我进入整个python，机器学习领域大约4周。 我使用借阅俱乐部数据在张量流中使用 LinearClassifier 编写了一些东西。
然而，当我运行脚本时，它会在某个时刻挂起。
任何有经验的人帮助将不胜感激。这是脚本的副本。
""" Collect and load the data """
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})
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.float(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(0,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=1000).repeat(count=None).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")
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_input_fn(X_test, Y_test, 100)
)
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
以下是控制台在挂起之前输出的示例;
43: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
loans['loan_status'] = loans['loan_status'].apply(lambda s: np.float(s == 'Fully Paid')) # Convert to boolean integer
/Users/acacia/Desktop/work/machine_learning/tensor_flow/logistic_regression.py:53: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
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)
/Users/acacia/Desktop/work/machine_learning/tensor_flow/logistic_regression.py:57: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
loans['term'] = loans['term'].apply(lambda s:np.float(s[1:3]))
/Users/acacia/Desktop/work/machine_learning/tensor_flow/logistic_regression.py:59: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
loans['emp_length'] = loans['emp_length'].apply(lambda s: convert_duration(s))
/Users/acacia/Desktop/work/machine_learning/tensor_flow/logistic_regression.py:62: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
loans['zip_code'] = loans['zip_code'].apply(lambda s:np.float(s[:3]))
/Users/acacia/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:3035: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
downcast=downcast, **kwargs)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /var/folders/2t/bhtmq3ln5mb6mv26w6pfbq_m0000gn/T/tmpictbxp6x
INFO:tensorflow:Using config: {'_model_dir': '/var/folders/2t/bhtmq3ln5mb6mv26w6pfbq_m0000gn/T/tmpictbxp6x', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1a205d6358>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 1 into /var/folders/2t/bhtmq3ln5mb6mv26w6pfbq_m0000gn/T/tmpictbxp6x/model.ckpt.
INFO:tensorflow:loss = 69.31472, step = 1
INFO:tensorflow:Saving checkpoints for 100 into /var/folders/2t/bhtmq3ln5mb6mv26w6pfbq_m0000gn/T/tmpictbxp6x/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-05-07-10:55:12
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /var/folders/2t/bhtmq3ln5mb6mv26w6pfbq_m0000gn/T/tmpictbxp6x/model.ckpt-100
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.