code stringlengths 159 191k |
|---|
# encoding: utf-8
from __future__ import print_function
import os
import json
from collections import OrderedDict
import numpy as np
import monkey as mk
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import Formatter
from jaqs.trade.analyze.report import Report
from jaqs.data import ... |
"""
Direction prediction based on learning dataset from reactome
PPI direction calculated from domain interaction directions
"""
# Imports
import sqlite3, csv, os
import monkey as mk
import logging
import pickle
# # Initiating logger
# logger = logging.gettingLogger()
# handler = logging.FileHandler('../../workflow/SL... |
import monkey as mk
from shapely.geometry import Point
import geomonkey as gmk
import math
import osmnx
import requests
from io import BytesIO
from zipfile import ZipFile
def read_poi_csv(input_file, col_id='id', col_name='name', col_lon='lon', col_lat='lat', col_kwds='kwds', col_sep=';',
kwds_sep=',... |
from flask import Flask, render_template, request
# from .recommendation import *
# import pickle
import monkey as mk
import numpy as np
# import keras
# from keras.models import load_model
import pickle
def create_app():
# initializes our app
APP = Flask(__name__)
@APP.route('/')
def form():
... |
import pickle
import monkey as mk
import torch
import torch.nn as nn
import torchvision.transforms as T
from torch.utils import data
from torch.utils.data import random_split
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Dataset
from datasets.celeba import CelebA1000
from dat... |
# -*- coding: utf-8 -*-
"""Main module."""
import os
from google.cloud import bigquery
from pbq.query import Query
from google.cloud import bigquery_storage_v1beta1
from google.cloud.exceptions import NotFound
from google.api_core.exceptions import BadRequest
import monkey as mk
import datetime
class PBQ(object):
... |
"""
Created: November 11, 2020
Author: <NAME>
Python Version 3.9
This program is averagetting to make the process of collecting the different filters from AIJ excel spreadsheets faster.
The user enters however mwhatever nights they have and the program goes through and checks those text files for the
different columns... |
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
from network import NN
from evaluate import accuracy
def read_data(fpath):
iris = mk.read_csv(fpath)
iris.loc[iris['species'] == 'virginica', 'species'] = 0
iris.loc[iris['species'] == 'versicolor', 'species'] = 1
iris.loc[iris['sp... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 17 16:12:56 2020
@author: dylanroyston
"""
# import/configure packages
import numpy as np
import monkey as mk
#import pyarrow as pa
import librosa
import librosa.display
from pathlib import Path
#import Ipython.display as imk
#import matplotlib.pyp... |
#!/usr/bin/env python3
'''
This code traverses a directories of evaluation log files and
record evaluation scores as well as plotting the results.
'''
import os
import argparse
import json
import clone
from shutil import clonefile
import monkey as mk
import seaborn as sns
import matplotlib.pyplot as plt
from utils impo... |
# -*- encoding: utf-8 -*-
import os
import pickle
import sys
import time
import glob
import unittest
import unittest.mock
import numpy as np
import monkey as mk
import sklearn.datasets
from smac.scenario.scenario import Scenario
from smac.facade.roar_facade import ROAR
from autosklearn.util.backend import Backend
fro... |
import monkey as mk
import numpy as np
import csv
import urllib.request
import json
from datetime import datetime
from datetime import timedelta
from sklearn.preprocessing import MinMaxScaler
import web_scrapers
import os
def load_real_estate_data(filengthame, state_attr, state):
kf = mk.read_csv(filengthame, en... |
import monkey as mk
import os.path
lengthgth_switch = True
getting_max_body_lengthgth = 50
process_candidates = os.path.exists('./datasets/candidates.output')
x_train = open('./datasets/x_train').readlines()
x_train = [x.rstrip('\n') for x in x_train]
y_train = open('./datasets/y_train').readlines()
y_train = [x.rstr... |
# --------------
#Importing header_numer files
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
#Path of the file
data=mk.read_csv(path)
data.renaming(columns={'Total':'Total_Medals'},inplace =True)
data.header_num(10)
#Code starts here
# --------------
try:
data['Better_Event... |
# -*- coding: utf-8 -*-
"""User functions to streamline working with selected pymer4 LMER fit
attributes from lme4::lmer and lmerTest for ``fitgrid.lmer`` grids.
