import os.path
#os.chdir('OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt')
import preprocessing
import pandas as pd
import time
from dev_convnet import conv_selection_parallel
import multiprocessing as mp
import itertools
import torch.nn as nn
#%% Load Data
datadir = r"OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt"
X, Y = preprocessing.get_splits(sites=["le_bray"],
datadir=os.path.join(datadir, "data"),
dataset="profound",
simulations=None)
#%% Grid search of hparams
hiddensize = [16, 64, 128, 256, 512]
batchsize = [16, 64, 128, 256, 512]
learningrate = [1e-4, 1e-3, 5e-3, 1e-2, 5e-2]
history = [5, 10, 15, 20]
channels = [[7, 14], [10, 20], [14, 28]]
kernelsize = [2, 3, 4]
activation = [nn.Sigmoid, nn.ReLU]
hp_list = [
hiddensize, batchsize, learningrate, history, channels, kernelsize,
activation
]
import os.path
import preprocessing
import pandas as pd
import time
from dev_lstm import lstm_selection_parallel
import multiprocessing as mp
import itertools
import torch
import torch.nn.functional as F
#%% Load Data
data_dir = r"OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt"
X, Y = preprocessing.get_splits(
sites=["le_bray"],
years=[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008],
datadir=os.path.join(data_dir, "data"),
dataset="profound",
simulations=None)
#%% Grid search of hparams
hiddensize = [16, 64, 128, 256, 512]
batchsize = [16, 64, 128, 256, 512]
learningrate = [1e-4, 1e-3, 5e-3, 1e-2, 5e-2]
history = [5, 10, 15, 20]
activation = [torch.sigmoid, F.relu]
hp_list = [hiddensize, batchsize, learningrate, history, activation]
epochs = 3000
splits = 6
searchsize = 50
import dev_lstm
import torch
import torch.optim as optim
import torch.nn as nn
import preprocessing
import visualizations
import models
from sklearn import metrics
import utils
import numpy as np
#%%
data_dir = "OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt"
X, Y = preprocessing.get_splits(sites=['hyytiala'],
years=[2001, 2003, 2004],
datadir=os.path.join(data_dir, "data"),
dataset="profound",
simulations=None)
X = utils.minmax_scaler(X)
#%%
fc = nn.Linear(1, 32)
latent = []
for feature in range(3):
latent.append(fc(X.unsqueeze(1)[:, :, feature]).unsqueeze(2))
latent = torch.stack(latent, dim=2).squeeze(3)
latent.shape
latent = torch.mean(latent, dim=2)
import preprocessing
import pandas as pd
import time
from dev_rf import rf_selection_parallel
import multiprocessing as mp
import itertools
import utils
#%% Load Data
data_dir = r"/home/fr/fr_fr/fr_mw263/scripts"
X, Y = preprocessing.get_splits(
sites=["bily_kriz", "collelongo", "soro"],
years=[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008],
datadir=os.path.join(data_dir, "data"),
dataset="profound",
simulations=None)
#%%
cv_splits = [5]
shuffled = [False]
n_trees = [200, 300, 400, 500]
depth = [4, 5, 6, 7]
p_list = utils.expandgrid(cv_splits, shuffled, n_trees, depth)
searchsize = len(p_list[0])
if __name__ == '__main__':
#freeze_support()
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 14 12:01:09 2020
@author: marie
"""
import sys
sys.path.append('OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt')
import preprocessing
import numpy as np
import matplotlib.pyplot as plt
#%%
X, Y = preprocessing.get_splits(
sites=["hyytiala"],
datadir="OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt\data",
dataset="profound",
to_numpy=False)
#%%
fig, ax = plt.subplots(5, figsize=(8, 9), sharex='col')
fig.suptitle("Preles input data")
for i in range(5):
ax[i].plot(X.to_numpy()[:365, i])
ax[i].set_ylabel(X.columns[i])
fig.text(0.5, 0.04, "Day of Year")
import os.path
import preprocessing
import pandas as pd
import time
from dev_lstm import lstm_selection_parallel
import multiprocessing as mp
import itertools
import torch
import torch.nn.functional as F
#%% Load Data
data_dir = r"/home/fr/fr_fr/fr_mw263"
X, Y = preprocessing.get_splits(sites = ["le_bray"],
years = [2001,2003,2004,2005,2006],
datadir = os.path.join(data_dir, "scripts/data"),
dataset = "profound",
simulations = None)
X_test, Y_test = preprocessing.get_splits(sites = ['le_bray'],
years = [2008],
datadir = os.path.join(data_dir, "scripts/data"),
dataset = "profound",
simulations = None)
#%% Grid search of hparams
hiddensize = [16, 64, 128, 256, 512]
batchsize = [16, 64, 128, 256, 512]
learningrate = [1e-4, 1e-3, 5e-3, 1e-2, 5e-2]
history = [5,10,15,20]
activation = [torch.sigmoid, F.relu]
#%% Set working directory
import os.path
import preprocessing
import pandas as pd
import time
from dev_mlp import mlp_selection_parallel
import multiprocessing as mp
import itertools
#%% Load Data
data_dir = r"OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt\python"
X, Y = preprocessing.get_splits(sites=['le_bray', 'bily_kriz', 'collelongo'],
years=[2001, 2003, 2004, 2005, 2006],
datadir=os.path.join(data_dir, "data"),
dataset="profound",
simulations=None)
X_test, Y_test = preprocessing.get_splits(
sites=['le_bray', 'bily_kriz', 'collelongo'],
years=[2008],
datadir=os.path.join(data_dir, "data"),
dataset="profound",
simulations=None)
#%% Grid search of hparams
hiddensize = [16, 64, 128, 256, 512]
batchsize = [8, 64, 128, 256, 512]
learningrate = [1e-4, 1e-3, 5e-3, 1e-2, 5e-2]
history = [0, 1, 2]
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
import preprocessing
import utils
import models
import torch.nn.functional as F
#%% Load Data
datadir = "OneDrive\Dokumente\Sc_Master\Masterthesis\Project\DomAdapt"
X, Y = preprocessing.get_splits(sites = ["hyytiala"],
years = [2001, 2002, 2003, 2004, 2005, 2006, 2007],
datadir = os.path.join(datadir, "data"),
dataset = "profound")
#x = torch.tensor(np.transpose(sims['sim1'][0])).type(dtype=torch.float)
#y = torch.tensor(np.transpose(sims['sim1'][1])).type(dtype=torch.float)
#%% Normalize features
X = utils.minmax_scaler(X)
#%% Prep data
N = 50
subset = random.sample(range(X.shape[0]), N)
X_batch, y_batch = X[subset], Y[subset]
x = torch.tensor(X_batch).type(dtype=torch.float)