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Paul-Corbalan
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# Prerequisites
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*.d
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# Object files
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*.o
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*.ko
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*.obj
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*.elf
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# Linker output
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*.ilk
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*.map
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*.exp
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# Precompiled Headers
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*.gch
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*.pch
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# Libraries
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*.lib
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*.a
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*.la
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*.lo
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# Shared objects (inc. Windows DLLs)
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*.dll
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*.so
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*.so.*
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*.dylib
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# Executables
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*.exe
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*.out
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*.app
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*.i*86
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*.x86_64
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*.hex
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# Debug files
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*.dSYM/
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*.su
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*.idb
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*.pdb
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# Kernel Module Compile Results
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*.mod*
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*.cmd
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.tmp_versions/
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modules.order
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Module.symvers
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Mkfile.old
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dkms.conf
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__pycache__
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creditcard.csv
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*.txt
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*.dot
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data/models/
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runs/
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fake_data/*
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.ipynb_checkpoints/
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*.csv
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images/
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*.pt
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*.RData
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*.Rhistory
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# Use of "Generative Adversarial Networks" for the generation of virtual patients
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## Abstract
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The use of Generative Adversarial Networks (GANs) for the generation of virtual patients is a promising approach for healthcare applications. GANs are a type of deep learning model that can generate new, realistic data that is similar to existing data. In this report, we discuss the use of GANs for the generation of virtual patients. We review the current state of the art in GANs and their applications with the Pima Indians Diabetes Database.
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We compare this work with what has been done previously with this dataset for the copulas from article *Agent-Based modeling in Medical Research. Example in Health Economics*[1].
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A practical case is discussed with the training of the Generative Adversarial Network, for several possible configurations and taking into account what is done in similar works (*Data augmentation using GANs*[2]). We then generated data with the GAN and copulas for comparison.
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Through this comparison, we observe different generated data in terms of distribution for those generated with Generative Adversarial Networks, compared to the original data. The data generated by the copulas are much closer in terms of the spread. We also conclude that both methods are currently limited in generating atypical patient data, but still efficient in generating more conventional data.
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[1] Philippe Saint-Pierre, Romain Demeulemeester, Nadège Costa, and Nicolas Savy. Agent-based modeling in medical research. example in health economics. *arXiv preprint arXiv:2205.10131*, 2022.
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[2] Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha. Data augmentation using gans. *arXiv preprint arXiv:1904.09135*, 2019.
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**Keywords:** GAN, copula, PIMA, synthetic data, virtual patients, ABM, healthcare, Artificial Intelligence (AI).
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## Dependencies
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### Python libraries
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To run Python scripts and Jupyter notebooks, please use the following command in terminal once [Anaconda](https://www.anaconda.com/) is install:
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```
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conda env create --file environment.yml
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```
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### R libraries
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To run and compile the code [Prog_R_IJB_2022](./visualisation/Prog_R_IJB_2022.rmd) in PDF format, please use the following command in R terminal before running the file:
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```
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install.packages(c("rvinecopulib", "e1071", "GGally", "caret", "MASS", "tidyverse", "corrr", "lsr", "cowplot", "EnvStats", "ggraph", "fitdistrplus", "truncdist", "truncnorm"))
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```
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import pandas as pd
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import seaborn as sns
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import numpy as np
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from scipy.stats import norm
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import ipywidgets as widgets
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import matplotlib.pyplot as plt
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import glob
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from data_treatment import DataAtts
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from IPython.display import display
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from sklearn.tree import DecisionTreeClassifier as DT
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from sklearn.tree import export_graphviz # Decision tree from sklearn
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import pydotplus # Decision tree plotting
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def compare_data (original_data, fake_data, size_of_fake, mode="save"):
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dataAtts = DataAtts(original_data)
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data = pd.read_csv(original_data)
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fake_data = pd.read_csv(fake_data).tail(size_of_fake)
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print(dataAtts.message, "\n")
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print(dataAtts.values_names[0], round(data[dataAtts.class_name].value_counts()[0]/len(data) * 100,2), '% of the dataset')
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print(dataAtts.values_names[1], round(data[dataAtts.class_name].value_counts()[1]/len(data) * 100,2), '% of the dataset')
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classes = list(data)
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for name in classes:
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if name=="Unnamed: 32":
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continue
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plt.xlabel('Values')
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plt.ylabel('Probability')
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plt.title(name + " distribution")
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real_dist = data[name].values
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fake_dist = fake_data[name].values
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plt.hist(real_dist, 50, density=True, alpha=0.5)
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plt.hist(fake_dist, 50, density=True, alpha=0.5, facecolor='r')
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if mode=="save":
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plt.savefig('fake_data/'+ dataAtts.fname + "/"+name+'_distribution.png')
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elif mode=="show":
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plt.show()
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plt.clf()
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def create_comparing_table(original_data_name, fake_data_name):
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dataAtts = DataAtts(original_data_name)
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data = pd.read_csv(original_data_name)
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fake_data = pd.read_csv(fake_data_name)
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fake_data.loc[getattr(fake_data, dataAtts.class_name) >= 0.5, dataAtts.class_name] = 1
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fake_data.loc[getattr(fake_data, dataAtts.class_name) < 0.5, dataAtts.class_name] = 0
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# Creates the training set
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training_data = [["original", data.head(int(data.shape[0]*0.7))]]
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fake_name = "fake" + str(fake_data_name).split("/")[2][0]
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training_data.append([fake_name, fake_data.head(int(fake_data.shape[0]*0.7))])
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test = data.tail(int(data.shape[0]*0.3))
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print("| Database \t| Proportion \t| Test Error \t|")
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print("| ---------\t| ---------: \t| :--------- \t|")
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for episode in training_data:
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name = episode[0]
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train = episode[1]
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try:
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positive=str(round(train[dataAtts.class_name].value_counts()[0]/len(train) * 100,2))
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except:
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positive="0"
