Python get_filename Exemples

Exemple #1

import os, sys
ROOT = os.path.abspath(
    os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))
sys.path.append(ROOT)
APP_ROOT = os.path.join(ROOT, "avito")
OUTPUT_DIR = os.path.join(APP_ROOT, "output")
SUB_DIR = os.path.join(APP_ROOT, "submission")
PRED_TRAIN = os.path.join(OUTPUT_DIR, "pred_train.csv")
PRED_TEST = os.path.join(OUTPUT_DIR, "pred_test.csv")
GAZOU_TRAIN = os.path.join(OUTPUT_DIR, "image_train.csv")
GAZOU_TEST = os.path.join(OUTPUT_DIR, "image_test.csv")
PSEUDO_PRED_99 = os.path.join(SUB_DIR, "submission99p.csv")
from avito.common import filename_getter
DESC_TF_COLS, DESC_TF_TRAIN, DESC_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_desc", "tf")
TITLE_TF_COLS, TITLE_TF_TRAIN, TITLE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_title", "tf")
DENSE_TF_COLS, DENSE_TF_TRAIN, DENSE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_title_desc", "tf")
TITLE_CNT_COLS, TITLE_CNT_TRAIN, TITLE_CNT_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_title", "cnt")
DENSE_CNT15_COLS, DENSE_CNT15_TRAIN, DENSE_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "stem_cnt15_dense", "cnt")
DESC_CNT15_COLS, DESC_CNT15_TRAIN, DESC_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "stem_cnt15_desc", "cnt")
TITLE_CNT15_COLS, TITLE_CNT15_TRAIN, TITLE_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "stem_cnt15_title", "cnt")
BEST_SUB = os.path.join(SUB_DIR, "submission.csv")
import pandas as pd
import numpy as np
import scipy.sparse

Exemple #2

import os, sys
import pandas as pd

ROOT = os.path.abspath(
    os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))
sys.path.append(ROOT)
APP_ROOT = os.path.join(ROOT, "avito")
OUTPUT_DIR = os.path.join(APP_ROOT, "output")
from avito.common import filename_getter

DESC_TF_COLS, DESC_TF_TRAIN, DESC_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "desc", "tf")
TITLE_TF_COLS, TITLE_TF_TRAIN, TITLE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "title", "tf")
TITLE_CNT_COLS, TITLE_CNT_TRAIN, TITLE_CNT_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "title", "cnt")
DENSE_TF_COLS, DENSE_TF_TRAIN, DENSE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "title_desc", "tf")
STEM_DESC_TF_COLS, STEM_DESC_TF_TRAIN, STEM_DESC_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_desc", "tf")
STEM_TITLE_TF_COLS, STEM_TITLE_TF_TRAIN, STEM_TITLE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_title", "tf")
STEM_TITLE_CNT_COLS, STEM_TITLE_CNT_TRAIN, STEM_TITLE_CNT_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_title", "cnt")
STEM_DENSE_TF_COLS, STEM_DENSE_TF_TRAIN, STEM_DENSE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "stem_title_desc", "tf")


def get_predict_col():
    # item_id, user_id, title, description, image, deal_probability
    pred_col = [

Exemple #3

Fichier : vanilla.py Projet : backonhighway/kaggle_avito

import os, sys
ROOT = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))
sys.path.append(ROOT)
APP_ROOT = os.path.join(ROOT, "avito")
OUTPUT_DIR = os.path.join(APP_ROOT, "output")
SUB_DIR = os.path.join(APP_ROOT, "submission")
PRED_TRAIN = os.path.join(OUTPUT_DIR, "pred_train.csv")
PRED_TEST = os.path.join(OUTPUT_DIR, "pred_test.csv")
GAZOU_TRAIN = os.path.join(OUTPUT_DIR, "image_train.csv")
GAZOU_TEST = os.path.join(OUTPUT_DIR, "image_test.csv")
OUTPUT_PRED = os.path.join(SUB_DIR, "submission.csv")
OUTPUT_CV_PRED = os.path.join(SUB_DIR, "cv_pred.csv")
from avito.common import filename_getter
DENSE_CNT15_COLS, DENSE_CNT15_TRAIN, DENSE_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "vanilla_cnt15_dense", "cnt")
DESC_CNT15_COLS, DESC_CNT15_TRAIN, DESC_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "vanilla_cnt15_desc", "cnt")
TITLE_CNT15_COLS, TITLE_CNT15_TRAIN, TITLE_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "vanilla_cnt15_title", "cnt")
VDESC_TF_COLS, VDESC_TF_TRAIN, VDESC_TF_TEST = filename_getter.get_filename(OUTPUT_DIR, "desc", "tf")
VTITLE_TF_COLS, VTITLE_TF_TRAIN, VTITLE_TF_TEST = filename_getter.get_filename(OUTPUT_DIR, "title", "tf")
VDENSE_TF_COLS, VDENSE_TF_TRAIN, VDENSE_TF_TEST = filename_getter.get_filename(OUTPUT_DIR, "title_desc", "tf")
import pandas as pd
import numpy as np
import scipy.sparse
import gc
from sklearn import model_selection
from dask import dataframe as dd
from avito.common import csv_loader, column_selector, pocket_lgb, pocket_timer, pocket_logger, holdout_validator
from avito.fe import additional_fe