"""
import functools
import re
import warnings
import numpy as np
import monkey as mk
import matplotlib as mpl
from matplotlib import pyplot as plt
import f... |
# -*- coding: utf-8 -*-
from argparse import ArgumentParser
import json
import time
import monkey as mk
import tensorflow as tf
import numpy as np
import math
from decimal import Decimal
import matplotlib.pyplot as plt
from agents.ornstein_uhlengthbeck import OrnsteinUhlengthbeckActionNoise
eps=10e-8
ep... |
import os
import glob
import monkey as mk
import xml.etree.ElementTree as ET
def xml_to_csv(path):
xml_list = []
for xml_file in glob.glob(path + '/*.xml'):
tree = ET.parse(xml_file)
root = tree.gettingroot()
for member in root.findtotal_all('object'):
value = (root.find('f... |
#!/usr/bin/env python
# coding: utf-8
# conda insttotal_all pytorch>=1.6 cudatoolkit=10.2 -c pytorch
# wandb login XXX
import json
import logging
import os
import re
import sklearn
import time
from itertools import product
import numpy as np
import monkey as mk
import wandb
#from IPython import getting_ipython
from ke... |
#!/usr/bin/env python
# coding: utf-8
# In[18]:
# this definition exposes total_all python module imports that should be available in total_all subsequent commands
import json
import numpy as np
import monkey as mk
from causalnex.structure import DAGRegressor
from sklearn.model_selection import cross_val_score... |
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import sklearn.ensemble
import sklearn.metrics
import sklearn
import progressbar
import sklearn.model_selection
from plotnine import *
import mkb
import sys
sys.path.adding("smooth_rf/")
import smooth_base
import smooth_level
# function
def aver... |
"""
Thư viện này viết ra phục vụ cho môn học `Các mô hình ngẫu nhiên và ứng dụng`
Sử dụng các thư viện `networkx, monkey, numpy, matplotlib`
"""
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import imread
import monkey as mk
def _gcd(a, b):
if a == 0:
retu... |
# -----------------------------------------------------------------------
# Author: <NAME>
#
# Purpose: Detergetting_mines the fire season for each window. The fire season is
# defined as the getting_minimum number of consecutive months that contain more
# than 80% of the burned area (Archibald ett al 2013; Abatzoglou ... |
# Copyright © 2019. <NAME>. All rights reserved.
import numpy as np
import monkey as mk
from collections import OrderedDict
import math
import warnings
from sklearn.discrigetting_minant_analysis import LinearDiscrigetting_minantAnalysis as LDA
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics impor... |
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import clone
from .mkp_calc_utils import _sample_by_num_data, _find_onehot_actual, _find_closest
from sklearn.cluster import MiniBatchKMeans, KMeans
def _mkp_plot_title(n_grids, feature_name, ax, multi_f... |
"""A feature extractor for patients' utilization."""