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try:
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negative=str(round(train[dataAtts.class_name].value_counts()[1]/len(train) * 100,2))
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except:
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negative="0"
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trainX = train.drop(dataAtts.class_name, 1)
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testX = test.drop(dataAtts.class_name, 1)
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y_train = train[dataAtts.class_name]
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y_test = test[dataAtts.class_name]
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#trainX = pd.get_dummies(trainX)
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clf1 = DT(max_depth = 3, min_samples_leaf = 1)
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clf1 = clf1.fit(trainX,y_train)
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export_graphviz(clf1, out_file="models/tree.dot", feature_names=trainX.columns, class_names=["0","1"], filled=True, rounded=True)
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g = pydotplus.graph_from_dot_file(path="models/tree.dot")
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pred = clf1.predict_proba(testX)
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if pred.shape[1] > 1:
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pred = np.argmax(pred, axis=1)
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else:
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pred = pred.reshape((pred.shape[0]))
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if negative=="0":
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pred = pred-1
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mse = round(((pred - y_test.values)**2).mean(axis=0), 4)
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string="| " + name + " \t| " + positive + "/" + negative + " \t| " + str(mse) + " \t|"
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print(string)
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Results Tables\n",
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"Proportion: outcome=0/outcome=1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from data_treatment import DataAtts\n",
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"from matplotlib import pyplot as plt\n",
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"%matplotlib inline\n",
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"\n",
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"from sklearn.tree import DecisionTreeClassifier as DT\n",
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"from sklearn.tree import export_graphviz # Decision tree from sklearn\n",
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"\n",
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"import pydotplus # Decision tree plotting\n",
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"from IPython.display import Image\n",
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"\n",
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"import ipywidgets as widgets\n",
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"import glob"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"file_name = 'original_data/diabetes.csv'\n",
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"dataAtts = DataAtts(file_name) \n",
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"data = pd.read_csv(file_name)\n",
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"folder_name = file_name[14:-4]\n",
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"\n",
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"# Creates the training set\n",
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"training_data = [[\"original\", data.head(int(data.shape[0]*0.7))]]\n",
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"test = data.tail(int(data.shape[0]*0.3))\n",
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"for file in glob.glob(\"fake_data\\\\\" + folder_name + \"\\\\*.csv\"):\n",
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" name = \"fake\" + str(file).split(\"\\\\\")[2][0]\n",
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" fake_data = pd.read_csv(file)\n",
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" fake_data.loc[getattr(fake_data, dataAtts.class_name) >= 0.5, dataAtts.class_name] = 1\n",
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||||||
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" fake_data.loc[getattr(fake_data, dataAtts.class_name) < 0.5, dataAtts.class_name] = 0\n",
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" fake_training=fake_data.head(int(fake_data.shape[0]*0.7))\n",
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" training_data.append([name, fake_training])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"| Database \\t| Proportion \\t| Test Error \\t|\")\n",
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||||||
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"print(\"| ---------\\t| ---------: \\t| :--------- \\t|\")\n",
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"\n",
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"for episode in training_data:\n",
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" name = episode[0]\n",
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" train = episode[1]\n",
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" try:\n",
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" positive=str(round(train[dataAtts.class_name].value_counts()[0]/len(train) * 100,2))\n",
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" except:\n",
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" positive=\"0\"\n",
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" try:\n",
|
||||||
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" negative=str(round(train[dataAtts.class_name].value_counts()[1]/len(train) * 100,2))\n",
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||||||
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" except:\n",
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" negative=\"0\"\n",
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||||||
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" \n",
|
||||||
|
" \n",
|
||||||
|
" trainX = train.drop(dataAtts.class_name, 1)\n",
|
||||||
|
" testX = test.drop(dataAtts.class_name, 1)\n",
|
||||||
|
" y_train = train[dataAtts.class_name]\n",
|
||||||
|
" y_test = test[dataAtts.class_name]\n",
|
||||||
|
" #trainX = pd.get_dummies(trainX)\n",
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|
"\n",
|
||||||
|
" clf1 = DT(max_depth = 3, min_samples_leaf = 1)\n",
|
||||||
|
" clf1 = clf1.fit(trainX,y_train)\n",
|
||||||
|
" export_graphviz(clf1, out_file=\"models/tree.dot\", feature_names=trainX.columns, class_names=[\"0\",\"1\"], filled=True, rounded=True)\n",
|
||||||
|
" g = pydotplus.graph_from_dot_file(path=\"models/tree.dot\")\n",
|
||||||
|
"\n",
|
||||||
|
" pred = clf1.predict_proba(testX)\n",
|
||||||
|
" if pred.shape[1] > 1:\n",
|
||||||
|
" pred = np.argmax(pred, axis=1)\n",
|
||||||
|
" else:\n",
|
||||||
|
" pred = pred.reshape((pred.shape[0]))\n",
|
||||||
|
" if negative==\"0\":\n",
|
||||||
|
" pred = pred-1\n",
|
||||||
|
" \n",
|
||||||
|
" mse = round(((pred - y_test.values)**2).mean(axis=0), 4)\n",
|
||||||
|
" \n",
|
||||||
|
" string=\"| \" + name + \" \\t| \" + positive + \"/\" + negative + \" \\t| \" + str(mse) + \" \\t|\"\n",
|
||||||
|
" print(string)"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "tabular_gan",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.8 | packaged by conda-forge | (main, Nov 24 2022, 14:07:00) [MSC v.1916 64 bit (AMD64)]"
|
||||||
|
},
|
||||||
|
"vscode": {
|
||||||
|
"interpreter": {
|
||||||
|
"hash": "2f1136a7f15cd1225735fd9261403f7c342baa42a12d30e4630e4cfef11f2512"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
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@ -0,0 +1,133 @@
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|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import torch\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"from torch import nn, optim\n",
|
||||||
|
"from torch.autograd.variable import Variable\n",
|
||||||
|
"from torchvision import transforms, datasets\n",
|
||||||
|
"from data_treatment import DataSet, DataAtts\n",
|
||||||
|
"from discriminator import *\n",
|
||||||
|
"from generator import *\n",
|
||||||
|
"import ipywidgets as widgets\n",
|
||||||
|
"from IPython.display import display\n",
|
||||||
|
"import matplotlib.pyplot as plt\n",
|
||||||
|
"import glob\n",
|
||||||
|
"from format_data import *"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"folder_name = \"models/diabetes\"+\"/generator*.pt\"\n",
|
||||||
|
"model_widget = widgets.Dropdown(\n",
|
||||||
|
" options=glob.glob(folder_name),\n",
|
||||||
|
" description='Generator:',\n",
|
||||||
|
" disabled=False,\n",
|
||||||
|
")\n",
|
||||||
|
"display(model_widget)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"original_db_name = \"models/diabetes\"[7:]\n",
|
||||||
|
"original_db_path = \"original_data/\" + original_db_name + \".csv\"\n",
|
||||||
|
"original_db = pd.read_csv(original_db_path)\n",
|
||||||
|
"original_db_size=original_db.shape[0]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"try:\n",
|
||||||
|
" checkpoint= torch.load(model_widget.value, map_location='cuda')\n",
|
||||||
|
"except:\n",
|
||||||
|
" checkpoint= torch.load(model_widget.value, map_location='cpu')\n",
|
||||||
|
"checkpoint['model_attributes']['out_features'] = len(original_db.columns)\n",
|
||||||
|
"generator = GeneratorNet(**checkpoint['model_attributes'])\n",
|
||||||
|
"generator.load_state_dict(checkpoint['model_state_dict'])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"size = original_db_size\n",
|
||||||
|
"new_data = generator.create_data(size)\n",
|
||||||
|
"df = pd.DataFrame(new_data, columns=original_db.columns)\n",
|
||||||
|
"#Changes the name to be easier to read\n",
|
||||||
|
"name = model_widget.value.split(\"/\")[-1][9:-4] + \"_size-\" + str(size)\n",
|
||||||
|
"df.to_csv( \"fake_data/\" + original_db_name + \"/\" + name + \".csv\", index=False)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"#Do the same thing as the cells above but for all the files in the directory\n",
|
||||||
|
"import glob\n",
|
||||||
|
"for file in glob.glob(folder_name):\n",
|
||||||
|
" name = file.split(\"/\")[-1][9:-4]\n",
|
||||||
|
" print(name)\n",
|
||||||
|
" try:\n",
|
||||||
|
" checkpoint= torch.load(file, map_location='cuda')\n",
|
||||||
|
" except:\n",
|
||||||
|
" checkpoint= torch.load(file, map_location='cpu')\n",
|
||||||
|
" generator = GeneratorNet(**checkpoint['model_attributes'])\n",
|
||||||
|
" generator.load_state_dict(checkpoint['model_state_dict'])\n",
|
||||||
|
" size = original_db_size\n",
|
||||||
|
" new_data = generator.create_data(size)\n",
|
||||||
|
" new_data = format_output(new_data)\n",
|
||||||
|
" df = pd.DataFrame(new_data, columns=original_db.columns)\n",
|
||||||
|
" df = format_output_db(df)\n",
|
||||||
|
" name = name + \"_size-\" + str(size)\n",
|
||||||
|
" df.to_csv( \"fake_data/\" + original_db_name + \"/\" + name + \".csv\", index=False)"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "tabular_gan",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.8 | packaged by conda-forge | (main, Nov 24 2022, 14:07:00) [MSC v.1916 64 bit (AMD64)]"
|
||||||
|
},
|
||||||
|
"vscode": {
|
||||||
|
"interpreter": {
|
||||||
|
"hash": "2f1136a7f15cd1225735fd9261403f7c342baa42a12d30e4630e4cfef11f2512"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,100 @@
|
||||||
|
import torch
|
||||||
|
import pandas as pd
|
||||||
|
import os
|
||||||
|
from sklearn import preprocessing
|
||||||
|
from sklearn.utils import shuffle
|
||||||
|
from torch import nn, optim
|
||||||
|
from torch.autograd.variable import Variable
|
||||||
|
from torchvision import transforms, datasets, utils
|
||||||
|
from torch.utils.data import Dataset, DataLoader
|
||||||
|
|
||||||
|
class ToTensor(object):
|
||||||
|
"""Convert ndarrays in sample to Tensors."""
