/
cv_final.py
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cv_final.py
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#%%
import datetime
import gc
import os
HOME = os.path.dirname(os.path.abspath(__file__))
MODEL_DIR = HOME+'/models/'
DATA_DIR = HOME+'/data/'
# from mlp.mlp import *
from utils import *
from utils_js import *
from mlp.tf_models import *
from mlp.mlp import *
import random
import sys
import datatable as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from numba import njit
from sklearn.metrics import roc_auc_score
from tqdm import tqdm
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
tf.config.optimizer.set_jit(True)
device = torch.device('cpu')
# %%
'''
Various setup for different models
'''
CV_START_DAY = 401
CV_DAYS = 32
features = [f'feature_{i}' for i in range(130)]
features_t = features+ ['cross_41_42_43', 'cross_1_2']
resp_cols = ['resp_1', 'resp_2', 'resp_3', 'resp', 'resp_4']
target_cols = ['action_1', 'action_2', 'action_3', 'action', 'action_4']
resp_cols_vol = ['resp_3', 'resp', 'resp_4']
target_cols_vol = ['action_3', 'action', 'action_4']
# split features for a ResNet feature 2 is more important
features_2_index = [0, 1, 2, 3, 4, 5, 6, 15, 16, 25, 26, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
49, 50, 51, 52, 53, 54, 59, 60, 61, 62, 63, 64, 65,
66, 67, 68, 69, 70, 71, 76, 77, 82, 83, 88, 89, 94,
95, 100, 101, 106, 107, 112, 113, 118, 119, 128, 129]
features_1_index = [0] + list(set(range(130)).difference(features_2_index))
features_1 = [f'feature_{i}' for i in features_1_index]
features_2 = [f'feature_{i}' for i in features_2_index]
# split features for a ResNet feature 2 is more important
features_1_index_v = [0,
7, 8, 17, 18, 27, 28, 55, 72, 78, 84, 90, 96, 102, 108, 114, 120, 121,
11, 12, 21, 22, 31, 32, 57, 74, 80, 86, 92, 98, 104, 110, 116, 124, 125]
# resp_1 resp_2 feat
features_2_index_v = [0] + list(set(range(130)).difference(features_1_index_v))
features_1_v = [f'feature_{i}' for i in features_1_index_v]
features_2_v = [f'feature_{i}' for i in features_2_index_v]
feat_spike_index = [1, 2, 3, 4, 5, 6, 10, 14, 16, 69, 70, 71, 73, 74, 75, 76, 79, 80, 81, 82, 85,
86, 87, 88, 91, 92, 93, 94, 97, 98, 99, 100, 103, 104, 105, 106, 109, 111, 112, 115, 117, 118]
features_spike = [f'feature_{i}' for i in feat_spike_index]
cat_cols = [f+'_c' for f in features_spike]
#%%
'''
Loading model trained in tf and verify their utility scores
'''
train_parquet = os.path.join(DATA_DIR, 'train_pdm.parquet')
train = pd.read_parquet(train_parquet)
train['action'] = (train['resp'] > 0).astype(int)
for c in range(1,5):
train['action_'+str(c)] = (train['resp_'+str(c)] > 0).astype(int)
train['cross_41_42_43'] = train['feature_41'] + train['feature_42'] + train['feature_43']
train['cross_1_2'] = train['feature_1'] / (train['feature_2'] + 1e-5).astype(np.float32)
most_common_vals = np.load(DATA_DIR+'spike_common_vals_42.npy').reshape(-1)
for i, feat in tqdm(enumerate(features_spike)):
train[feat+'_c'] = (train[feat] - most_common_vals[i]).astype(np.int32)
#%%
'''
Final model resnet
'''
# model_files = ['resnet_reg_fold_0_seed_1127802.h5',
# 'resnet_reg_fold_1_seed_1127802.h5',
# 'resnet_reg_fold_2_seed_1127802.h5']
model_files = ['resnet_reg_fold_1_res_seed_792734.h5',
'resnet_reg_fold_2_res_seed_97275.h5']
# model_files = ['resnet_reg_fold_0_seed_157157.h5',
# 'resnet_reg_fold_1_seed_157157.h5',
# 'resnet_reg_fold_2_seed_157157.h5']
for _fold, model_file in enumerate(model_files):