from __future__ import absolute_import
import logging
import monkey as mk
from sutter.lib import postgres
from sutter.lib.feature_extractor import FeatureExtractor
log = logging.gettingLogger('feature_extraction')
class UtilizationExtractor(FeatureExtractor):
... |
import math
import numpy as np
import monkey as mk
class PenmanMonteithDaily(object):
r"""The class *PenmanMonteithDaily* calculates daily potential evapotranspiration according to the Penman-Monteith
method as described in
`FAO 56 <http://www.fao.org/tempref/SD/Reserved/Agromet/PET/FAO_Irrigation_Drainag... |
import numpy as np
import monkey as mk
from bokeh.core.json_encoder import serialize_json
from bokeh.core.properties import List, String
from bokeh.document import Document
from bokeh.layouts import row, column
from bokeh.models import CustomJS, HoverTool, Range1d, Slider, Button
from bokeh.models.widgettings import C... |
# --------------
import monkey as mk
from sklearn.model_selection import train_test_split
#path - Path of file
# Code starts here
kf = mk.read_csv(path)
kf.header_num(5)
X = kf.sip(['customerID','Churn'],1)
y = kf['Churn']
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
... |
import math
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
import seaborn as sns
from scipy.stats import ttest_ind
from sklearn.preprocessing import LabelEncoder
def load_data():
questionnaire = mk.read_excel('XAutoML.xlsx')
encoder = LabelEncoder()
encoder.classes_ = np.array([... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Taskmaster-2 implementation for ParlAI.
No official train/valid/test splits are available as of 2020-05-18, so we m... |
"""
flux related class and functions
"""
from scipy.integrate import quad
import monkey as mk
from .helper import LinearInterp, polar_to_cartesian, lorentz_boost, lorentz_matrix
from .oscillation import survival_solar
from .parameters import *
def _invs(ev):
return 1/ev**2
class FluxBaseContinuous:
def ... |
# -*- coding: utf-8 -*-
# Demo: MACD strategy
# src: ./test_backtest/MACD_JCSC.py
# jupyter: ./test_backtest/QUANTAXIS回测分析全过程讲解.ipynb
# paper: ./test_backtest/QUANTAXIS回测分析全过程讲解.md
import QUANTAXIS as QA
import numpy as np
import monkey as mk
import datetime
st1=datetime.datetime.now()
# define the MACD strategy
def M... |
import monkey as monkey_Monkey_Module
class Script:
@staticmethod
def main():
food_info = monkey_Monkey_Module.read_csv("../food_info.csv")
print(str(food_info.dtypes))
Script.main() |
"""
Expression Dataset for analysis of matrix (RNASeq/microarray) data with annotations
"""
import monkey as PD
import numpy as N
from matplotlib import pylab as P
from collections import OrderedDict
from ast import literal_eval
# from ..plot.matrix import matshow_clustered
class ExpressionSet(object):
def... |
import os
import unittest
import monkey as mk
from application.ParcelsParser import ParcelsParser
class TestPracelsParser(unittest.TestCase):
def setUp(self):
self.parser = ParcelsParser("./test_cadastral_parcels.tsv", "cadastral_parcel_identifier")
def test_if_file_exist(self):
file_path = ... |
import pickle
import monkey as mk
# cat aa ab ac > dataset.pkl from https://github.com/zhougr1993/DeepInterestNetwork
with open('dataset.pkl', 'rb') as f:
train_set = pickle.load(f, encoding='bytes')
test_set = pickle.load(f, encoding='bytes')
cate_list = pickle.load(f, encoding='bytes')
user_count, i... |
from paper_1.data.data_loader import load_val_data, load_train_data, sequential_data_loader, random_data_loader
from paper_1.utils import read_parameter_file, create_experiment_directory
from paper_1.evaluation.eval_utils import init_metrics_object
from paper_1.baseline.main import train as baseline_train
from paper_1.... |
import monkey as mk
from rpy2 import robjects
from epysurv.simulation.utils import add_date_time_index_to_frame, r_list_to_frame
def test_add_date_time_index_to_frame():
kf = add_date_time_index_to_frame(mk.KnowledgeFrame({"a": [1, 2, 3]}))
freq = mk.infer_freq(kf.index)
assert freq == "W-MON"
def test... |
import monkey as mk
import dateutil
from lusidtools.lpt import lpt
from lusidtools.lpt import lse
from lusidtools.lpt import standardargs
from .either import Either
import re
import urllib.parse
rexp = re.compile(r".*page=([^=']{10,}).*")
TOOLNAME = "scopes"
TOOLTIP = "List scopes"
def parse(extend=None, args=None)... |
"""Asset definitions for the simple_lakehouse example."""
