Tian et al. 2011 Model 2

A simple 1d cnn

# Hacky way to schedule. Here I'm setting these to sleep until the gpus should be free.
# At the end of the notebooks  os._exit(00) will kill the kernel freeing the gpu. 
#                          Hours to wait
# import time; time.sleep( 24 * (60*60))

# Run Settings:
nb_name = '12_TianEtAl2011'# Set manually! -----------------------------------

downsample_obs = False
train_n = 90
test_n = 10

dataloader_batch_size = 16 #64
run_epochs = 200

use_gpu_num = 0

# Imports --------------------------------------------------------------------
import os
import pandas as pd
import numpy as np
import re

import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch import nn

import tqdm
from tqdm import tqdm

import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"


import dlgwas
from dlgwas.kegg import ensure_dir_path_exists
from dlgwas.kegg import get_cached_result
from dlgwas.kegg import put_cached_result

from dlgwas.dlfn import calc_cs
from dlgwas.dlfn import apply_cs
from dlgwas.dlfn import reverse_cs

from dlgwas.dlfn import TianEtAl2011Dataset
from dlgwas.dlfn import train_loop
from dlgwas.dlfn import train_error
from dlgwas.dlfn import test_loop
from dlgwas.dlfn import train_nn
from dlgwas.dlfn import yhat_loop


device = "cuda" if torch.cuda.is_available() else "cpu"
if use_gpu_num in [0, 1]: 
    torch.cuda.set_device(use_gpu_num)
print(f"Using {device} device")


ensure_dir_path_exists(dir_path = '../models/'+nb_name)
ensure_dir_path_exists(dir_path = '../reports/'+nb_name)

ensure_dir_path_exists(dir_path = '../models/'+nb_name)
ensure_dir_path_exists(dir_path = '../reports/'+nb_name)

Using cuda device

# use_gpu_num = 0 # This should change based on whichever gpu is free. 
#                 # If even notebooks are set to 0 then that will be a reasonable default. 

# import os
# import pandas as pd
# import numpy as np
# import re

# import torch
# from torch.utils.data import Dataset
# from torch.utils.data import DataLoader
# from torch import nn

# device = "cuda" if torch.cuda.is_available() else "cpu"
# if use_gpu_num in [0, 1]: 
#     torch.cuda.set_device(use_gpu_num)
# print(f"Using {device} device")

# import tqdm
# from tqdm import tqdm

# import plotly.graph_objects as go
# import plotly.express as px
# import plotly.io as pio
# pio.templates.default = "plotly_white"


# import dlgwas
# from dlgwas.kegg import ensure_dir_path_exists
# from dlgwas.kegg import get_cached_result
# from dlgwas.kegg import put_cached_result

# from dlgwas.dlfn import calc_cs
# from dlgwas.dlfn import apply_cs
# from dlgwas.dlfn import reverse_cs

# # set up directory for notebook artifacts
# nb_name = '12_TianEtAl2011'
# ensure_dir_path_exists(dir_path = '../models/'+nb_name)
# ensure_dir_path_exists(dir_path = '../reports/'+nb_name)

Load Cleaned Data

# Read in cleaned data
taxa_groupings = pd.read_csv('../models/10_TianEtAl2011/taxa_groupings.csv')
data           = pd.read_csv('../models/10_TianEtAl2011/clean_data.csv')

# Define holdout sets (Populations)
uniq_pop = list(set(taxa_groupings['Population']))
print(str(len(uniq_pop))+" Unique Holdout Groups.")
taxa_groupings['Holdout'] = None
for i in range(len(uniq_pop)):
    mask = (taxa_groupings['Population'] == uniq_pop[i])
    taxa_groupings.loc[mask, 'Holdout'] = i

taxa_groupings

25 Unique Holdout Groups.

	Unnamed: 0	sample	Population	Holdout
0	0	Z001E0001	B73 x B97	22
1	1	Z001E0002	B73 x B97	22
2	2	Z001E0003	B73 x B97	22
3	3	Z001E0004	B73 x B97	22
4	4	Z001E0005	B73 x B97	22
...	...	...	...	...
4671	4671	Z026E0196	B73 x Tzi8	4
4672	4672	Z026E0197	B73 x Tzi8	4
4673	4673	Z026E0198	B73 x Tzi8	4
4674	4674	Z026E0199	B73 x Tzi8	4
4675	4675	Z026E0200	B73 x Tzi8	4

4676 rows × 4 columns

# # Read in cleaned data
# taxa_groupings = pd.read_csv('../models/10_TianEtAl2011/taxa_groupings.csv')
# data           = pd.read_csv('../models/10_TianEtAl2011/clean_data.csv')

