如果我们有：
185是否有更短的方法来检查cb是否为空并调用它？这将在Node上运行。
答案 0 :(得分：4)
您可以直接查看import os
import tarfile
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from six.moves import urllib
import tensorflow as tf
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Imputer
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
HOME_PATH = os.getcwd()
""" load the csv file with the lending data and convert to tensors """
def convert_duration(s):
try:
if pd.isnull(s):
return s
elif s[0] == '<':
return 0.0
elif s[:2] == '10':
return 10.0
else:
return np.float(s[0])
except TypeError:
return np.float64(s)
def load_data(file_name):
csv_path = os.path.join(HOME_PATH, file_name)
csv_data = pd.read_csv(csv_path, encoding = "ISO-8859-1", dtype={'desc': np.str, 'verification_status_joint': np.str, 'loan_status': np.str},low_memory=False, na_values=[])
loans = csv_data.loc[csv_data['loan_status'].isin(['Fully Paid', 'Charged Off'])] # Sort out only fully Paid (Paid) and Charged Off (Default)
loans['loan_status'] = loans['loan_status'].apply(lambda s: np.int(s == 'Fully Paid')) # Convert to boolean integer
# Drop Columns with one distinct data field
for col in loans.columns:
if loans[col].nunique() == 1:
del loans[col]
for col in loans.columns:
if (loans[col].notnull().sum() / len(loans.index)) < 0.1 :
del loans[col]
# Remove all irrelevant columns & hifg prediction columns based on pure descetion
loans.drop(labels=['id', 'member_id', 'grade', 'sub_grade', 'last_credit_pull_d', 'emp_title', 'url', 'desc', 'title', 'issue_d', 'earliest_cr_line', 'last_pymnt_d','addr_state'], axis=1, inplace=True)
# Process the text based variables
# Term
loans['term'] = loans['term'].apply(lambda s:np.float(s[1:3]))
loans['emp_length'] = loans['emp_length'].apply(lambda s: convert_duration(s))
#change zip code to just the first 3 significant digits
loans['zip_code'] = loans['zip_code'].apply(lambda s:np.float(s[:3]))
loans.fillna('',inplace=True)
loan_data = shuffle(loans)
X = loan_data.drop(labels=['loan_status'], axis=1)
Y = loan_data['loan_status']
## consider processing tensorflow feature columns here and return as one response and standardise at one
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
# scaler = StandardScaler()
# X_train = scaler.fit_transform(X_train)
# X_test = scaler.fit_transform(X_test)
return (X_train, Y_train), (X_test, Y_test)
def my_input_fn(features, labels, batch_size , shuffle=True):
# consider changing categorical columns and all
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
dataset = dataset.shuffle(buffer_size=100000).repeat(count=None).batch(batch_size)
return dataset.make_one_shot_iterator().get_next()
def my_eval_fn(features, labels, batch_size , shuffle=True):
# consider changing categorical columns and all
dataset = tf.data.Dataset.from_tensor_slices((features,labels))
dataset = dataset.batch(batch_size)
return dataset.make_one_shot_iterator().get_next()
#Start on calls to make data available
(X_train, Y_train), (X_test, Y_test) = load_data("loan_data.csv")
print(dict(X_train))
my_feature_columns = []
numerical_columns = ['loan_amnt',
'funded_amnt',
'funded_amnt_inv',
'int_rate',
'installment',
'annual_inc',
'dti',
'delinq_2yrs',
'inq_last_6mths',
'mths_since_last_delinq',
'mths_since_last_record',
'open_acc',
'pub_rec',
'revol_bal',
'revol_util',
'total_acc',
'total_pymnt',
'total_pymnt_inv',
'total_rec_prncp',
'total_rec_int',
'total_rec_late_fee',
'recoveries',
'collection_recovery_fee',
'last_pymnt_amnt',
'collections_12_mths_ex_med',
'mths_since_last_major_derog',
'acc_now_delinq',
'tot_coll_amt',
'tot_cur_bal',
'total_rev_hi_lim']
categorical_columns = ['home_ownership',
'verification_status',
'pymnt_plan',
'purpose',
'initial_list_status',
'application_type']
for key in numerical_columns:
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
for key in categorical_columns:
my_feature_columns.append(tf.feature_column.categorical_column_with_hash_bucket(key=key, hash_bucket_size = 10))
classifier = tf.estimator.LinearClassifier(
feature_columns=my_feature_columns
)
classifier.train(
input_fn=lambda:my_input_fn(X_train, Y_train, 100),
steps=100
)
eval_result = classifier.evaluate(
input_fn=lambda:my_eval_fn(X_test, Y_test, 100)
)
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
。
如果cb是一个函数，则首先检查truthy值，然后调用该函数。
如果cb为cb，则第一部分为falsy，第二部分未执行。
null答案 1 :(得分：0)
现在 2021 年：Optional chaining
cb?.()
如果 cb 是 null - cb 未被调用。
如果 cb 是一个函数，cb 被调用。
注意：如果 cb 是一个非空/未定义的值，（例如 23 或 false）那么这将抛出一个 TypeError。