def tfidf_cloud(n_trees):
dio = DataIO("/data/Settings_cloud.json")
submission = False
min_samples_split = 2
param = """Normal count vector with max 200. New submission which is repeatable.
and nicer
count_vector_titles = TfidfVectorizer(
read_column(train_filename, column_name),
max_features=200, norm='l1', smooth_idf=True, sublinear_tf=False, use_idf=True)
"""
if submission:
type_n = "train_full"
type_v = "valid_full"
else:
type_n = "train"
type_v = "valid"
#features = dio.join_features("%s_" + type_n + "_tfidf_matrix_max_f_200",
#["Title", "FullDescription", "LocationRaw"],
#extra_features)
#validation_features = dio.join_features("%s_" + type_v + "_tfidf_matrix_max_f_200",
#["Title", "FullDescription", "LocationRaw"],
#extra_valid_features)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_features", features)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_valid_features", validation_features)
def load(filename):
return joblib.load(path_join("/data", filename))
features = load("train_200f_noNorm_categoryTimeType_tfidfl1_features_jl")
validation_features = load(
"train_200f_noNorm_categoryTimeType_tfidfl1_valid_features_jl")
print "features", features.shape
print "valid features", validation_features.shape
#salaries = dio.get_salaries(type_n, log=True)
#if not submission:
#valid_salaries = dio.get_salaries(type_v, log=True)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_salaries", salaries)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_valid_salaries", valid_salaries)
#joblib.dump(salaries, "train_200f_noNorm_categoryTimeType_tfidfl2_salaries_jl", compress=5)
#joblib.dump(valid_salaries, "train_200f_noNorm_categoryTimeType_tfidfl2_valid_salaries_jl", compress=5)
#TODO: valid salaries so narobe dumpane
salaries = load("train_200f_noNorm_categoryTimeType_tfidfl2_salaries_jl")
valid_salaries = load(
"train_200f_noNorm_categoryTimeType_tfidfl2_valid_salaries_jl")
dio.is_log = True
print salaries.shape
name = "ExtraTree_min_sample%d_%dtrees_200f_noNorm_categoryTimeType_tfidfl1_new_log" % (
min_samples_split, n_trees)
print name
#dio.save_prediction("testni", np.array([1,2,3]), type_n="testno")
classifier = ExtraTreesRegressor(
n_estimators=n_trees,
verbose=2,
n_jobs=4, # 2 jobs on submission / 4 on valid test
oob_score=False,
min_samples_split=min_samples_split,
random_state=3465343)
#dio.save_model(classifier, "testni_model", 99.)
classifier.fit(features, salaries)
predictions = classifier.predict(validation_features)
if submission:
dio.save_prediction(name, predictions, type_n=type_v)
dio.write_submission(name + ".csv", predictions=predictions)
else:
dio.compare_valid_pred(valid_salaries, predictions)
metric = dio.error_metric
mae = metric(valid_salaries, predictions)
print "MAE validation: ", mae
dio.save_model(classifier, name, mae)
dio.save_prediction(name, predictions, type_n=type_v)
print features[salaries_idx[0], :].shape
print salaries[salaries_idx].shape
classifier.fit(features[salaries_idx[0], :], salaries[salaries_idx])
predictions_part = classifier.predict(validation_features[valid_idx[0]])
return predictions_part, valid_idx
predictions = np.zeros_like(valid_salaries)
for cur_class_id in range(num_classes + 1):
predictions_part, idx = predict(cur_class_id)
if idx is not None:
predictions[idx] = predictions_part
print "Part MAE: ", metric(valid_salaries[idx], predictions_part)
if submission:
dio.save_prediction(name, predictions, type_n=type_v)
dio.write_submission(name + ".csv", predictions=predictions)
else:
dio.compare_valid_pred(valid_salaries, predictions)
metric = dio.error_metric
mae = metric(valid_salaries, predictions)
print "MAE validation: ", mae
dio.save_model(ExtraTreesRegressor(), name, mae)
dio.save_prediction(name, predictions, type_n=type_v)
#oob_predictions = classifier.oob_prediction_
#mae_oob = mean_absolute_error(salaries, oob_predictions)
#print "MAE OOB: ", mae_oob
#classifier1 = ExtraTreesRegressor(n_estimators=n_trees,
#verbose=1,
#n_jobs=3,
#oob_score=False,