Exemple #4

Fichier : make_kernel_bow.py Projet : backonhighway/kaggle_avito

ROOT = os.path.abspath(
    os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))
sys.path.append(ROOT)
APP_ROOT = os.path.join(ROOT, "avito")
INPUT_DIR = os.path.join(APP_ROOT, "input")
OUTPUT_DIR = os.path.join(APP_ROOT, "output")
ORG_TRAIN = os.path.join(INPUT_DIR, "train.csv")
ORG_TEST = os.path.join(INPUT_DIR, "test.csv")
STEM_TRAIN = os.path.join(OUTPUT_DIR, "stem_train.csv")
STEM_TEST = os.path.join(OUTPUT_DIR, "stem_test.csv")
PRED_TRAIN = os.path.join(OUTPUT_DIR, "pred_train.csv")
PRED_TEST = os.path.join(OUTPUT_DIR, "pred_test.csv")
from avito.common import filename_getter

DESC_TF_COLS, DESC_TF_TRAIN, DESC_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "ker_desc", "tf")
TITLE_TF_COLS, TITLE_TF_TRAIN, TITLE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "ker_title", "tf")
TITLE_CNT_COLS, TITLE_CNT_TRAIN, TITLE_CNT_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "ker_title", "cnt")
DENSE_TF_COLS, DENSE_TF_TRAIN, DENSE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "ker_title_desc", "tf")

import numpy as np
import pandas as pd
import scipy.sparse
import gc
from avito.common import pocket_timer, pocket_logger, column_selector
from avito.fe import kernel_bow

logger = pocket_logger.get_my_logger()

Exemple #5

Fichier : make_cnt_stem.py Projet : backonhighway/kaggle_avito

import os, sys
ROOT = os.path.abspath(
    os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))
sys.path.append(ROOT)
APP_ROOT = os.path.join(ROOT, "avito")
INPUT_DIR = os.path.join(APP_ROOT, "input")
OUTPUT_DIR = os.path.join(APP_ROOT, "output")
ORG_TRAIN = os.path.join(INPUT_DIR, "train.csv")
ORG_TEST = os.path.join(INPUT_DIR, "test.csv")
STEM_TRAIN = os.path.join(OUTPUT_DIR, "stem_train.csv")
STEM_TEST = os.path.join(OUTPUT_DIR, "stem_test.csv")
PRED_TRAIN = os.path.join(OUTPUT_DIR, "pred_train.csv")
PRED_TEST = os.path.join(OUTPUT_DIR, "pred_test.csv")
from avito.common import filename_getter
DENSE_CNT15_COLS, DENSE_CNT15_TRAIN, DENSE_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "stem_cnt15_dense", "cnt")
DESC_CNT15_COLS, DESC_CNT15_TRAIN, DESC_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "stem_cnt15_desc", "cnt")
TITLE_CNT15_COLS, TITLE_CNT15_TRAIN, TITLE_CNT15_TEST = \
    filename_getter.get_filename(OUTPUT_DIR, "stem_cnt15_title", "cnt")

import numpy as np
import pandas as pd
import scipy.sparse
import gc
from avito.common import pocket_timer, pocket_logger, column_selector
from avito.fe import lda_cnt_bow

logger = pocket_logger.get_my_logger()
timer = pocket_timer.GoldenTimer(logger)
#dtypes = csv_loader.get_featured_dtypes()

Exemple #6

Fichier : sparsed_submit.py Projet : backonhighway/kaggle_avito

import os, sys
ROOT = os.path.abspath(
    os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))
sys.path.append(ROOT)
APP_ROOT = os.path.join(ROOT, "avito")
OUTPUT_DIR = os.path.join(APP_ROOT, "output")
SUBMISSION = os.path.join(APP_ROOT, "submission")
PRED_TRAIN = os.path.join(OUTPUT_DIR, "pred_train.csv")
PRED_TEST = os.path.join(OUTPUT_DIR, "pred_test.csv")
OUTPUT_PRED = os.path.join(SUBMISSION, "submission.csv")
MODEL_FILE = os.path.join(SUBMISSION, "pred_model.txt")
from avito.common import filename_getter
DESC_TF_COLS, DESC_TF_TRAIN, DESC_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "desc", "tf")
TITLE_TF_COLS, TITLE_TF_TRAIN, TITLE_TF_TEST = filename_getter.get_filename(
    OUTPUT_DIR, "title", "tf")

import pandas as pd
import numpy as np
import scipy.sparse
import gc
from sklearn import model_selection
from dask import dataframe as dd
from avito.common import csv_loader, column_selector, pocket_lgb, pocket_timer, pocket_logger

logger = pocket_logger.get_my_logger()
timer = pocket_timer.GoldenTimer(logger)
dtypes = csv_loader.get_featured_dtypes()
predict_col = column_selector.get_predict_col()
lgb_col = column_selector.get_pred_tf_col()