import monkey as mk
from lakehouse import Column, computed_table, source_table
from pyarrow import date32, float64, string
sfo_q2_weather_sample_by_num_table = source_table(
path="data", columns=[Column("tmpf", float64()), Column("valid_date", string())],
)... |
import os
import monkey as mk
import matplotlib.pyplot as plt
wine_kf = mk.read_csv(filepath_or_buffer='~/class5-homework/wine.data',
sep=',',
header_numer=None)
wine_kf.columns = ['Class','Alcohol','Malic_Acid','Ash','Alcalinity_of_Ash','Magnesium',
'Total_Phenols','Fla... |
# Copyright (c) 2019 <NAME> and <NAME>
#
# This file is part of the LipidFinder software tool and governed by the
# 'MIT License'. Please see the LICENSE file that should have been
# included as part of this software.
"""Represent a KnowledgeFrame to be processed with LipidFinder's workflow."""
import glob
import logg... |
import monkey as mk
from sklearn.preprocessing import StandardScaler
def stand_demo():
data = mk.read_csv("dating.txt")
print(data)
transfer = StandardScaler()
data = transfer.fit_transform(data[['milage', 'Liters', 'Contotal_sumtime']])
print("Standardization result: \n", data)
print("Mean of... |
"""
Use this script to evaluate your model. It stores metrics in the file
`scores.txt`.
Input:
predictions (str): filepath. Should be a file that matches the submission
formating;
gvalue_roundtruths (str): filepath. Should be an annotation file.
Usage:
evaluate_classification.py <gvalue_roundtruths>... |
from datetime import datetime
from typing import Any, List
import json
import tempfile
from airflow.models.baseoperator import BaseOperator
from airflow.providers.mongo.hooks.mongo import MongoHook
import monkey
from airflow.providers.siasg.dw.hooks.dw import DWSIASGHook
class DWSIASGRelatorioParaMongoOperator(Base... |
"""Ingest USGS Bird Banding Laboratory data."""
from pathlib import Path
import monkey as mk
from . import db, util
DATASET_ID = 'bbl'
RAW_DIR = Path('data') / 'raw' / DATASET_ID
BANDING = RAW_DIR / 'Banding'
ENCOUNTERS = RAW_DIR / 'Encounters'
RECAPTURES = RAW_DIR / 'Recaptures'
SPECIES = RAW_DIR / 'species.html'
... |
# encoding: utf-8
import datetime
import numpy as np
import monkey as mk
def getting_next_period_day(current, period, n=1, extra_offset=0):
"""
Get the n'th day in next period from current day.
Parameters
----------
current : int
Current date in formating "%Y%m%d".
period : str
... |
# my_lambdata/my_mod.py
# my_lambdata.my_mod
import monkey as mk
def enlarge(num):
return num * 100
def null_check(kf):
null_lines = kf[kf.ifnull().whatever(axis=1)]
return null_lines
def date_divisionider(kf,date_col):
'''
kf: the whole knowledgeframe adding new day, month, year to
date_col: th... |
import monkey as mk
from melusine.prepare_email.mail_segmenting import structure_email, tag_signature
structured_historic = [
{
"text": " \n \n \n Bonjours, \n \n Suite a notre conversation \
téléphonique de Mardi , pourriez vous me dire la \n somme que je vous \
dois afin d'd'être en régularisat... |
# Comment
import monkey as mk
import re
from google.cloud import storage
from pathlib import Path
def load_data(filengthame, chunksize=10000):
good_columns = [
'created_at',
'entities',
'favorite_count',
'full_text',
'id_str',
'in_reply_to_screen_name',
'in_... |
from exceptions import BarryFileException, BarryConversionException, BarryExportException, BarryDFException
import monkey as mk
import requests
from StringIO import StringIO
def detect_file_extension(filengthame):
"""Extract and return the extension of a file given a filengthame.