|
||||||
|
|
||||||
|
def __call__(self, sample):
|
||||||
|
# print (sample.values[2])
|
||||||
|
# print (torch.from_numpy(sample.values)[2].item())
|
||||||
|
return torch.from_numpy(sample.values)
|
||||||
|
|
||||||
|
class DataSet(Dataset):
|
||||||
|
"""Face Landmarks dataset."""
|
||||||
|
|
||||||
|
def __init__(self, csv_file, root_dir, transform=transforms.Compose([ToTensor()]), training_porcentage=0.7, shuffle_db=False):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
csv_file (string): Path to the csv file with annotations.
|
||||||
|
root_dir (string): Directory with all the images.
|
||||||
|
transform (callable, optional): Optional transform to be applied
|
||||||
|
on a sample.
|
||||||
|
"""
|
||||||
|
# self.data = pd.read_csv(csv_file).head(100000)
|
||||||
|
self.file = pd.read_csv(csv_file)
|
||||||
|
if (shuffle):
|
||||||
|
self.file = shuffle(self.file)
|
||||||
|
self.data = self.file.head(int(self.file.shape[0]*training_porcentage))
|
||||||
|
self.test_data = self.file.tail(int(self.file.shape[0]*(1-training_porcentage)))
|
||||||
|
self.root_dir = root_dir
|
||||||
|
self.transform = transform
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return len(self.data)
|
||||||
|
|
||||||
|
def __getitem__(self, idx):
|
||||||
|
item = self.data.iloc[idx]
|
||||||
|
if self.transform:
|
||||||
|
item = self.transform(item)
|
||||||
|
return item
|
||||||
|
|
||||||
|
def get_columns(self):
|
||||||
|
return self.data.columns
|
||||||
|
|
||||||
|
class DataAtts():
|
||||||
|
def __init__(self, file_name):
|
||||||
|
if file_name == "original_data/data.csv":
|
||||||
|
self.message = "Breast Cancer Wisconsin (Diagnostic) Data Set"
|
||||||
|
self.class_name = "diagnosis"
|
||||||
|
self.values_names = {0: "Benign", 1: "Malignant"}
|
||||||
|
self.class_len = 32
|
||||||
|
self.fname="data"
|
||||||
|
elif file_name == "original_data/creditcard.csv":
|
||||||
|
self.message = "Credit Card Fraud Detection"
|
||||||
|
self.class_name = "Class"
|
||||||
|
self.values_names = {0: "No Frauds", 1: "Frauds"}
|
||||||
|
self.class_len = 31
|
||||||
|
self.fname="creditcard"
|
||||||
|
elif file_name == "original_data/diabetes.csv":
|
||||||
|
self.message="Pima Indians Diabetes Database"
|
||||||
|
self.class_name = "Outcome"
|
||||||
|
self.values_names = {0: "Normal", 1: "Diabets"}
|
||||||
|
self.class_len = 9
|
||||||
|
self.fname="diabetes"
|
||||||
|
|
||||||
|
elif file_name == "original_data/data_escalonated.csv":
|
||||||
|
self.message = "Breast Cancer Wisconsin (Diagnostic) Data Set eSCALONATED"
|
||||||
|
self.class_name = "diagnosis"
|
||||||
|
self.values_names = {0: "Benign", 1: "Malignant"}
|
||||||
|
self.class_len = 32
|
||||||
|
self.fname="data_escalonated"
|
||||||
|
elif file_name == "original_data/creditcard_escalonated.csv":
|
||||||
|
self.message = "Credit Card Fraud Detection eSCALONATED"
|
||||||
|
self.class_name = "Class"
|
||||||
|
self.values_names = {0: "No Frauds", 1: "Frauds"}
|
||||||
|
self.class_len = 31
|
||||||
|
self.fname="creditcard_escalonated"
|
||||||
|
elif file_name == "original_data/diabetes_escalonated.csv":
|
||||||
|
self.message="Pima Indians Diabetes Database eSCALONATED"
|
||||||
|
self.class_name = "Outcome"
|
||||||
|
self.values_names = {0: "Normal", 1: "Diabets"}
|
||||||
|
self.class_len = 9
|
||||||
|
self.fname="diabetes_escalonated"
|
||||||
|
elif file_name == "original_data/creditcard_1s_escalonated.csv":
|
||||||
|
self.message = "Credit Card Fraud Detection eSCALONATED"
|
||||||
|
self.class_name = "Class"
|
||||||
|
self.values_names = {0: "No Frauds", 1: "Frauds"}
|
||||||
|
self.class_len = 31
|
||||||
|
self.fname="creditcard_1s_escalonated"
|
||||||
|
else:
|
||||||
|
print("File not found, exiting")
|
||||||
|
exit(1)
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,60 @@
|
||||||
|
import torch
|
||||||
|
from torch import nn, optim
|
||||||
|
from torch.autograd.variable import Variable
|
||||||
|
from torchvision import transforms, datasets
|
||||||
|
from utils import real_data_target, fake_data_target
|
||||||
|
|
||||||
|
class DiscriminatorNet(torch.nn.Module):
|
||||||
|
"""
|
||||||
|
A three hidden-layer discriminative neural network
|
||||||
|
"""
|
||||||
|
def __init__(self, in_features, leakyRelu=0.2, dropout=0.3, hidden_layers=[1024, 512, 256]):
|
||||||
|
super(DiscriminatorNet, self).__init__()
|
||||||
|
|
||||||
|
out_features = 1
|
||||||
|
self.layers = hidden_layers.copy()
|
||||||
|
self.layers.insert(0, in_features)
|
||||||
|
|
||||||
|
for count in range(0, len(self.layers)-1):
|
||||||
|
self.add_module("hidden_" + str(count),
|
||||||
|
nn.Sequential(
|
||||||
|
nn.Linear(self.layers[count], self.layers[count+1]),
|
||||||
|
nn.LeakyReLU(leakyRelu),
|
||||||
|
nn.Dropout(dropout)
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
self.add_module("out",
|
||||||
|
nn.Sequential(
|
||||||
|
nn.Linear(self.layers[-1], out_features),
|
||||||
|
torch.nn.Sigmoid()
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
for name, module in self.named_children():
|
||||||
|
x = module(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
