How to use test_transactions method in pytest-django

Best Python code snippet using pytest-django_python

split_data_8-1-1_sample_item.py

Source:split_data_8-1-1_sample_item.py Github

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1#!/bin/python2import pandas as pd3import numpy as np4from time import gmtime, strftime567def now():8 return strftime("%Y-%m-%d %H:%M:%S", gmtime())910ratings_train = pd.DataFrame()11ratings_test = pd.DataFrame()12ratings_train_80 = pd.DataFrame()13ratings_test_10 = pd.DataFrame()14test_input = pd.DataFrame()15test_eval = pd.DataFrame()16test_input_10 = pd.DataFrame()17test_eval_10 = pd.DataFrame()1819print now(), 'start clean'20ratings = pd.read_csv('../cleaned_rating.dat', header=None, sep=' ', names=['u', 'i', 'r'])21users = (ratings['u'].unique())22print now(), 'splitting train-test'23for user in users:24 # 90-1025 items = ratings[ratings['u'] == user]['i'].unique()26 items = np.random.permutation(items)27 k = int(round(items.shape[0] * 0.9))28 train_items = items[:k]29 test_items = items[k:]30 ratings_train = ratings_train.append(ratings[(ratings['u'] == user) & (ratings['i'].isin(train_items))])31 test_transactions = ratings[(ratings['u'] == user) & (ratings['i'].isin(test_items))]32 ratings_test = ratings_test.append(test_transactions)33 # splitting test34 n = test_transactions.shape[0]35 k = int(round(n / 2.0))36 test_input = test_input.append(test_transactions.iloc[:k, :])37 test_eval = test_eval.append(test_transactions.iloc[k:, :])38 # 80-10 in 9039 k = int(round((8 / 9.0) * train_items.shape[0]))40 train_items_80 = train_items[:k]41 test_items_10 = train_items[k:]42 ratings_train_80 = ratings_train_80.append(ratings[(ratings['u'] == user) & (ratings['i'].isin(train_items_80))])43 test_transactions = ratings[(ratings['u'] == user) & (ratings['i'].isin(test_items_10))]44 ratings_test_10 = ratings_test_10.append(test_transactions)45 # splitting test46 n = test_transactions.shape[0]47 k = int(round(n / 2.0))48 test_input_10 = test_input_10.append(test_transactions.iloc[:k, :])49 test_eval_10 = test_eval_10.append(test_transactions.iloc[k:, :])5051ratings_train.to_csv('./ratings_train.dat', header=False, sep=' ', index=False)52ratings_train_iuv = ratings_train[['i', 'u', 'r']]53ratings_train_iuv.to_csv('./ratings_train_iuv.dat', header=False, sep=' ', index=False)5455ratings_train_80.to_csv('./ratings_train_80.dat', header=False, sep=' ', index=False)56ratings_train_80_iuv = ratings_train_80[['i', 'u', 'r']]57ratings_train_80_iuv.to_csv('./ratings_train_80_iuv.dat', header=False, sep=' ', index=False)5859test_input = test_input[['u', 'i', 'r']].astype(int)60test_eval = test_eval[['u', 'i', 'r']].astype(int)6162test_input_10 = test_input_10[['u', 'i', 'r']].astype(int)63test_eval_10 = test_eval_10[['u', 'i', 'r']].astype(int)6465test_input.to_csv('./ratings_test_input.dat', header=False, sep=' ', index=False)66test_eval.to_csv('./ratings_test_eval.dat', header=False, sep=' ', index=False)6768test_input_10.to_csv('./ratings_train_input.dat', header=False, sep=' ', index=False)69test_eval_10.to_csv('./ratings_train_eval.dat', header=False, sep=' ', index=False)7071pd.DataFrame(test_eval['u'].unique()).to_csv('./target_users.dat', header=False, sep=' ', index=False)72pd.DataFrame(test_eval_10['u'].unique()).to_csv('./train_target_users.dat', header=False, sep=' ', index=False) ...

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split_data_6-2-2_sample_item.py