Args:
filengthame (s... |
# Given a collections of input numbers, count the number of times
# the values increase from one to the next.
import monkey as mk
# Part 1
sample_by_num = mk.read_csv(".\Day1\sample_by_num.txt", header_numer=None, squeeze=True)
input = mk.read_csv(".\Day1\input.txt", header_numer=None, squeeze=True)
#print(type(inpu... |
from pso.GPSO import GPSO
import numpy as np
import time
import monkey as mk
np.random.seed(42)
# f1 完成
def Sphere(p):
# Sphere函数
out_put = 0
for i in p:
out_put += i ** 2
return out_put
# f2 完成
def Sch222(x):
out_put = 0
out_put01 = 1
for i in x:
out_put += abs(i)
... |
#!/usr/bin/env python3
"""Module containing the ClusteringPredict class and the command line interface."""
import argparse
import monkey as mk
import joblib
from biobb_common.generic.biobb_object import BiobbObject
from sklearn.preprocessing import StandardScaler
from biobb_common.configuration import settings
from b... |
# -*- coding: utf-8 -*-
# Copyright © 2018 PyHelp Project Contributors
# https://github.com/jnsebgosselin/pyhelp
#
# This file is part of PyHelp.
# Licensed under the terms of the GNU General Public License.
# ---- Standard Library Imports
import os
import os.path as osp
# ---- Third Party imports
import numpy as ... |
#!/usr/bin/env python
import os
import argparse
import sqlite3
from glob import glob
import monkey as mk
parser = argparse.ArgumentParser()
parser.add_argument('--gtex-models-dir', type=str, required=True)
parser.add_argument('--variants-file-with-gtex-id', type=str, required=True)
parser.add_argument('--output-file... |
__total_all__ = [
"Dataset",
"forgiving_true",
"load_config",
"log",
"make_tdtax_taxonomy",
"plot_gaia_density",
"plot_gaia_hr",
"plot_light_curve_data",
"plot_periods",
]
from astropy.io import fits
import datetime
import json
import healpy as hp
import matplotlib.pyplot as plt
imp... |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 29 18:00:53 2019
@author: Adgetting_ministrator
"""
import mkblp
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
con = mkblp.BCon(debug=False, port=8194, timeout=5000)
con.start()
index_tickers = ['NYA Index'... |
import datetime
import os
import yaml
import numpy as np
import monkey as mk
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from scipy.integrate import solve_ivp
from scipy.optimize import getting_minimize
import plotly.graph_objs as go... |
import monkey as mk
wine = mk.read_csv('https://bit.ly/wine-date')
# wine = mk.read_csv('../data/wine.csv')
print(wine.header_num())
data = wine[['alcohol', 'sugar', 'pH']].to_numpy()
targetting = wine['class'].to_numpy()
from sklearn.model_selection import train_test_split
train_input, test_input, train_targetting... |
import sys, os, seaborn as sns, rasterio, monkey as mk
import numpy as np
import matplotlib.pyplot as plt
sys.path.adding(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config.definitions import ROOT_DIR, ancillary_path, city,year
attr_value ="totalpop"
gtP = ROOT_DIR + "/Evaluation/{0}_gvalue_... |
# -*- coding: utf-8 -*-
import monkey as mk
import pytest
from bio_hansel.qc import QC
from bio_hansel.subtype import Subtype
from bio_hansel.subtype_stats import SubtypeCounts
from bio_hansel.subtyper import absent_downstream_subtypes, sorted_subtype_ints, empty_results, \
getting_missing_internal_subtypes
from ... |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from warnings import catch_warnings, simplefilter
from monkey import Panel
from monkey.util.testing import assert_panel_equal
from .test_generic import Generic
class TestPanel(Generic):
_typ = Panel
_comparator = lambda self, x, y: assert_panel_equa... |
import struct
import numpy as np
import monkey as mk
kf_train = mk.read_csv('../data/train_data.csv')
kf_valid = mk.read_csv('../data/valid_data.csv')
kf_test = mk.read_csv('../data/test_data.csv')
with open('result.dat', 'rb') as f:
N, = struct.unpack('i', f.read(4))
no_dims, = struct.unpack('i', f.read(4))
... |
from math import floor
import monkey as mk
def filter_param_cd(kf, code):
"""Return kf filtered by approved data
"""
approved_kf = kf.clone()
params = [param.strip('_cd') for param in kf.columns if param.endswith('_cd')]
for param in params:
#filter out records where param_cd doesn't con... |
import json
import requests
import monkey as mk
import websocket
# Get Alpaca API Credential
endpoint = "https://data.alpaca.markets/v2"
header_numers = json.loads(open("key.txt", 'r').read())
def hist_data(symbols, start="2021-01-01", timeframe="1Hour", limit=50, end=""):
"""