# train_discriminator(d_optimizer, discriminator, loss, real_data, fake_data)
|
||||||
|
def train_discriminator(optimizer, discriminator, loss, real_data, fake_data):
|
||||||
|
# Reset gradients
|
||||||
|
optimizer.zero_grad()
|
||||||
|
|
||||||
|
# 1.1 Train on Real Data
|
||||||
|
prediction_real = discriminator(real_data)
|
||||||
|
# Calculate error and backpropagate
|
||||||
|
error_real = loss(prediction_real, real_data_target(real_data.size(0)))
|
||||||
|
error_real.backward()
|
||||||
|
|
||||||
|
# 1.2 Train on Fake Data
|
||||||
|
prediction_fake = discriminator(fake_data)
|
||||||
|
# Calculate error and backpropagate
|
||||||
|
error_fake = loss(prediction_fake, fake_data_target(real_data.size(0)))
|
||||||
|
error_fake.backward()
|
||||||
|
|
||||||
|
# 1.3 Update weights with gradients
|
||||||
|
optimizer.step()
|
||||||
|
|
||||||
|
# Return error
|
||||||
|
return error_real + error_fake, prediction_real, prediction_fake
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,127 @@
|
||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Fake Data Analysis"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import numpy as np\n",
|
||||||
|
"from scipy.stats import norm\n",
|
||||||
|
"from data_treatment import DataAtts\n",
|
||||||
|
"import ipywidgets as widgets\n",
|
||||||
|
"import matplotlib.pyplot as plt\n",
|
||||||
|
"import glob\n",
|
||||||
|
"from compare_data import *\n",
|
||||||
|
"from IPython.display import display"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"folder_name = 'original_data/diabetes.csv'[14:-4]\n",
|
||||||
|
"fake_files_dropdown = widgets.Dropdown(\n",
|
||||||
|
" options=glob.glob(\"fake_data/\" + folder_name + \"/*.csv\"),\n",
|
||||||
|
" description='Fake file:',\n",
|
||||||
|
" disabled=False,\n",
|
||||||
|
")\n",
|
||||||
|
"display(fake_files_dropdown)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"file_name='original_data/diabetes.csv'\n",
|
||||||
|
"dataAtts = DataAtts(file_name)\n",
|
||||||
|
" \n",
|
||||||
|
" \n",
|
||||||
|
"data = pd.read_csv(file_name)\n",
|
||||||
|
"fake_data = pd.read_csv(fake_files_dropdown.value)\n",
|
||||||
|
"fake_data.loc[getattr(fake_data, dataAtts.class_name) >= 0.5, dataAtts.class_name] = 1\n",
|
||||||
|
"fake_data.loc[getattr(fake_data, dataAtts.class_name) < 0.5, dataAtts.class_name] = 0\n",
|
||||||
|
"\n",
|
||||||
|
"print(dataAtts.message)\n",
|
||||||
|
"print(dataAtts.values_names[0], round(data[dataAtts.class_name].value_counts()[0]/len(data) * 100,2), '% of the dataset')\n",
|
||||||
|
"print(dataAtts.values_names[1], round(data[dataAtts.class_name].value_counts()[1]/len(data) * 100,2), '% of the dataset')\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\nFake Data\")\n",
|
||||||
|
"try:\n",
|
||||||
|
" positive=str(round(fake_data[dataAtts.class_name].value_counts()[0]/len(fake_data) * 100,2))\n",
|
||||||
|
"except:\n",
|
||||||
|
" positive=\"0\"\n",
|
||||||
|
"try:\n",
|
||||||
|
" negative=str(round(fake_data[dataAtts.class_name].value_counts()[1]/len(fake_data) * 100,2))\n",
|
||||||
|
"except:\n",
|
||||||
|
" negative=\"0\"\n",
|
||||||
|
" \n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Outcome = 0: \", positive, '% of the dataset')\n",
|
||||||
|
"print(\"Outcome = 1: \", negative, '% of the dataset')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"classes = list(data)\n",
|
||||||
|
"\n",
|
||||||
|
"for name in classes:\n",
|
||||||
|
" if name==\"Unnamed: 32\":\n",
|
||||||
|
" continue\n",
|
||||||
|
" \n",
|
||||||
|
" plt.xlabel('Values')\n",
|
||||||
|
" plt.ylabel('Probability')\n",
|
||||||
|
" plt.title(name + \" distribution\")\n",
|
||||||
|
" real_dist = data[name].values\n",
|
||||||
|
" fake_dist = fake_data[name].values\n",
|
||||||
|
" plt.hist(real_dist, 50, density=True, alpha=0.5)\n",
|
||||||
|
" plt.hist(fake_dist, 50, density=True, alpha=0.5, facecolor='r')\n",
|
||||||
|
" #plt.savefig('fake_data/'+ dataAtts.fname + \"/\"+name+'_distribution.png')\n",
|
||||||
|
" plt.show()\n",
|
||||||
|
" plt.clf()\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "tabular_gan",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.8 | packaged by conda-forge | (main, Nov 24 2022, 14:07:00) [MSC v.1916 64 bit (AMD64)]"
|
||||||
|
},
|
||||||
|
"vscode": {
|
||||||
|
"interpreter": {
|
||||||
|
"hash": "2f1136a7f15cd1225735fd9261403f7c342baa42a12d30e4630e4cfef11f2512"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,18 @@
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
def format_output(x):
|
||||||
|
x[:, 0] = np.rint(np.maximum(0, x[:, 0]))
|
||||||
|
x[:, 1] = np.rint(np.maximum(0, x[:, 1]))
|
||||||
|
x[:, 2] = np.rint(np.maximum(0, x[:, 2]))
|
||||||
|
x[:, 3] = np.rint(np.maximum(0, x[:, 3]))
|
||||||
|
x[:, 4] = np.rint(np.maximum(0, x[:, 4]))
|
||||||
|
x[:, 5] = np.maximum(0, x[:, 5])
|
||||||
|
x[:, 6] = np.maximum(0, x[:, 6])
|
||||||
|
x[:, 7] = np.rint(np.maximum(0, x[:, 7]))
|
||||||
|
x[:, 8] = (x[:, 8]>=0.5)
|
||||||
|
return x
|
||||||
|
|
||||||
|
def format_output_db(db):
|
||||||
|
