def grid_search_multinomial_nb():
nb_parameters = {
"alpha": [1, 0.1, 0.01, 0.001, 0.0001, 0.00001]
}
pipeline = TextPipeline(MultinomialNB(), nb_parameters, n_jobs=6, verbose = 1)
corpus_folder = "train_pages_preprocessed/"
corpus, metadata = create_corpus(corpus_folder, ["bad", "good"])
vfunc = np.vectorize(binarize)
pipeline.fit(corpus, vfunc(metadata['label']))
pipeline.save_results("results/naive_bayes_results.csv")
def grid_search_logistic_regression():
lg_parameters = {
"penalty": ["l1", "l2"],
"C": [0.001, 0.01, 0.1, 1, 10, 100, 1000]
}
pipeline = TextPipeline(LogisticRegression(), lg_parameters, n_jobs=6, verbose = 1)
corpus_folder = "train_pages_preprocessed/"
corpus, metadata = create_corpus(corpus_folder, ["bad", "good"])
vfunc = np.vectorize(binarize)
pipeline.fit(corpus, vfunc(metadata['label']))
pipeline.save_results("results/logistic_regression_results.csv")
def grid_search_svm():
svc_parameters = {
'C': (0.1, 1, 10, 100), #0.1 remover 100
'gamma': ('auto', 0.001, 0.01, 0.1, 1),
'kernel': ('poly', 'rbf')
}
pipeline = TextPipeline(SVC(), svc_parameters, n_jobs=6, verbose = 1)
corpus_folder = "train_pages_preprocessed/"
corpus, metadata = create_corpus(corpus_folder, ["bad", "good"])
vfunc = np.vectorize(binarize)
pipeline.fit(corpus, vfunc(metadata['label']))
pipeline.save_results("results/svm_results2.csv")
def grid_search_mlp():
mlp_parameters = {
"activation": ["relu", "tanh", "logistic"],
"learning_rate_init":[0.0001, 0.001, 0.01],
"hidden_layer_sizes":[(30,), (60,), (100,)]
}
pipeline = TextPipeline(MLPClassifier(), mlp_parameters, n_jobs=6, verbose = 1)
corpus_folder = "train_pages_preprocessed/"
corpus, metadata = create_corpus(corpus_folder, ["bad", "good"])
vfunc = np.vectorize(binarize)
pipeline.fit(corpus, vfunc(metadata['label']))
pipeline.save_results("results/mlp_results.csv")
def grid_search_random_forest():
rf_parameters = {
"max_depth": [3, None],
"max_features": ["log2", "sqrt", None],
"min_samples_split": [2, 3, 10],
"min_samples_leaf": [1, 3, 10],
"criterion": ["gini"]
}
pipeline = TextPipeline(RandomForestClassifier(), rf_parameters, n_jobs=6, verbose = 1)
corpus_folder = "train_pages_preprocessed/"
corpus, metadata = create_corpus(corpus_folder, ["bad", "good"])
vfunc = np.vectorize(binarize)
pipeline.fit(corpus, vfunc(metadata['label']))
pipeline.save_results("results/random_forest_results2.csv")
def grid_search_knn():
knn_parameters = {
"n_neighbors": [1, 2, 3, 4],
"weights": ["uniform", "distance"],
}
pipeline = TextPipeline(KNeighborsClassifier(),
knn_parameters,
n_jobs=-1,
verbose=1)
corpus_folder = "train_pages_preprocessed/"
corpus, metadata = create_corpus(corpus_folder, ["bad", "good"])
vfunc = np.vectorize(binarize)
pipeline.fit(corpus, vfunc(metadata['label']))
pipeline.save_results("results/knn_results.csv")