print(f"Model {model_file}")
tf.keras.backend.clear_session()
tf_model = create_resnet_reg(len(features_1), len(features_2), len(resp_cols),
hidden_size=256, label_smoothing=5e-03)
tf_model.load_weights(os.path.join(MODEL_DIR, model_file))
# tf_model.call = tf.function(tf_model.call, experimental_relax_shapes=True)
print_valid_score_tf(train, tf_model, start_day=400, num_days=33,
f=median_avg, threshold=0.5,
feature_indices=(features, features_1_index, features_2_index))
#%%
'''
Final model resnet
'''
# model_files = ['resnet_volatile_fold_0_seed_1127802.h5',
# 'resnet_volatile_fold_1_seed_1127802.h5',
# 'resnet_volatile_fold_2_seed_1127802.h5']
# model_files = ['resnet_volatile_fold_0_seed_157157.h5',
# 'resnet_volatile_fold_1_seed_157157.h5',
# 'resnet_volatile_fold_2_seed_157157.h5']
# model_files = ['resnet_volatile_fold_0_seed_745273.h5',
# 'resnet_volatile_fold_2_seed_962656.h5']
model_files = ['resnet_volatile_fold_0_seed_5567273.h5',
'resnet_volatile_fold_1_seed_123835.h5',
'resnet_volatile_fold_2_seed_676656.h5']
for _fold, model_file in enumerate(model_files):
print(f"Model {model_file}")
tf.keras.backend.clear_session()
tf_model = create_resnet(len(features_1_v), len(features_2_v), len(resp_cols_vol),
hidden_size=256, label_smoothing=5e-03)
tf_model.load_weights(os.path.join(MODEL_DIR, model_file))
# tf_model.call = tf.function(tf_model.call, experimental_relax_shapes=True)
print_valid_score_tf(train, tf_model, start_day=400, num_days=33,
f=median_avg, threshold=0.5,
feature_indices=(features, features_1_index_v, features_2_index_v))
# %%
'''
Final model ae+mlp, 5 targets
'''
# encoder_file = 'encoder_reg.hdf5'
# model_files = ['ae_reg_fold_0.hdf5',
# 'ae_reg_fold_1.hdf5',
# 'ae_reg_fold_2.hdf5']
# hp_file = 'hp_ae_reg.pkl'
# encoder_file = 'encoder_692874.hdf5'
# model_files = ['model_692874_0.hdf5',
# 'model_692874_1.hdf5',
# 'model_692874_2.hdf5']
# hp_file = 'best_hp_692874.pkl'
encoder_file = 'ae_encoder_157157.hdf5'
model_files = ['ae_157157_0.hdf5',
'ae_157157_1.hdf5',
'ae_157157_2.hdf5']
hp_file = 'ae_hp_157157.pkl'
_, encoder = create_autoencoder(len(features), len(resp_cols), noise=0.1)
encoder.load_weights(os.path.join(MODEL_DIR, encoder_file))
encoder.trainable = False
model_fn = lambda hp: create_model(hp, len(features), len(resp_cols), encoder)
hp = pd.read_pickle(os.path.join(MODEL_DIR, hp_file))
for _fold, model_file in enumerate(model_files):
tf.keras.backend.clear_session()
print(f"Model {model_file}")
model = model_fn(hp)
model.load_weights(os.path.join(MODEL_DIR, model_files[_fold]))
print_valid_score_tf(train, model, start_day=400, num_days=33,
f=median_avg, threshold=0.5,
feature_indices=[features])
# %%
'''
Final model ae+mlp
'''
# volatile models, 3 targets
# encoder_file = 'encoder_volatile.hdf5'
# model_files = ['ae_volatile_fold_0.hdf5',
# 'ae_volatile_fold_1.hdf5',
# 'ae_volatile_fold_2.hdf5']
# hp_file = 'hp_ae_volatile.pkl'
# encoder_file = 'v_encoder_969725.hdf5'
# model_files = ['v_model_969725_0.hdf5',
# 'v_model_969725_1.hdf5',
# 'v_model_969725_2.hdf5']
# hp_file = 'v_best_hp_969725.pkl'
# encoder_file = 'v_encoder_618734.hdf5'
# model_files = ['v_model_618734_0.hdf5',
# 'v_model_618734_1.hdf5',
# 'v_model_618734_2.hdf5']
# hp_file = 'v_best_hp_618734.pkl'
encoder_file = 'ae_vol_encoder_283467.hdf5'
