
clf.fit(comments, labels)
probs = clf.predict_proba(comments_test)
#print("score: %f" % auc_score(labels_test, probs[:, 1]))
probs_common += probs
write_test(probs[:, 1],
"test_prediction_model_%d.csv" % i,
ds="impermium_verification_set_.csv")
probs_common /= 4.
#score = auc_score(labels_test, probs_common[:, 1])
#print("combined score: %f" % score)
write_test(probs_common[:, 1],
"test_prediction_combined.csv",
ds="impermium_verification_set_.csv")
def grid_search():
comments, labels = load_data()
param_grid = dict(logr__C=np.arange(1, 20, 5))
clf = build_nltk_model()
cv = ShuffleSplit(len(comments), n_iterations=10, test_size=0.2)
grid = GridSearchCV(clf, cv=cv, param_grid=param_grid, verbose=4,
n_jobs=12, score_func=auc_score)
grid.fit(comments, labels)
print(grid.best_score_)
print(grid.best_params_)
tracer()
cv_scores = grid.scores_
for param in cv_scores.params:
means, errors = cv_scores.accumulated(param, 'max')
plt.errorbar(cv_scores.values[param], means, yerr=errors)
plt.xlabel(param)
plt.ylim((0.85, 0.93))
plt.savefig("grid_plot_%s.png" % param)
plt.close()
comments_test, dates_test = load_test()
prob_pred = grid.best_estimator_.predict_proba(comments_test)
write_test(prob_pred[:, 1])
