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import data_science.lendingclub.dataprep_and_modeling.modeling_utils.data_prep_new as data_prep
import dir_constants as dc
from sklearn.externals import joblib
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import time
from sklearn.metrics import mean_squared_error
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
from lendingclub.dataprep_and_modeling.model_dump.nn_1_0_1 import net_class
import torch
%matplotlib inline
pd.options.display.max_columns = 999
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platform = 'lendingclub'
nn_path = '/home/justin/justin_tinkering/data_science/lendingclub/dataprep_and_modeling/model_dump/nn_1_0_1/1.0.1_e600'
rf_path = '/home/justin/justin_tinkering/data_science/lendingclub/dataprep_and_modeling/model_dump/model_0.2.1.pkl'
regr_version = '2.0.0'
store = pd.HDFStore(
dc.home_path+'/justin_tinkering/data_science/lendingclub/{0}_store.h5'.
format(platform),
append=True)
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loan_info = store['loan_info_merged']
loan_info_clean = store['loan_info_clean']
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loan_info['desc'].replace([' ','.','i'], np.nan, inplace=True)
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examine = loan_info[~pd.isnull(loan_info.desc)]
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loan_info_clean.ix[examine.index,'issue_d'].max()
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examine_2 = examine.ix[examine.desc.str.contains('Borrower added', regex=False), 'desc']
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examine_2
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examine.str.contains?
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examine.loc['384151']
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