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submission_extra_new_cloud.py
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submission_extra_new_cloud.py
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from data_io import DataIO
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.cross_validation import cross_val_score
from os.path import join as path_join
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import joblib
import cloud
import os
tfidf_columns = ["Title", "FullDescription", "LocationRaw"]
dio = DataIO("Settings.json")
vectorizer = TfidfVectorizer(
max_features=200,
norm='l1',
smooth_idf=True,
sublinear_tf=False,
use_idf=True
)
short_id = "tfidf_200f_l1"
type_n = "train"
type_v = "valid"
dio.make_counts(vectorizer, short_id, tfidf_columns, "train", "valid")
columns = ["Category", "ContractTime", "ContractType"]
le_features = dio.get_le_features(columns, "train_full")
extra_features = dio.get_features(columns, type_n, le_features)
extra_valid_features = dio.get_features(columns, type_v, le_features)
features = dio.join_features("%s_" + type_n + "_" + short_id + "_matrix",
tfidf_columns,
extra_features)
validation_features = dio.join_features("%s_" + type_v + "_" + short_id + "_matrix",
tfidf_columns,
extra_valid_features)
print features.shape
print validation_features.shape
run = raw_input("OK (Y/N)?")
print run
if run != "Y":
os.exit()
files = joblib.dump(features, "train_200f_noNorm_categoryTimeType_tfidfl1_features_jl", compress=9)
files1 = joblib.dump(validation_features, "train_200f_noNorm_categoryTimeType_tfidfl1_valid_features_jl", compress=9)
files.extend(files1)
print files
run = raw_input("OK (Y/N)?")
print run
if run != "Y":
os.exit()
for file_name in files:
cloud.volume.sync(file_name, "my-vol-job:")
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)
##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=4,
#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)
#print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
#mae_cv = "%0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
#dio.save_model(classifier, name, mae, mae_cv=mae_cv, parameters=param)
#jid = cloud.call(tfidf_cloud, 10, _type='f2', _cores=4, _env='sklearnPrecise', _vol="my-vol-job", _label="10 trees l1")
#jid = cloud.call(tfidf_cloud, 20, _type='f2', _cores=4, _env='sklearnPrecise', _vol="my-vol-job")
#print jid
#jid = cloud.call(tfidf_cloud, 30, _type='f2', _cores=4, _env='sklearnPrecise', _vol="my-vol-job")
#print jid
#jid = cloud.call(tfidf_cloud, 40, _type='f2', _cores=4, _env='sklearnPrecise', _vol="my-vol-job")
#print jid