return db.astype({"Pregnancies": int, "Glucose": int, "BloodPressure": int, "SkinThickness": int, "Insulin": int, "BMI": float, "DiabetesPedigreeFunction": float, "Age": int, "Outcome": int})
|
||||||
|
|
@ -0,0 +1,68 @@
|
||||||
|
import torch
|
||||||
|
from torch import nn, optim
|
||||||
|
from torch.autograd.variable import Variable
|
||||||
|
from torchvision import transforms, datasets
|
||||||
|
from utils import real_data_target
|
||||||
|
|
||||||
|
def noise(quantity, size):
|
||||||
|
return Variable(torch.randn(quantity, size))
|
||||||
|
|
||||||
|
class GeneratorNet(torch.nn.Module):
|
||||||
|
"""
|
||||||
|
A three hidden-layer generative neural network
|
||||||
|
"""
|
||||||
|
def __init__(self, out_features, leakyRelu=0.2, hidden_layers=[256, 512, 1024], in_features=100, escalonate=False):
|
||||||
|
super(GeneratorNet, self).__init__()
|
||||||
|
|
||||||
|
self.in_features = in_features
|
||||||
|
self.layers = hidden_layers.copy()
|
||||||
|
self.layers.insert(0, self.in_features)
|
||||||
|
|
||||||
|
for count in range(0, len(self.layers)-1):
|
||||||
|
self.add_module("hidden_" + str(count),
|
||||||
|
nn.Sequential(
|
||||||
|
nn.Linear(self.layers[count], self.layers[count+1]),
|
||||||
|
nn.LeakyReLU(leakyRelu)
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
if not escalonate:
|
||||||
|
self.add_module("out",
|
||||||
|
nn.Sequential(
|
||||||
|
nn.Linear(self.layers[-1], out_features)
|
||||||
|
)
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
self.add_module("out",
|
||||||
|
nn.Sequential(
|
||||||
|
nn.Linear(self.layers[-1], out_features),
|
||||||
|
escalonate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
for name, module in self.named_children():
|
||||||
|
x = module(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
def create_data(self, quantity):
|
||||||
|
points = noise(quantity, self.in_features)
|
||||||
|
try:
|
||||||
|
data=self.forward(points.cuda())
|
||||||
|
except RuntimeError:
|
||||||
|
data=self.forward(points.cpu())
|
||||||
|
return data.detach().numpy()
|
||||||
|
|
||||||
|
def train_generator(optimizer, discriminator, loss, fake_data):
|
||||||
|
# 2. Train Generator
|
||||||
|
# Reset gradients
|
||||||
|
optimizer.zero_grad()
|
||||||
|
# Sample noise and generate fake data
|
||||||
|
prediction = discriminator(fake_data)
|
||||||
|
# Calculate error and backpropagate
|
||||||
|
error = loss(prediction, real_data_target(prediction.size(0)))
|
||||||
|
error.backward()
|
||||||
|
# Update weights with gradients
|
||||||
|
optimizer.step()
|
||||||
|
# Return error
|
||||||
|
return error
|
||||||
|
|
@ -0,0 +1,769 @@
|
||||||
|
Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
|
||||||
|
6,148,72,35,0,33.6,0.627,50,1
|
||||||
|
1,85,66,29,0,26.6,0.351,31,0
|
||||||
|
8,183,64,0,0,23.3,0.672,32,1
|
||||||
|
1,89,66,23,94,28.1,0.167,21,0
|
||||||
|
0,137,40,35,168,43.1,2.288,33,1
|
||||||
|
5,116,74,0,0,25.6,0.201,30,0
|
||||||
|
3,78,50,32,88,31,0.248,26,1
|
||||||
|
10,115,0,0,0,35.3,0.134,29,0
|
||||||
|
2,197,70,45,543,30.5,0.158,53,1
|
||||||
|
8,125,96,0,0,0,0.232,54,1
|
||||||
|
4,110,92,0,0,37.6,0.191,30,0
|
||||||
|
10,168,74,0,0,38,0.537,34,1
|
||||||
|
10,139,80,0,0,27.1,1.441,57,0
|
||||||
|
1,189,60,23,846,30.1,0.398,59,1
|
||||||
|
5,166,72,19,175,25.8,0.587,51,1
|
||||||
|
7,100,0,0,0,30,0.484,32,1
|
||||||
|
0,118,84,47,230,45.8,0.551,31,1
|
||||||
|
7,107,74,0,0,29.6,0.254,31,1
|
||||||
|
1,103,30,38,83,43.3,0.183,33,0
|
||||||
|
1,115,70,30,96,34.6,0.529,32,1
|
||||||
|
3,126,88,41,235,39.3,0.704,27,0
|
||||||
|
8,99,84,0,0,35.4,0.388,50,0
|
||||||
|
7,196,90,0,0,39.8,0.451,41,1
|
||||||
|
9,119,80,35,0,29,0.263,29,1
|
||||||
|
11,143,94,33,146,36.6,0.254,51,1
|
||||||
|
10,125,70,26,115,31.1,0.205,41,1
|
||||||
|
7,147,76,0,0,39.4,0.257,43,1
|
||||||
|
1,97,66,15,140,23.2,0.487,22,0
|
||||||
|
13,145,82,19,110,22.2,0.245,57,0
|
||||||
|
5,117,92,0,0,34.1,0.337,38,0
|
||||||
|
5,109,75,26,0,36,0.546,60,0
|
||||||
|
3,158,76,36,245,31.6,0.851,28,1
|
||||||
|
3,88,58,11,54,24.8,0.267,22,0
|
||||||
|
6,92,92,0,0,19.9,0.188,28,0
|
||||||
|
10,122,78,31,0,27.6,0.512,45,0
|
||||||
|
4,103,60,33,192,24,0.966,33,0
|
||||||
|
11,138,76,0,0,33.2,0.42,35,0
|
||||||
|
9,102,76,37,0,32.9,0.665,46,1
|
||||||
|
2,90,68,42,0,38.2,0.503,27,1
|
||||||
|
4,111,72,47,207,37.1,1.39,56,1
|
||||||
|
3,180,64,25,70,34,0.271,26,0
|
||||||
|
7,133,84,0,0,40.2,0.696,37,0
|
||||||
|
7,106,92,18,0,22.7,0.235,48,0
|
||||||
|
9,171,110,24,240,45.4,0.721,54,1
|
||||||
|
7,159,64,0,0,27.4,0.294,40,0
|
||||||
|
0,180,66,39,0,42,1.893,25,1
|
||||||
|
1,146,56,0,0,29.7,0.564,29,0
|
||||||
|
2,71,70,27,0,28,0.586,22,0
|
||||||
|
7,103,66,32,0,39.1,0.344,31,1
|
||||||
|
7,105,0,0,0,0,0.305,24,0
|
||||||
|
1,103,80,11,82,19.4,0.491,22,0
|
||||||
|
1,101,50,15,36,24.2,0.526,26,0
|
||||||
|
5,88,66,21,23,24.4,0.342,30,0
|
||||||
|
8,176,90,34,300,33.7,0.467,58,1
|
||||||
|
7,150,66,42,342,34.7,0.718,42,0
|
||||||
|
1,73,50,10,0,23,0.248,21,0
|
||||||
|
7,187,68,39,304,37.7,0.254,41,1
|
||||||
|
0,100,88,60,110,46.8,0.962,31,0
|
||||||
|
0,146,82,0,0,40.5,1.781,44,0
|
||||||
|
0,105,64,41,142,41.5,0.173,22,0