# # Define holdout sets (Populations)
# uniq_pop = list(set(taxa_groupings['Population']))
# print(str(len(uniq_pop))+" Unique Holdout Groups.")
# taxa_groupings['Holdout'] = None
# for i in range(len(uniq_pop)):
#     mask = (taxa_groupings['Population'] == uniq_pop[i])
#     taxa_groupings.loc[mask, 'Holdout'] = i

# taxa_groupings

Setup Holdouts

#randomly holdout a population if there is not a file with the population held out.
# Holdout_Int = 0
Holdout_Int_path = '../models/'+nb_name+'/holdout_pop_int.pkl'
if None != get_cached_result(Holdout_Int_path):
    Holdout_Int = get_cached_result(Holdout_Int_path)
else:
    Holdout_Int = int(np.random.choice([i for i in range(len(uniq_pop))], 1))
    put_cached_result(Holdout_Int_path, Holdout_Int)

    
print("Holding out i="+str(Holdout_Int)+": "+uniq_pop[Holdout_Int])

mask = (taxa_groupings['Holdout'] == Holdout_Int)
train_idxs = list(taxa_groupings.loc[~mask, ].index)
test_idxs = list(taxa_groupings.loc[mask, ].index)

Holding out i=13: B73 x CML52

[len(e) for e in [test_idxs, train_idxs]]

[196, 4480]

# downsample_obs = True
# train_n = 900
# test_n = 100

if downsample_obs == True:
    train_idxs = np.random.choice(train_idxs, train_n)
    test_idxs = np.random.choice(test_idxs, test_n)
    print([len(e) for e in [test_idxs, train_idxs]])

# used to go from index in tensor to index in data so that the right xs tensor can be loaded in
idx_original = np.array(data.index)

y1 = data['leaf_length']
y2 = data['leaf_width']
y3 = data['upper_leaf_angle']
y1 = np.array(y1)
y2 = np.array(y2)
y3 = np.array(y3)

# #randomly holdout a population if there is not a file with the population held out.
# # Holdout_Int = 0
# Holdout_Int_path = '../models/'+nb_name+'/holdout_pop_int.pkl'
# if None != get_cached_result(Holdout_Int_path):
#     Holdout_Int = get_cached_result(Holdout_Int_path)
# else:
#     Holdout_Int = int(np.random.choice([i for i in range(len(uniq_pop))], 1))
#     put_cached_result(Holdout_Int_path, Holdout_Int)

# print("Holding out i="+str(Holdout_Int)+": "+uniq_pop[Holdout_Int])

# mask = (taxa_groupings['Holdout'] == Holdout_Int)
# train_idxs = list(taxa_groupings.loc[~mask, ].index)
# test_idxs = list(taxa_groupings.loc[mask, ].index)

# # used to go from index in tensor to index in data so that the right xs tensor can be loaded in
# idx_original = np.array(data.index)

# y1 = data['leaf_length']
# y2 = data['leaf_width']
# y3 = data['upper_leaf_angle']
# y1 = np.array(y1)
# y2 = np.array(y2)
# y3 = np.array(y3)

Scale data

scale_dict_path = '../models/'+nb_name+'/scale_dict.pkl'
if None != get_cached_result(scale_dict_path):
    scale_dict = get_cached_result(scale_dict_path)
else:
    scale_dict = {
        'y1':calc_cs(y1[train_idxs]),
        'y2':calc_cs(y2[train_idxs]),
        'y3':calc_cs(y3[train_idxs])
    }
    put_cached_result(scale_dict_path, scale_dict)

y1 = apply_cs(y1, scale_dict['y1'])
y2 = apply_cs(y2, scale_dict['y2'])
y3 = apply_cs(y3, scale_dict['y3'])

# scale_dict = {
#     'y1':calc_cs(y1[train_idxs]),
#     'y2':calc_cs(y2[train_idxs]),
#     'y3':calc_cs(y3[train_idxs])
# }

# y1 = apply_cs(y1, scale_dict['y1'])
# y2 = apply_cs(y2, scale_dict['y2'])
# y3 = apply_cs(y3, scale_dict['y3'])

Allow for cycling data onto and off of GPU

# loading this into memory causes the session to crash

y1_train = torch.from_numpy(y1[train_idxs])[:, None]
y2_train = torch.from_numpy(y2[train_idxs])[:, None]
y3_train = torch.from_numpy(y3[train_idxs])[:, None]

idx_original_train = torch.from_numpy(idx_original[train_idxs])

y1_test = torch.from_numpy(y1[test_idxs])[:, None]
y2_test = torch.from_numpy(y2[test_idxs])[:, None]
y3_test = torch.from_numpy(y3[test_idxs])[:, None]

idx_original_test = torch.from_numpy(idx_original[test_idxs])


