from data_io import (

get_paths,

read_column,

load_model,

join_features,

label_encode_column_fit,

label_encode_column_transform,

load_predictions,

fit_predict,

)

from os.path import join as path_join

#import joblib

import numpy as np

#from sklearn.ensemble import RandomForestRegressor

from sklearn.ensemble import ExtraTreesRegressor

from sklearn.ensemble import GradientBoostingRegressor

#from sklearn.linear_model import SGDRegressor

from sklearn.linear_model import LinearRegression, RidgeCV, Ridge

from sklearn.metrics import mean_absolute_error

from sklearn.cross_validation import cross_val_score

from itertools import combinations, chain

import joblib

def log_mean_absolute_error(y_true, y_pred):

return mean_absolute_error(np.exp(y_true), np.exp(y_pred))

paths = get_paths("Settings.json")

data_dir = paths["data_path"]

cache_dir = path_join(data_dir, "tmp")

prediction_dir = path_join(data_dir, "predictions")

#le_category, category_train = label_encode_column_fit("Category")

#category_valid = label_encode_column_transform(le_category, "Category")

#le_contractTime, contractTime_train = label_encode_column_fit("ContractTime")

#contractTime_valid = label_encode_column_transform(le_contractTime, "ContractTime")

#le_contractType, contractType_train = label_encode_column_fit("ContractType")

#contractType_valid = label_encode_column_transform(le_contractType, "ContractType")

#features = join_features("%s_train_count_vector_matrix_max_f_200", #train_tfidf_matrix_max_f_200

##features = join_features("%s_train_tfidf_matrix_max_f_200",

#["Title", "FullDescription", "LocationRaw"],

#data_dir,

#[contractTime_train, contractType_train, category_train])

#validation_features = join_features("%s_valid_count_vector_matrix_max_f_200",#valid_tfidf_matrix_max_f_200

##validation_features = join_features("%s_valid_tfidf_matrix_max_f_200",

#["Title", "FullDescription", "LocationRaw"],

#data_dir,

#[contractTime_valid, contractType_valid, category_valid])

#print "features", features.shape

#print "valid features", validation_features.shape

#salaries = np.array(list(read_column(paths["train_data_path"], "SalaryNormalized"))).astype(np.float64)

valid_salaries = np.array(list(read_column(paths["valid_data_path"], "SalaryNormalized"))).astype(np.float64)

#salaries = np.log(salaries)

#print salaries.shape

#valid_salaries = np.log(valid_salaries)

print valid_salaries.shape

model1 = "ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log"

model2 = "vowpall"

model3 = "Random_forest_min_sample2_20trees_200f_noNorm_categoryTimeType_log"

model4 = "ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_log"

model_names = [model1, model2, model3, model4]

#fit_predict(model2)

#fit_predict(model1)

#fit_predict(model3)

all_model_predictions = []

for model_name in model_names:

model_predictions = load_predictions(model_name)

#print model_predictions[0]

if not model_name.endswith("log") and not model_name.startswith("vowpall"):

model_predictions = np.log(model_predictions)

#if model_name.startswith("vowpall"):

#model_predictions = np.log(model_predictions)

#print model_predictions[0]

print "%s\nMAE: %f\n" % (model_name, mean_absolute_error(valid_salaries, np.exp(model_predictions)))

all_model_predictions.append(model_predictions)

predictions = np.vstack(all_model_predictions).T

predictions = np.exp(predictions)

#predictions = np.random.randint(0,5, size=(10,3))

print predictions.shape

print predictions[1:10, :]

indexes = range(0, len(model_names))

def print_index(index):

names = map(lambda x: model_names[x], index)

return "\n ".join(names)

best_average = (10000,(0))

best_classifier = best_average

for num in range(2, len(model_names) + 1):

for average_index in combinations(indexes, num):

print average_index

print print_index(average_index)

my_prediction = predictions[:, average_index]

#print my_prediction[1:10,:]

mean_pred = my_prediction.mean(axis=1)

mae = mean_absolute_error(mean_pred, valid_salaries)

print "MAE:", mae

if best_average[0] > mae:

best_average = (mae, average_index)

classifier = LinearRegression()

#classifier = RidgeCV(loss_func=mean_absolute_error)

#classifier.fit(my_prediction, valid_salaries)

#alpha = classifier.alpha_

#classifier = Ridge(alpha=alpha)

scores = cross_val_score(classifier, my_prediction, valid_salaries, cv=5, score_func=mean_absolute_error, verbose=0, n_jobs=-1)

print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)

if best_classifier[0] > scores.mean():

best_classifier = (scores.mean(), average_index)

print "best average:", best_average[0], print_index(best_average[1])

print "best classifier:", best_classifier[0], print_index(best_classifier[1])

#

#(0, 1)

#ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log vowpall

#MAE: 5925.75752661

#(0, 2)

#ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log Random_forest_min_sample2_20trees_200f_noNorm_categoryTimeType_log

#MAE: 6373.82572206

#(1, 2)

#vowpall Random_forest_min_sample2_20trees_200f_noNorm_categoryTimeType_log

#MAE: 6157.26021497

#(0, 1, 2)

#ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log vowpall

#MAE: 5889.15272898

#Linear regression (0,1,2):

#Accuracy: 5834.14 (+/- 29.86)

#Ridge Linear je isto

#best average: 5766.06198285 ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log, vowpall, ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_log

#best classifier: 5778.35931012 vowpall, ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_log

#best average: 5766.06198285 ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log, vowpall, ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_log

#best classifier: 5778.35931012 vowpall, ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_log

#best average: 5694.24220595 ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log

#vowpall

#vowpall_loc5

#ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType

#ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_tfidf_log

#ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_tfidf1_log

#best classifier: 5691.47252485 ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log

#vowpall

#Random_forest_min_sample2_20trees_200f_noNorm_categoryTimeType_log

#Random_forest_min_sample2_40trees_200f_noNorm_categoryTimeType_log

#vowpall_loc5

#ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType

#ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_tfidf_log

#ExtraTree_min_sample2_40trees_200f_noNorm_categoryTimeType_tfidf1_log