|
||||||
|
2,84,0,0,0,0,0.304,21,0
|
||||||
|
8,133,72,0,0,32.9,0.27,39,1
|
||||||
|
5,44,62,0,0,25,0.587,36,0
|
||||||
|
2,141,58,34,128,25.4,0.699,24,0
|
||||||
|
7,114,66,0,0,32.8,0.258,42,1
|
||||||
|
5,99,74,27,0,29,0.203,32,0
|
||||||
|
0,109,88,30,0,32.5,0.855,38,1
|
||||||
|
2,109,92,0,0,42.7,0.845,54,0
|
||||||
|
1,95,66,13,38,19.6,0.334,25,0
|
||||||
|
4,146,85,27,100,28.9,0.189,27,0
|
||||||
|
2,100,66,20,90,32.9,0.867,28,1
|
||||||
|
5,139,64,35,140,28.6,0.411,26,0
|
||||||
|
13,126,90,0,0,43.4,0.583,42,1
|
||||||
|
4,129,86,20,270,35.1,0.231,23,0
|
||||||
|
1,79,75,30,0,32,0.396,22,0
|
||||||
|
1,0,48,20,0,24.7,0.14,22,0
|
||||||
|
7,62,78,0,0,32.6,0.391,41,0
|
||||||
|
5,95,72,33,0,37.7,0.37,27,0
|
||||||
|
0,131,0,0,0,43.2,0.27,26,1
|
||||||
|
2,112,66,22,0,25,0.307,24,0
|
||||||
|
3,113,44,13,0,22.4,0.14,22,0
|
||||||
|
2,74,0,0,0,0,0.102,22,0
|
||||||
|
7,83,78,26,71,29.3,0.767,36,0
|
||||||
|
0,101,65,28,0,24.6,0.237,22,0
|
||||||
|
5,137,108,0,0,48.8,0.227,37,1
|
||||||
|
2,110,74,29,125,32.4,0.698,27,0
|
||||||
|
13,106,72,54,0,36.6,0.178,45,0
|
||||||
|
2,100,68,25,71,38.5,0.324,26,0
|
||||||
|
15,136,70,32,110,37.1,0.153,43,1
|
||||||
|
1,107,68,19,0,26.5,0.165,24,0
|
||||||
|
1,80,55,0,0,19.1,0.258,21,0
|
||||||
|
4,123,80,15,176,32,0.443,34,0
|
||||||
|
7,81,78,40,48,46.7,0.261,42,0
|
||||||
|
4,134,72,0,0,23.8,0.277,60,1
|
||||||
|
2,142,82,18,64,24.7,0.761,21,0
|
||||||
|
6,144,72,27,228,33.9,0.255,40,0
|
||||||
|
2,92,62,28,0,31.6,0.13,24,0
|
||||||
|
1,71,48,18,76,20.4,0.323,22,0
|
||||||
|
6,93,50,30,64,28.7,0.356,23,0
|
||||||
|
1,122,90,51,220,49.7,0.325,31,1
|
||||||
|
1,163,72,0,0,39,1.222,33,1
|
||||||
|
1,151,60,0,0,26.1,0.179,22,0
|
||||||
|
0,125,96,0,0,22.5,0.262,21,0
|
||||||
|
1,81,72,18,40,26.6,0.283,24,0
|
||||||
|
2,85,65,0,0,39.6,0.93,27,0
|
||||||
|
1,126,56,29,152,28.7,0.801,21,0
|
||||||
|
1,96,122,0,0,22.4,0.207,27,0
|
||||||
|
4,144,58,28,140,29.5,0.287,37,0
|
||||||
|
3,83,58,31,18,34.3,0.336,25,0
|
||||||
|
0,95,85,25,36,37.4,0.247,24,1
|
||||||
|
3,171,72,33,135,33.3,0.199,24,1
|
||||||
|
8,155,62,26,495,34,0.543,46,1
|
||||||
|
1,89,76,34,37,31.2,0.192,23,0
|
||||||
|
4,76,62,0,0,34,0.391,25,0
|
||||||
|
7,160,54,32,175,30.5,0.588,39,1
|
||||||
|
4,146,92,0,0,31.2,0.539,61,1
|
||||||
|
5,124,74,0,0,34,0.22,38,1
|
||||||
|
5,78,48,0,0,33.7,0.654,25,0
|
||||||
|
4,97,60,23,0,28.2,0.443,22,0
|
||||||
|
4,99,76,15,51,23.2,0.223,21,0
|
||||||
|
0,162,76,56,100,53.2,0.759,25,1
|
||||||
|
6,111,64,39,0,34.2,0.26,24,0
|
||||||
|
2,107,74,30,100,33.6,0.404,23,0
|
||||||
|
5,132,80,0,0,26.8,0.186,69,0
|
||||||
|
0,113,76,0,0,33.3,0.278,23,1
|
||||||
|
1,88,30,42,99,55,0.496,26,1
|
||||||
|
3,120,70,30,135,42.9,0.452,30,0
|
||||||
|
1,118,58,36,94,33.3,0.261,23,0
|
||||||
|
1,117,88,24,145,34.5,0.403,40,1
|
||||||
|
0,105,84,0,0,27.9,0.741,62,1
|
||||||
|
4,173,70,14,168,29.7,0.361,33,1
|
||||||
|
9,122,56,0,0,33.3,1.114,33,1
|
||||||
|
3,170,64,37,225,34.5,0.356,30,1
|
||||||
|
8,84,74,31,0,38.3,0.457,39,0
|
||||||
|
2,96,68,13,49,21.1,0.647,26,0
|
||||||
|
2,125,60,20,140,33.8,0.088,31,0
|
||||||
|
0,100,70,26,50,30.8,0.597,21,0
|
||||||
|
0,93,60,25,92,28.7,0.532,22,0
|
||||||
|
0,129,80,0,0,31.2,0.703,29,0
|
||||||
|
5,105,72,29,325,36.9,0.159,28,0
|
||||||
|
3,128,78,0,0,21.1,0.268,55,0
|
||||||
|
5,106,82,30,0,39.5,0.286,38,0
|
||||||
|
2,108,52,26,63,32.5,0.318,22,0
|
||||||
|
10,108,66,0,0,32.4,0.272,42,1
|
||||||
|
4,154,62,31,284,32.8,0.237,23,0
|
||||||
|
0,102,75,23,0,0,0.572,21,0
|
||||||
|
9,57,80,37,0,32.8,0.096,41,0
|
||||||
|
2,106,64,35,119,30.5,1.4,34,0
|
||||||
|
5,147,78,0,0,33.7,0.218,65,0
|
||||||
|
2,90,70,17,0,27.3,0.085,22,0
|
||||||
|
1,136,74,50,204,37.4,0.399,24,0
|
||||||
|
4,114,65,0,0,21.9,0.432,37,0
|
||||||
|
9,156,86,28,155,34.3,1.189,42,1
|
||||||
|
1,153,82,42,485,40.6,0.687,23,0
|
||||||
|
8,188,78,0,0,47.9,0.137,43,1
|
||||||
|
7,152,88,44,0,50,0.337,36,1
|
||||||
|
2,99,52,15,94,24.6,0.637,21,0
|
||||||
|
1,109,56,21,135,25.2,0.833,23,0
|
||||||
|
2,88,74,19,53,29,0.229,22,0
|
||||||
|
17,163,72,41,114,40.9,0.817,47,1
|
||||||
|
4,151,90,38,0,29.7,0.294,36,0
|
||||||
|
7,102,74,40,105,37.2,0.204,45,0
|
||||||
|
0,114,80,34,285,44.2,0.167,27,0
|
||||||
|
2,100,64,23,0,29.7,0.368,21,0
|
||||||
|
0,131,88,0,0,31.6,0.743,32,1
|
||||||
|
6,104,74,18,156,29.9,0.722,41,1
|
||||||
|
3,148,66,25,0,32.5,0.256,22,0
|
||||||
|
4,120,68,0,0,29.6,0.709,34,0
|
||||||
|
4,110,66,0,0,31.9,0.471,29,0
|
||||||
|
3,111,90,12,78,28.4,0.495,29,0
|
||||||
|
6,102,82,0,0,30.8,0.18,36,1
|
||||||
|
6,134,70,23,130,35.4,0.542,29,1
|
||||||
|
2,87,0,23,0,28.9,0.773,25,0
|
||||||
|
1,79,60,42,48,43.5,0.678,23,0
|
||||||
|
2,75,64,24,55,29.7,0.37,33,0
|
||||||
|
8,179,72,42,130,32.7,0.719,36,1
|
||||||
|
6,85,78,0,0,31.2,0.382,42,0
|
||||||
|
0,129,110,46,130,67.1,0.319,26,1
|
||||||
|
5,143,78,0,0,45,0.19,47,0
|
||||||
|
5,130,82,0,0,39.1,0.956,37,1
|
||||||
|
6,87,80,0,0,23.2,0.084,32,0
|
||||||
|
0,119,64,18,92,34.9,0.725,23,0
|
||||||
|
1,0,74,20,23,27.7,0.299,21,0
|
||||||
|
5,73,60,0,0,26.8,0.268,27,0
|
||||||
|
4,141,74,0,0,27.6,0.244,40,0
|
||||||
|
7,194,68,28,0,35.9,0.745,41,1