# dataloader_batch_size = 64

training_dataloader = DataLoader(
    TianEtAl2011Dataset(
        y1 = y1_train,
        y2 = y2_train,
        y3 = y3_train,
        idx_original = idx_original_train,
        use_gpu_num = use_gpu_num,
#         device = 'cpu'
    ), 
    batch_size = dataloader_batch_size, 
    shuffle = True)

testing_dataloader = DataLoader(
    TianEtAl2011Dataset(
        y1 = y1_test,
        y2 = y2_test,
        y3 = y3_test,
        idx_original = idx_original_test,
        use_gpu_num = use_gpu_num,
#         device = 'cpu'
    ), 
    batch_size = dataloader_batch_size, 
    shuffle = True)

Using cuda device
Using cuda device

# # loading this into memory causes the session to crash

# y1_train = torch.from_numpy(y1[train_idxs])[:, None]
# y2_train = torch.from_numpy(y2[train_idxs])[:, None]
# y3_train = torch.from_numpy(y3[train_idxs])[:, None]

# idx_original_train = torch.from_numpy(idx_original[train_idxs])

# y1_test = torch.from_numpy(y1[test_idxs])[:, None]
# y2_test = torch.from_numpy(y2[test_idxs])[:, None]
# y3_test = torch.from_numpy(y3[test_idxs])[:, None]

# idx_original_test = torch.from_numpy(idx_original[test_idxs])

# class CustomDataset(Dataset):
#     device = "cuda" if torch.cuda.is_available() else "cpu"
#     if use_gpu_num in [0, 1]: 
#         torch.cuda.set_device(use_gpu_num)
#     print(f"Using {device} device")
    
#     def __init__(self, y1, y2, y3, 
#                  idx_original,
#                  transform = None, target_transform = None, 
#                  **kwargs
#                 ):
#         self.y1 = y1
#         self.y2 = y2
#         self.y3 = y3
#         self.idx_original = idx_original
#         self.transform = transform
#         self.target_transform = target_transform    
    
#     def __len__(self):
#         return len(self.y1)
    
#     def __getitem__(self, idx):
#         y1_idx = self.y1[idx].to(device).float()
#         y2_idx = self.y2[idx].to(device).float()
#         y3_idx = self.y3[idx].to(device).float()
        
        
#         # Change type of xs loaded !! ----------------------------------------
#         # load in xs as they are needed.
#         # Non-Hilbert Version
        
        
#         save_path = '../models/10_TianEtAl2011/markers/'
#         # Hilbert version
#         # save_path = '../models/'+nb_name+'/hilbert/'
#         save_file_path = save_path+'m'+str(int(self.idx_original[idx]))+'.npz'
#         xs_idx = np.load(save_file_path)['arr_0']
#         xs_idx = torch.from_numpy(xs_idx).to(device).float()
#         xs_idx = xs_idx.squeeze()
        
#         # to match pytorch's conventions channel must be in the second dim
#         xs_idx = torch.swapaxes(xs_idx, 0, 1) 
        
#         if self.transform:
#             xs_idx = self.transform(xs_idx)
            
#         if self.target_transform:
#             y1_idx = self.transform(y1_idx)
#             y2_idx = self.transform(y2_idx)
#             y3_idx = self.transform(y3_idx)
#         return xs_idx, y1_idx, y2_idx, y3_idx

# training_dataloader = DataLoader(
#     CustomDataset(
#         y1 = y1_train,
#         y2 = y2_train,
#         y3 = y3_train,
#         idx_original = idx_original_train
#     ), 
#     batch_size = 64, 
#     shuffle = True)

# testing_dataloader = DataLoader(
#     CustomDataset(
#         y1 = y1_test,
#         y2 = y2_test,
#         y3 = y3_test,
#         idx_original = idx_original_train
#     ), 
#     batch_size = 64, 
#     shuffle = True)