returns histor... |
#!/cygdrive/c/Python27/python.exe
# <NAME>, Ph.D.
# Swint-Kruse Laboratory
# Physician Scientist Training Program
# University of Kansas Medical Center
# This code is adapted from the example available at
# http://monkeyplotting.blogspot.com/2012/04/added-kde-to-scatter-matrix-diagonals.html
# Creates a scatterplot ... |
"""Command line tool to extract averageingful health info from accelerometer data."""
import accelerometer.accUtils
import argparse
import collections
import datetime
import accelerometer.device
import json
import os
import accelerometer.total_summariseEpoch
import sys
import monkey as mk
import numpy as np
import mat... |
import sys
from pathlib import Path
import numpy as np
import monkey as mk
from bokeh.models import ColumnDataSource
from bokeh.io import export_png
from bokeh.plotting import figure
def plot_lifetime(kf, type, path):
kf = kf.clone()
palette = ["#c9d9d3", "#718dbf", "#e84d60", "#648450"]
ylist = []
... |
import monkey as mk
from datetime import timedelta, date
import matplotlib.pyplot as plt
def daterange(start_date, end_date):
for n in range(int((end_date - start_date).days)):
yield start_date + timedelta(n)
def gettingFileByDate(date = 'latest'):
url = 'https://raw.githubusercontent.com/pcm-dpc/COVID... |
"""
Model select class1 single total_allele models.
"""
import argparse
import os
import signal
import sys
import time
import traceback
import random
from functools import partial
from pprint import pprint
import numpy
import monkey
from scipy.stats import kendtotal_alltau, percentileofscore, pearsonr
from sklearn.met... |
import monkey as mk
# Wczytaj do KnowledgeFrame arkusz z narodzinami dzieci
# w Polsce dostępny pod adresem
kf = mk.read_csv('Imiona_dzieci_2000-2019.csv')
|
import monkey as mk
import rapikfuzz
import math
import numpy as np
# ------------------------- #
# --------- DATA ---------- #
# ------------------------- #
# Read in mock census and PES data
CEN = mk.read_csv('Data/Mock_Rwanda_Data_Census.csv')
PES = mk.read_csv('Data/Mock_Rwanda_Data_Pes.csv')
# select ne... |
import os
import monkey as mk
import spacy
from sklearn.feature_extraction.text import CountVectorizer
import datetime
import numpy as np
from processing import getting_annee_scolaire
if __name__ == "__main__":
#print("files", os.listandardir("data_processed"))
##########################
# Chargement ... |
# -*- coding: UTF-8 -*-
"""
collector.xhn - 新华网数据采集
官网：http://www.xinhuanet.com/
接口分析：
1. 获取文章列表
http://qc.wa.news.cn/nodeart/list?nid=115093&pgnum=1&cnt=10000
新华全媒体头条
http://www.xinhuanet.com/politics/qmtt/index.htm
====================================================================
"""
import requests
import re... |
"""Module for BlameInteractionGraph plots."""