Source:split_data_6-2-2_sample_item.py Github

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1#!/bin/python2import pandas as pd3import numpy as np4from time import gmtime, strftime567def now():8 return strftime("%Y-%m-%d %H:%M:%S", gmtime())910ratings_train = pd.DataFrame()11ratings_test = pd.DataFrame()12ratings_train_80 = pd.DataFrame()13ratings_test_10 = pd.DataFrame()14test_input = pd.DataFrame()15test_eval = pd.DataFrame()16test_input_10 = pd.DataFrame()17test_eval_10 = pd.DataFrame()1819print now(), 'start clean'20ratings = pd.read_csv('../cleaned_rating.dat', header=None, sep=' ', names=['u', 'i', 'r'])21users = (ratings['u'].unique())22print now(), 'splitting train-test'23for user in users:24 # 90-1025 items = ratings[ratings['u'] == user]['i'].unique()26 items = np.random.permutation(items)27 k = int(round(items.shape[0] * 0.8))28 train_items = items[:k]29 test_items = items[k:]30 ratings_train = ratings_train.append(ratings[(ratings['u'] == user) & (ratings['i'].isin(train_items))])31 test_transactions = ratings[(ratings['u'] == user) & (ratings['i'].isin(test_items))]32 ratings_test = ratings_test.append(test_transactions)33 # splitting test34 n = test_transactions.shape[0]35 k = int(round(n / 2.0))36 test_input = test_input.append(test_transactions.iloc[:k, :])37 test_eval = test_eval.append(test_transactions.iloc[k:, :])38 # 80-10 in 9039 k = int(round((6 / 8.0) * train_items.shape[0]))40 train_items_80 = train_items[:k]41 test_items_10 = train_items[k:]42 ratings_train_80 = ratings_train_80.append(ratings[(ratings['u'] == user) & (ratings['i'].isin(train_items_80))])43 test_transactions = ratings[(ratings['u'] == user) & (ratings['i'].isin(test_items_10))]44 ratings_test_10 = ratings_test_10.append(test_transactions)45 # splitting test46 n = test_transactions.shape[0]47 k = int(round(n / 2.0))48 test_input_10 = test_input_10.append(test_transactions.iloc[:k, :])49 test_eval_10 = test_eval_10.append(test_transactions.iloc[k:, :])5051ratings_train.to_csv('./ratings_train.dat', header=False, sep=' ', index=False)52ratings_train_iuv = ratings_train[['i', 'u', 'r']]53ratings_train_iuv.to_csv('./ratings_train_iuv.dat', header=False, sep=' ', index=False)5455ratings_train_80.to_csv('./ratings_train_80.dat', header=False, sep=' ', index=False)56ratings_train_80_iuv = ratings_train_80[['i', 'u', 'r']]57ratings_train_80_iuv.to_csv('./ratings_train_80_iuv.dat', header=False, sep=' ', index=False)5859test_input = test_input[['u', 'i', 'r']].astype(int)60test_eval = test_eval[['u', 'i', 'r']].astype(int)6162test_input_10 = test_input_10[['u', 'i', 'r']].astype(int)63test_eval_10 = test_eval_10[['u', 'i', 'r']].astype(int)6465test_input.to_csv('./ratings_test_input.dat', header=False, sep=' ', index=False)66test_eval.to_csv('./ratings_test_eval.dat', header=False, sep=' ', index=False)6768test_input_10.to_csv('./ratings_train_input.dat', header=False, sep=' ', index=False)69test_eval_10.to_csv('./ratings_train_eval.dat', header=False, sep=' ', index=False)7071pd.DataFrame(test_eval['u'].unique()).to_csv('./target_users.dat', header=False, sep=' ', index=False)72pd.DataFrame(test_eval_10['u'].unique()).to_csv('./train_target_users.dat', header=False, sep=' ', index=False) ...

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pipelines_test.py

Source:pipelines_test.py Github

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1"""Tests for the Machine Learning Helpers."""2# pylint: disable=missing-docstring3from beancount.parser import parser4from smart_importer.pipelines import AttrGetter5TEST_DATA, _, __ = parser.parse_string(6 """72016-01-01 open Assets:US:BofA:Checking USD82016-01-01 open Expenses:Food:Groceries USD92016-01-01 open Expenses:Food:Coffee USD102016-01-06 * "Farmer Fresh" "Buying groceries"11 Assets:US:BofA:Checking -10.00 USD122016-01-07 * "Starbucks" "Coffee"13 Assets:US:BofA:Checking -4.00 USD14 Expenses:Food:Coffee152016-01-07 * "Farmer Fresh" "Groceries"16 Assets:US:BofA:Checking -11.20 USD17 Expenses:Food:Groceries182016-01-08 * "Gimme Coffee" "Coffee"19 Assets:US:BofA:Checking -3.50 USD20 Expenses:Food:Coffee21"""22)23TEST_TRANSACTIONS = TEST_DATA[3:]24TEST_TRANSACTION = TEST_TRANSACTIONS[0]25def test_get_payee():26 assert AttrGetter("payee").transform(TEST_TRANSACTIONS) == [27 "Farmer Fresh",28 "Starbucks",29 "Farmer Fresh",30 "Gimme Coffee",31 ]32def test_get_narration():33 assert AttrGetter("narration").transform(TEST_TRANSACTIONS) == [34 "Buying groceries",35 "Coffee",36 "Groceries",37 "Coffee",38 ]39def test_get_metadata():40 txn = TEST_TRANSACTION41 txn.meta["attr"] = "value"42 assert AttrGetter("meta.attr").transform([txn]) == ["value"]43 assert AttrGetter("meta.attr", "default").transform(TEST_TRANSACTIONS) == [44 "value",45 "default",46 "default",47 "default",48 ]49def test_get_day_of_month():...

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