# xs_i, y1_i, y2_i, y3_i = next(iter(training_dataloader))

# del training_dataloader

# torch.cuda.empty_cache()

# xs_i.shape

Non-Boilerplate

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()    
        self.x_network = nn.Sequential(
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            nn.Conv1d(
                in_channels= 4, # second channel
                out_channels= 4,
                kernel_size= 3,
                stride= 2
            ), 
            nn.MaxPool1d((3,), stride=2),
            
            nn.Flatten(),
            nn.Linear(52, 32),
            nn.BatchNorm1d(32),
            nn.ReLU(),
                        
            nn.Linear(32, 32),
            nn.BatchNorm1d(32),
            nn.ReLU(),
            
            nn.Linear(32, 1)
        )
        
    def forward(self, x):
        x_out = self.x_network(x)
        return x_out

# model = NeuralNetwork().to(device)

# xs_i, y1_i, y2_i, y3_i = next(iter(training_dataloader))
# xs_i.shape

# xs_i, y1_i, y2_i, y3_i = next(iter(testing_dataloader))
# xs_i.shape

# model(xs_i)

# model, loss_df = train_nn(
#     nb_name,
#     training_dataloader,
#     testing_dataloader,
#     model,
#     learning_rate = 1e-3,
#     batch_size = dataloader_batch_size,
#     epochs = run_epochs
# )

# don't run if either of these exist because there may be cases where we want the results but not the model

if not os.path.exists('../models/'+nb_name+'/model.pt'): 
    # Shared setup (train from scratch and load latest)
    model = NeuralNetwork()

    # find the biggest model to save
    saved_models = os.listdir('../models/'+nb_name+'/')
    saved_models = [e for e in saved_models if re.match('model*', e)]

    if saved_models == []:
        epochs_run = 0
    else:
        # if there are saved models reload and resume training
        saved_models_numbers = [int(e.replace('model_', ''
                                    ).replace('.pt', ''
                                    ).split('_')[0]) for e in saved_models]
        # saved_models
        epochs_run = max(saved_models_numbers)+1 # add 1 to account for 0 index
        latest_model = [e for e in saved_models if re.match(
            '^model_'+str(epochs_run-1)+'_.*\.pt$', e)][0] # subtract 1 to convert back
        model.load_state_dict(torch.load('../models/'+nb_name+'/'+latest_model))
        print('Resuming Training: '+str(epochs_run)+'/'+str(run_epochs)+' epochs run.')
    
    model.to(device)   

    model, loss_df = train_nn(
        nb_name,
        training_dataloader,
        testing_dataloader,
        model,
        learning_rate = 1e-3,
        batch_size = dataloader_batch_size,
        epochs = (run_epochs - epochs_run)
    )
    
    # experimental outputs:
    # 1. Model
    torch.save(model.state_dict(), '../models/'+nb_name+'/model.pt') # convention is to use .pt or .pth

    # 2. loss_df
    loss_df.to_csv('../reports/'+nb_name+'/loss_df.csv', index=False)  
    
    
    # 3. predictions 
    yhats = pd.concat([
        yhat_loop(testing_dataloader, model).assign(Split = 'Test'),
        yhat_loop(training_dataloader, model).assign(Split = 'Train')], axis = 0)

    yhats.to_csv('../reports/'+nb_name+'/yhats.csv', index=False)

NeuralNetwork()

NeuralNetwork(
  (x_network): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (1): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (3): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (5): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (7): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (8): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (9): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (11): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (12): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (13): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (15): MaxPool1d(kernel_size=(3,), stride=2, padding=0, dilation=1, ceil_mode=False)
    (16): Flatten(start_dim=1, end_dim=-1)
    (17): Linear(in_features=52, out_features=32, bias=True)
    (18): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU()
    (20): Linear(in_features=32, out_features=32, bias=True)
    (21): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (22): ReLU()
    (23): Linear(in_features=32, out_features=1, bias=True)
  )
)

Standard Visualizations

loss_df = pd.read_csv('../reports/'+nb_name+'/loss_df.csv')

loss_df.TrainMSE = reverse_cs(loss_df.TrainMSE, scale_dict['y1'])
loss_df.TestMSE  = reverse_cs(loss_df.TestMSE , scale_dict['y1'])


fig = go.Figure()
fig.add_trace(go.Scatter(x=loss_df.Epoch, y=loss_df.TestMSE,
                    mode='lines', name='Test'))
fig.add_trace(go.Scatter(x=loss_df.Epoch, y=loss_df.TrainMSE,
                    mode='lines', name='Train'))
fig.show()

yhats = pd.read_csv('../reports/'+nb_name+'/yhats.csv')

yhats.y_true = reverse_cs(yhats.y_true, scale_dict['y1'])
yhats.y_pred = reverse_cs(yhats.y_pred, scale_dict['y1'])

px.scatter(yhats, x = 'y_true', y = 'y_pred', color = 'Split', trendline="ols")

yhats['Error'] = yhats.y_true - yhats.y_pred

px.histogram(yhats, x = 'Error', color = 'Split',
             marginal="box", # can be `rug`, `violin`
             nbins= 50)

# automatically kill kernel after running. 
# This is a hacky way to free up _all_ space on the gpus
# os._exit(00)