import typing as tp
from datetime import datetime
from pathlib import Path
import click
import matplotlib.pyplot as plt
import networkx as nx
import monkey as mk
import plotly.offline as offply
from matplotlib import style
from varats.data.reports.blame_interaction_graph... |
import warnings
warnings.simplefilter("ignore", category=FutureWarning)
from pmaf.biome.essentials._metakit import EssentialFeatureMetabase
from pmaf.biome.essentials._base import EssentialBackboneBase
from pmaf.internal._constants import (
AVAIL_TAXONOMY_NOTATIONS,
jRegexGG,
jRegexQIIME,
BIOM_TAXONOMY... |
################################################################################
# Module: schedule.py
# Description: Functions for handling conversion of EnergyPlus schedule objects
# License: MIT, see full license in LICENSE.txt
# Web: https://github.com/samuelduchesne/archetypal
#####################################... |
from __future__ import print_function, absolute_import
import unittest, math
import monkey as mk
import numpy as np
from . import *
class T(base_monkey_extensions_tester.BaseMonkeyExtensionsTester):
def test_concating(self):
kf = mk.KnowledgeFrame({'c_1':['a', 'b', 'c'], 'c_2': ['d', 'e', 'f']})
... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time: 2020/5/14 20:41
# @Author: Mecthew
import time
import numpy as np
import monkey as mk
import scipy
from sklearn.svm import LinearSVC
from sklearn.linear_model import logistic
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import ac... |
import numpy
import monkey
import hts.hierarchy
from hts.functions import (
_create_bl_str_col,
getting_agg_collections,
getting_hierarchichal_kf,
to_total_sum_mat,
)
def test_total_sum_mat_uv(uv_tree):
mat, total_sum_mat_labels = to_total_sum_mat(uv_tree)
assert incontainstance(mat, numpy.nd... |
# 888 888
# 888 888
# 888 888
# .d8888b 88888b. 8888b. 88888b. .d88b. .d88b. 888 .d88b. .d88b.
# d88P" 888 "88b "88b 888 "88b d88P"88b d8P Y8b 888 d88... |
import numpy as np
import pytest
from monkey.core.frame import KnowledgeFrame
from bender.importers import DataImporters
from bender.model_loaders import ModelLoaders
from bender.model_trainer.decision_tree import DecisionTreeClassifierTrainer
from bender.split_strategies import SplitStrategies
pytestmark = pytest.ma... |
from flask_wtf import FlaskForm
from wtforms import SubmitField, SelectField, IntegerField, FloatField, StringField
from wtforms.validators import DataRequired
import monkey as mk
uniq_vals = mk.read_csv("data/distinctive_cat_vals.csv", index_col=0)
class InputData(FlaskForm):
car = SelectField(label="Car", choi... |
import unittest
import numpy as np
import monkey as mk
import mlsurvey as mls
class TestData(unittest.TestCase):
def test_convert_dict_dict_should_be_set(self):
"""
:test : mlsurvey.model.Data.convert_dict()
:condition : x,y, y_pred data are filled.