model_files = ['ae_vol_283467_0.hdf5',
'ae_vol_283467_1.hdf5',
'ae_vol_283467_2.hdf5']
hp_file = 'ae_vol_hp_283467.pkl'
_, encoder = create_autoencoder(len(features), len(resp_cols_vol), noise=0.1)
encoder.load_weights(os.path.join(MODEL_DIR, encoder_file))
encoder.trainable = False
model_fn = lambda hp: create_model(hp, len(features), len(resp_cols_vol), encoder)
hp = pd.read_pickle(os.path.join(MODEL_DIR, hp_file))
for _fold, model_file in enumerate(model_files):
tf.keras.backend.clear_session()
print(f"Model {model_file}")
model = model_fn(hp)
model.load_weights(os.path.join(MODEL_DIR, model_files[_fold]))
print_valid_score_tf(train, model, start_day=400, num_days=33,
f=median_avg, threshold=0.5,
feature_indices=[features])
#%%
model_files = ['tf_spike_reg_seed_1127802_fold_0.h5',
'tf_spike_reg_seed_1127802_fold_1.h5',
# 'tf_spike_reg_seed_1127802_fold_2.h5',
'tf_spike_reg_seed_802_fold_2.h5'
]
for _fold, model_file in enumerate(model_files):
print(f"Model {model_file}")
tf.keras.backend.clear_session()
tf_model = create_spikenet(len(features_1), len(features_2), len(cat_cols), len(resp_cols),
hidden_size=256, label_smoothing=5e-03)
tf_model.load_weights(os.path.join(MODEL_DIR, model_file))
# tf_model.call = tf.function(tf_model.call, experimental_relax_shapes=True)
print_valid_score_tf(train, tf_model, start_day=400, num_days=33,
f=median_avg, threshold=0.5,
feature_indices=(features, features_1_index, features_2_index, feat_spike_index))
# %%
model_files = ['emb_volatile_fold_0_util_1445_auc_0.5550.pth',
'emb_volatile_fold_1_util_1225_auc_0.5557.pth',
'emb_volatile_fold_2_util_240_auc_0.5455.pth']
for _fold, model_file in enumerate(model_files):
model = SpikeNet()
model.to(device)
model_weights = os.path.join(MODEL_DIR, model_file)
model.load_state_dict(torch.load(model_weights, map_location='cpu'))
model.eval();
print(f"\n\nModel {model_file}")
print_all_valid_score(train, model, start_day=CV_START_DAY, num_days=CV_DAYS,
batch_size =8192, f=median_avg, threshold=0.5,
target_cols=target_cols,
feat_cols=features,
resp_cols=resp_cols,
cat_input=cat_cols)
# %%
model_files = ['pt_volatile_0_util_1424_auc_0.5520.pth',
'pt_volatile_1_util_1137_auc_0.5470.pth',
'pt_volatile_2_util_322_auc_0.5444.pth']
for _fold, model_file in enumerate(model_files):
model = ResidualMLP(input_size=len(features_t), hidden_size=256, output_size=len(target_cols))
model.to(device)
model_weights = os.path.join(MODEL_DIR, model_file)
model.load_state_dict(torch.load(model_weights, map_location='cpu'))
model.eval();
print(f"\nModel {model_file}")
print_all_valid_score(train, model, start_day=CV_START_DAY, num_days=CV_DAYS,
batch_size = 8192, f=median_avg, threshold=0.5,
target_cols=target_cols,
feat_cols=features_t,
resp_cols=resp_cols)
# %%
# %%
model_files = ['final_0_util_1372_auc_0.5483.pth',
'final_1_util_865_auc_0.5450.pth',
'final_2_util_507_auc_0.5428.pth']
for _fold, model_file in enumerate(model_files):
model = ResidualMLP(input_size=len(features_t), hidden_size=256, output_size=len(target_cols))
model.to(device)
model_weights = os.path.join(MODEL_DIR, model_file)
model.load_state_dict(torch.load(model_weights, map_location='cpu'))
model.eval();
print(f"\nModel {model_file}")
print_all_valid_score(train, model, start_day=CV_START_DAY, num_days=CV_DAYS,
batch_size = 8192, f=median_avg, threshold=0.5,
target_cols=target_cols,
feat_cols=features_t,
resp_cols=resp_cols)
# %%