#min_samples_split=min_samples_split,
#random_state=3465343)
#scores = cross_val_score(classifier1, features, salaries, cv=3, score_func=metric, verbose=1)
mae_pred = metric(valid_salaries[idx], predictions_part)
mae_best_pred = metric(valid_salaries[idx], best_predictions[idx])
if mae_pred < mae_best_pred:
isbetter = "DA"
else:
isbetter = "nope"
ppara = "Curr MAE: %0.2f Best MAE: %0.2f %s\n" % (
mae_pred, mae_best_pred, isbetter)
print ppara
param += ppara
if submission:
dio.save_prediction(name, predictions, type_n=type_v)
dio.write_submission(name + ".csv", predictions=predictions)
else:
dio.compare_valid_pred(valid_salaries, predictions)
metric = dio.error_metric
mae = metric(valid_salaries, predictions)
print "MAE validation: ", mae
dio.save_model(ExtraTreesRegressor(n_estimators=n_trees,
min_samples_split=min_samples_split,
random_state=3465343),
name,
mae,
parameters=param)
dio.save_prediction(name, predictions, type_n=type_v)
#oob_predictions = classifier.oob_prediction_
#mae_oob = mean_absolute_error(salaries, oob_predictions)
#print "MAE OOB: ", mae_oob
#classifier1 = ExtraTreesRegressor(n_estimators=n_trees,
#verbose=1,
def tfidf_cloud(n_trees):
dio = DataIO("/data/Settings_cloud.json")
submission = False
min_samples_split = 2
param = """Normal count vector with max 200. New submission which is repeatable.
and nicer
count_vector_titles = TfidfVectorizer(
read_column(train_filename, column_name),
max_features=200, norm='l1', smooth_idf=True, sublinear_tf=False, use_idf=True)
"""
if submission:
type_n = "train_full"
type_v = "valid_full"
else:
type_n = "train"
type_v = "valid"
#features = dio.join_features("%s_" + type_n + "_tfidf_matrix_max_f_200",
#["Title", "FullDescription", "LocationRaw"],
#extra_features)
#validation_features = dio.join_features("%s_" + type_v + "_tfidf_matrix_max_f_200",
#["Title", "FullDescription", "LocationRaw"],
#extra_valid_features)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_features", features)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_valid_features", validation_features)
def load(filename):
return joblib.load(path_join("/data", filename))
features = load("train_200f_noNorm_categoryTimeType_tfidfl1_features_jl")
validation_features = load("train_200f_noNorm_categoryTimeType_tfidfl1_valid_features_jl")
print "features", features.shape
print "valid features", validation_features.shape
#salaries = dio.get_salaries(type_n, log=True)
#if not submission:
#valid_salaries = dio.get_salaries(type_v, log=True)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_salaries", salaries)
#np.save("train_200f_noNorm_categoryTimeType_tfidfl2_valid_salaries", valid_salaries)
#joblib.dump(salaries, "train_200f_noNorm_categoryTimeType_tfidfl2_salaries_jl", compress=5)
#joblib.dump(valid_salaries, "train_200f_noNorm_categoryTimeType_tfidfl2_valid_salaries_jl", compress=5)
#TODO: valid salaries so narobe dumpane
salaries = load("train_200f_noNorm_categoryTimeType_tfidfl2_salaries_jl")
valid_salaries = load("train_200f_noNorm_categoryTimeType_tfidfl2_valid_salaries_jl")
dio.is_log = True
print salaries.shape
name = "ExtraTree_min_sample%d_%dtrees_200f_noNorm_categoryTimeType_tfidfl1_new_log" % (min_samples_split, n_trees)
print name
#dio.save_prediction("testni", np.array([1,2,3]), type_n="testno")
classifier = ExtraTreesRegressor(n_estimators=n_trees,
verbose=2,
n_jobs=4, # 2 jobs on submission / 4 on valid test
oob_score=False,
min_samples_split=min_samples_split,
random_state=3465343)
#dio.save_model(classifier, "testni_model", 99.)
classifier.fit(features, salaries)
predictions = classifier.predict(validation_features)
if submission:
dio.save_prediction(name, predictions, type_n=type_v)
dio.write_submission(name + ".csv", predictions=predictions)
else:
dio.compare_valid_pred(valid_salaries, predictions)
metric = dio.error_metric
mae = metric(valid_salaries, predictions)
print "MAE validation: ", mae
dio.save_model(classifier, name, mae)
dio.save_prediction(name, predictions, type_n=type_v)