|
||||||
|
8,181,68,36,495,30.1,0.615,60,1
|
||||||
|
1,128,98,41,58,32,1.321,33,1
|
||||||
|
8,109,76,39,114,27.9,0.64,31,1
|
||||||
|
5,139,80,35,160,31.6,0.361,25,1
|
||||||
|
3,111,62,0,0,22.6,0.142,21,0
|
||||||
|
9,123,70,44,94,33.1,0.374,40,0
|
||||||
|
7,159,66,0,0,30.4,0.383,36,1
|
||||||
|
11,135,0,0,0,52.3,0.578,40,1
|
||||||
|
8,85,55,20,0,24.4,0.136,42,0
|
||||||
|
5,158,84,41,210,39.4,0.395,29,1
|
||||||
|
1,105,58,0,0,24.3,0.187,21,0
|
||||||
|
3,107,62,13,48,22.9,0.678,23,1
|
||||||
|
4,109,64,44,99,34.8,0.905,26,1
|
||||||
|
4,148,60,27,318,30.9,0.15,29,1
|
||||||
|
0,113,80,16,0,31,0.874,21,0
|
||||||
|
1,138,82,0,0,40.1,0.236,28,0
|
||||||
|
0,108,68,20,0,27.3,0.787,32,0
|
||||||
|
2,99,70,16,44,20.4,0.235,27,0
|
||||||
|
6,103,72,32,190,37.7,0.324,55,0
|
||||||
|
5,111,72,28,0,23.9,0.407,27,0
|
||||||
|
8,196,76,29,280,37.5,0.605,57,1
|
||||||
|
5,162,104,0,0,37.7,0.151,52,1
|
||||||
|
1,96,64,27,87,33.2,0.289,21,0
|
||||||
|
7,184,84,33,0,35.5,0.355,41,1
|
||||||
|
2,81,60,22,0,27.7,0.29,25,0
|
||||||
|
0,147,85,54,0,42.8,0.375,24,0
|
||||||
|
7,179,95,31,0,34.2,0.164,60,0
|
||||||
|
0,140,65,26,130,42.6,0.431,24,1
|
||||||
|
9,112,82,32,175,34.2,0.26,36,1
|
||||||
|
12,151,70,40,271,41.8,0.742,38,1
|
||||||
|
5,109,62,41,129,35.8,0.514,25,1
|
||||||
|
6,125,68,30,120,30,0.464,32,0
|
||||||
|
5,85,74,22,0,29,1.224,32,1
|
||||||
|
5,112,66,0,0,37.8,0.261,41,1
|
||||||
|
0,177,60,29,478,34.6,1.072,21,1
|
||||||
|
2,158,90,0,0,31.6,0.805,66,1
|
||||||
|
7,119,0,0,0,25.2,0.209,37,0
|
||||||
|
7,142,60,33,190,28.8,0.687,61,0
|
||||||
|
1,100,66,15,56,23.6,0.666,26,0
|
||||||
|
1,87,78,27,32,34.6,0.101,22,0
|
||||||
|
0,101,76,0,0,35.7,0.198,26,0
|
||||||
|
3,162,52,38,0,37.2,0.652,24,1
|
||||||
|
4,197,70,39,744,36.7,2.329,31,0
|
||||||
|
0,117,80,31,53,45.2,0.089,24,0
|
||||||
|
4,142,86,0,0,44,0.645,22,1
|
||||||
|
6,134,80,37,370,46.2,0.238,46,1
|
||||||
|
1,79,80,25,37,25.4,0.583,22,0
|
||||||
|
4,122,68,0,0,35,0.394,29,0
|
||||||
|
3,74,68,28,45,29.7,0.293,23,0
|
||||||
|
4,171,72,0,0,43.6,0.479,26,1
|
||||||
|
7,181,84,21,192,35.9,0.586,51,1
|
||||||
|
0,179,90,27,0,44.1,0.686,23,1
|
||||||
|
9,164,84,21,0,30.8,0.831,32,1
|
||||||
|
0,104,76,0,0,18.4,0.582,27,0
|
||||||
|
1,91,64,24,0,29.2,0.192,21,0
|
||||||
|
4,91,70,32,88,33.1,0.446,22,0
|
||||||
|
3,139,54,0,0,25.6,0.402,22,1
|
||||||
|
6,119,50,22,176,27.1,1.318,33,1
|
||||||
|
2,146,76,35,194,38.2,0.329,29,0
|
||||||
|
9,184,85,15,0,30,1.213,49,1
|
||||||
|
10,122,68,0,0,31.2,0.258,41,0
|
||||||
|
0,165,90,33,680,52.3,0.427,23,0
|
||||||
|
9,124,70,33,402,35.4,0.282,34,0
|
||||||
|
1,111,86,19,0,30.1,0.143,23,0
|
||||||
|
9,106,52,0,0,31.2,0.38,42,0
|
||||||
|
2,129,84,0,0,28,0.284,27,0
|
||||||
|
2,90,80,14,55,24.4,0.249,24,0
|
||||||
|
0,86,68,32,0,35.8,0.238,25,0
|
||||||
|
12,92,62,7,258,27.6,0.926,44,1
|
||||||
|
1,113,64,35,0,33.6,0.543,21,1
|
||||||
|
3,111,56,39,0,30.1,0.557,30,0
|
||||||
|
2,114,68,22,0,28.7,0.092,25,0
|
||||||
|
1,193,50,16,375,25.9,0.655,24,0
|
||||||
|
11,155,76,28,150,33.3,1.353,51,1
|
||||||
|
3,191,68,15,130,30.9,0.299,34,0
|
||||||
|
3,141,0,0,0,30,0.761,27,1
|
||||||
|
4,95,70,32,0,32.1,0.612,24,0
|
||||||
|
3,142,80,15,0,32.4,0.2,63,0
|
||||||
|
4,123,62,0,0,32,0.226,35,1
|
||||||
|
5,96,74,18,67,33.6,0.997,43,0
|
||||||
|
0,138,0,0,0,36.3,0.933,25,1
|
||||||
|
2,128,64,42,0,40,1.101,24,0
|
||||||
|
0,102,52,0,0,25.1,0.078,21,0
|
||||||
|
2,146,0,0,0,27.5,0.24,28,1
|
||||||
|
10,101,86,37,0,45.6,1.136,38,1
|
||||||
|
2,108,62,32,56,25.2,0.128,21,0
|
||||||
|
3,122,78,0,0,23,0.254,40,0
|
||||||
|
1,71,78,50,45,33.2,0.422,21,0
|
||||||
|
13,106,70,0,0,34.2,0.251,52,0
|
||||||
|
2,100,70,52,57,40.5,0.677,25,0
|
||||||
|
7,106,60,24,0,26.5,0.296,29,1
|
||||||
|
0,104,64,23,116,27.8,0.454,23,0
|
||||||
|
5,114,74,0,0,24.9,0.744,57,0
|
||||||
|
2,108,62,10,278,25.3,0.881,22,0
|
||||||
|
0,146,70,0,0,37.9,0.334,28,1
|
||||||
|
10,129,76,28,122,35.9,0.28,39,0
|
||||||
|
7,133,88,15,155,32.4,0.262,37,0
|
||||||
|
7,161,86,0,0,30.4,0.165,47,1
|
||||||
|
2,108,80,0,0,27,0.259,52,1
|
||||||
|
7,136,74,26,135,26,0.647,51,0
|
||||||
|
5,155,84,44,545,38.7,0.619,34,0
|
||||||
|
1,119,86,39,220,45.6,0.808,29,1
|
||||||
|
4,96,56,17,49,20.8,0.34,26,0
|
||||||
|
5,108,72,43,75,36.1,0.263,33,0
|
||||||
|
0,78,88,29,40,36.9,0.434,21,0
|
||||||
|
0,107,62,30,74,36.6,0.757,25,1
|
||||||
|
2,128,78,37,182,43.3,1.224,31,1
|
||||||
|
1,128,48,45,194,40.5,0.613,24,1
|
||||||
|
0,161,50,0,0,21.9,0.254,65,0
|
||||||
|
6,151,62,31,120,35.5,0.692,28,0
|
||||||
|
2,146,70,38,360,28,0.337,29,1
|
||||||
|
0,126,84,29,215,30.7,0.52,24,0
|
||||||
|
14,100,78,25,184,36.6,0.412,46,1
|
||||||
|
8,112,72,0,0,23.6,0.84,58,0
|
||||||
|
0,167,0,0,0,32.3,0.839,30,1
|
||||||
|
2,144,58,33,135,31.6,0.422,25,1
|
||||||
|
5,77,82,41,42,35.8,0.156,35,0
|
||||||
|
5,115,98,0,0,52.9,0.209,28,1
|
||||||
|
3,150,76,0,0,21,0.207,37,0
|
||||||
|
2,120,76,37,105,39.7,0.215,29,0
|
||||||
|
10,161,68,23,132,25.5,0.326,47,1
|
||||||
|
0,137,68,14,148,24.8,0.143,21,0
|
||||||
|
0,128,68,19,180,30.5,1.391,25,1
|
||||||
|
2,124,68,28,205,32.9,0.875,30,1
|
||||||
|
6,80,66,30,0,26.2,0.313,41,0