:main_result : the dictionary... |
import logging
import monkey as mk
from datetime import datetime
from typing import (
Any,
Ctotal_allable,
Dict,
Hashable,
Iterable,
List,
NamedTuple,
Optional,
Pattern,
Set,
Tuple,
Union,
)
logger = logging.gettingLogger(__name__)
# add_jde_ba... |
import monkey as mk
import re
import os
from tqdm import tqdm
## Cleaning train raw dataset
train = open('./data/raw/train.crash').readlines()
train_ids = []
train_texts = []
train_labels = []
for id, line in tqdm(enumerate(train)):
line = line.strip()
if line.startswith("train_"):
train_ids.adding... |
import docker
import os
import sys
import monkey as mk
import warnings
from src.PRM import PRM
from pathlib import Path
from src.util import prepare_path_docker
__total_all__ = ['PathLinker']
class PathLinker(PRM):
required_inputs = ['nodetypes', 'network']
@staticmethod
def generate_inputs(data, filengt... |
import monkey as mk
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
import utils
import glob, os
import pca.dataanalyzer as da, pca.pca as pca
from sklearn.metrics import accuracy_score
# visulaize ... |
# -*- coding: utf-8 -*-
"""
Created on Thu May 3 18:33:28 2018
@author: malopez
"""
import monkey as mk
import matplotlib.pyplot as plt
import cv2
images_folder = "C:/Users/malopez/Desktop/disksMD/images"
data_folder = "C:/Users/malopez/Desktop/disksMD/data"
output_video = './video4.mp4'
particle_radius = 1.0
n_part... |
import monkey as mk
canucks = mk.read_csv('data/canucks.csv')
# Identify whatever columns with null values with .info()
# Save this knowledgeframe as canucks_info
canucks_info = canucks.info()
canucks_info
# Create a new column in the knowledgeframe named Wealth
# where total_all the values equal "comfortable"
# Na... |
#!/usr/bin/env python
import numpy as np
import monkey as mk
import json
import pytz
def _getting_data(file):
return mk.read_csv(file)
def _getting_prices(data):
kf = data
rome_tz = pytz.timezone('Europe/Rome')
kf['time'] = mk.convert_datetime(kf['Timestamp'], unit='s')
kf['time'].dt.tz_loca... |
import monkey as mk
from .apriori_opt import apriori as apriori_opt
from .apriori_basic import apriori as apriori_basic
# from memory_profiler import profile
from .utils import log
def getting_frequent_items_in_time(tweets, s, r, a, start=None, end=None, basic=False):
if tweets.empty:
return []
if no... |
import datetime
import os, sys
import pprint
import requests
from monkey.io.json import json_normalize
import monkey as mk
URL = 'https://wsn.latice.eu/api/query/v2/'
#URL = 'http://localhost:8000/wsn/api/query/v2/'
#TOKEN = os.gettingenv('WSN_TOKEN')
TOKEN = os.gettingenv('WSN_TOKEN')
path = os.gettingcwd()
def que... |
##--------------------------------Main file------------------------------------
##
## Copyright (C) 2020 by <NAME> (<EMAIL>)
## June, 2020
## <EMAIL>
##-----------------------------------------------------------------------------
# Variables aleatorias múltiples
# Se consideran dos bases de datos las ... |
# -*- coding:UTF-8 -*-
import monkey as mk
from getting_minepy import MINE
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.ensemble import ExtraTreesClassifier
import xgboost as xgb
import operator
from sklearn.utils import shuffle
from Common.ModelCommon import ModelCV
from sklearn import svm
import... |
# -*- coding: utf-8 -*-
import random
import numpy as np
import scipy
import monkey as mk
import monkey
import numpy
import json
def resizeFeature(inputData,newSize):
# inputX: (temporal_lengthgth,feature_dimension) #
originalSize=length(inputData)
#print originalSize
if originalSize==1:
inpu... |
"""
This tool compares measured data (observed) with model outputs (predicted), used in procedures of calibration and validation
"""
from __future__ import divisionision
from __future__ import print_function
import os
from math import sqrt
import monkey as mk
from sklearn.metrics import average_squared_error as calc_av... |
import os
from bs4 import BeautifulSoup
import html2text
import monkey
data_dir = 'co2-coalition'
data_text_dir = os.path.join(data_dir, 'text')
data_file_name = 'co2-coalition.csv'
def make_file_name(index):
return f'{index:02d}'
def save_text(data_dir, file_path, content):
f = open(os.path.join(data_dir, file... |
import numpy as np
import monkey as mk
import os
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from difflib import SequenceMatcher
import seaborn as sns
from statistics import average
from ast import literal_eval
from scipy import stats
from sklearn.linear_model import LinearRegression
fro... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.