|
||||||
|
0,106,70,37,148,39.4,0.605,22,0
|
||||||
|
2,155,74,17,96,26.6,0.433,27,1
|
||||||
|
3,113,50,10,85,29.5,0.626,25,0
|
||||||
|
7,109,80,31,0,35.9,1.127,43,1
|
||||||
|
2,112,68,22,94,34.1,0.315,26,0
|
||||||
|
3,99,80,11,64,19.3,0.284,30,0
|
||||||
|
3,182,74,0,0,30.5,0.345,29,1
|
||||||
|
3,115,66,39,140,38.1,0.15,28,0
|
||||||
|
6,194,78,0,0,23.5,0.129,59,1
|
||||||
|
4,129,60,12,231,27.5,0.527,31,0
|
||||||
|
3,112,74,30,0,31.6,0.197,25,1
|
||||||
|
0,124,70,20,0,27.4,0.254,36,1
|
||||||
|
13,152,90,33,29,26.8,0.731,43,1
|
||||||
|
2,112,75,32,0,35.7,0.148,21,0
|
||||||
|
1,157,72,21,168,25.6,0.123,24,0
|
||||||
|
1,122,64,32,156,35.1,0.692,30,1
|
||||||
|
10,179,70,0,0,35.1,0.2,37,0
|
||||||
|
2,102,86,36,120,45.5,0.127,23,1
|
||||||
|
6,105,70,32,68,30.8,0.122,37,0
|
||||||
|
8,118,72,19,0,23.1,1.476,46,0
|
||||||
|
2,87,58,16,52,32.7,0.166,25,0
|
||||||
|
1,180,0,0,0,43.3,0.282,41,1
|
||||||
|
12,106,80,0,0,23.6,0.137,44,0
|
||||||
|
1,95,60,18,58,23.9,0.26,22,0
|
||||||
|
0,165,76,43,255,47.9,0.259,26,0
|
||||||
|
0,117,0,0,0,33.8,0.932,44,0
|
||||||
|
5,115,76,0,0,31.2,0.343,44,1
|
||||||
|
9,152,78,34,171,34.2,0.893,33,1
|
||||||
|
7,178,84,0,0,39.9,0.331,41,1
|
||||||
|
1,130,70,13,105,25.9,0.472,22,0
|
||||||
|
1,95,74,21,73,25.9,0.673,36,0
|
||||||
|
1,0,68,35,0,32,0.389,22,0
|
||||||
|
5,122,86,0,0,34.7,0.29,33,0
|
||||||
|
8,95,72,0,0,36.8,0.485,57,0
|
||||||
|
8,126,88,36,108,38.5,0.349,49,0
|
||||||
|
1,139,46,19,83,28.7,0.654,22,0
|
||||||
|
3,116,0,0,0,23.5,0.187,23,0
|
||||||
|
3,99,62,19,74,21.8,0.279,26,0
|
||||||
|
5,0,80,32,0,41,0.346,37,1
|
||||||
|
4,92,80,0,0,42.2,0.237,29,0
|
||||||
|
4,137,84,0,0,31.2,0.252,30,0
|
||||||
|
3,61,82,28,0,34.4,0.243,46,0
|
||||||
|
1,90,62,12,43,27.2,0.58,24,0
|
||||||
|
3,90,78,0,0,42.7,0.559,21,0
|
||||||
|
9,165,88,0,0,30.4,0.302,49,1
|
||||||
|
1,125,50,40,167,33.3,0.962,28,1
|
||||||
|
13,129,0,30,0,39.9,0.569,44,1
|
||||||
|
12,88,74,40,54,35.3,0.378,48,0
|
||||||
|
1,196,76,36,249,36.5,0.875,29,1
|
||||||
|
5,189,64,33,325,31.2,0.583,29,1
|
||||||
|
5,158,70,0,0,29.8,0.207,63,0
|
||||||
|
5,103,108,37,0,39.2,0.305,65,0
|
||||||
|
4,146,78,0,0,38.5,0.52,67,1
|
||||||
|
4,147,74,25,293,34.9,0.385,30,0
|
||||||
|
5,99,54,28,83,34,0.499,30,0
|
||||||
|
6,124,72,0,0,27.6,0.368,29,1
|
||||||
|
0,101,64,17,0,21,0.252,21,0
|
||||||
|
3,81,86,16,66,27.5,0.306,22,0
|
||||||
|
1,133,102,28,140,32.8,0.234,45,1
|
||||||
|
3,173,82,48,465,38.4,2.137,25,1
|
||||||
|
0,118,64,23,89,0,1.731,21,0
|
||||||
|
0,84,64,22,66,35.8,0.545,21,0
|
||||||
|
2,105,58,40,94,34.9,0.225,25,0
|
||||||
|
2,122,52,43,158,36.2,0.816,28,0
|
||||||
|
12,140,82,43,325,39.2,0.528,58,1
|
||||||
|
0,98,82,15,84,25.2,0.299,22,0
|
||||||
|
1,87,60,37,75,37.2,0.509,22,0
|
||||||
|
4,156,75,0,0,48.3,0.238,32,1
|
||||||
|
0,93,100,39,72,43.4,1.021,35,0
|
||||||
|
1,107,72,30,82,30.8,0.821,24,0
|
||||||
|
0,105,68,22,0,20,0.236,22,0
|
||||||
|
1,109,60,8,182,25.4,0.947,21,0
|
||||||
|
1,90,62,18,59,25.1,1.268,25,0
|
||||||
|
1,125,70,24,110,24.3,0.221,25,0
|
||||||
|
1,119,54,13,50,22.3,0.205,24,0
|
||||||
|
5,116,74,29,0,32.3,0.66,35,1
|
||||||
|
8,105,100,36,0,43.3,0.239,45,1
|
||||||
|
5,144,82,26,285,32,0.452,58,1
|
||||||
|
3,100,68,23,81,31.6,0.949,28,0
|
||||||
|
1,100,66,29,196,32,0.444,42,0
|
||||||
|
5,166,76,0,0,45.7,0.34,27,1
|
||||||
|
1,131,64,14,415,23.7,0.389,21,0
|
||||||
|
4,116,72,12,87,22.1,0.463,37,0
|
||||||
|
4,158,78,0,0,32.9,0.803,31,1
|
||||||
|
2,127,58,24,275,27.7,1.6,25,0
|
||||||
|
3,96,56,34,115,24.7,0.944,39,0
|
||||||
|
0,131,66,40,0,34.3,0.196,22,1
|
||||||
|
3,82,70,0,0,21.1,0.389,25,0
|
||||||
|
3,193,70,31,0,34.9,0.241,25,1
|
||||||
|
4,95,64,0,0,32,0.161,31,1
|
||||||
|
6,137,61,0,0,24.2,0.151,55,0
|
||||||
|
5,136,84,41,88,35,0.286,35,1
|
||||||
|
9,72,78,25,0,31.6,0.28,38,0
|
||||||
|
5,168,64,0,0,32.9,0.135,41,1
|
||||||
|
2,123,48,32,165,42.1,0.52,26,0
|
||||||
|
4,115,72,0,0,28.9,0.376,46,1
|
||||||
|
0,101,62,0,0,21.9,0.336,25,0
|
||||||
|
8,197,74,0,0,25.9,1.191,39,1
|
||||||
|
1,172,68,49,579,42.4,0.702,28,1
|
||||||
|
6,102,90,39,0,35.7,0.674,28,0
|
||||||
|
1,112,72,30,176,34.4,0.528,25,0
|
||||||
|
1,143,84,23,310,42.4,1.076,22,0
|
||||||
|
1,143,74,22,61,26.2,0.256,21,0
|
||||||
|
0,138,60,35,167,34.6,0.534,21,1
|
||||||
|
3,173,84,33,474,35.7,0.258,22,1
|
||||||
|
1,97,68,21,0,27.2,1.095,22,0
|
||||||
|
4,144,82,32,0,38.5,0.554,37,1
|
||||||
|
1,83,68,0,0,18.2,0.624,27,0
|
||||||
|
3,129,64,29,115,26.4,0.219,28,1
|
||||||
|
1,119,88,41,170,45.3,0.507,26,0
|
||||||
|
2,94,68,18,76,26,0.561,21,0
|
||||||
|
0,102,64,46,78,40.6,0.496,21,0
|
||||||
|
2,115,64,22,0,30.8,0.421,21,0
|
||||||
|
8,151,78,32,210,42.9,0.516,36,1
|
||||||
|
4,184,78,39,277,37,0.264,31,1
|
||||||
|
0,94,0,0,0,0,0.256,25,0
|
||||||
|
1,181,64,30,180,34.1,0.328,38,1
|
||||||
|
0,135,94,46,145,40.6,0.284,26,0
|
||||||
|
1,95,82,25,180,35,0.233,43,1
|
||||||
|
2,99,0,0,0,22.2,0.108,23,0
|
||||||
|
3,89,74,16,85,30.4,0.551,38,0
|
||||||
|
1,80,74,11,60,30,0.527,22,0
|
||||||
|
2,139,75,0,0,25.6,0.167,29,0
|
||||||
|
1,90,68,8,0,24.5,1.138,36,0
|
||||||
|
0,141,0,0,0,42.4,0.205,29,1
|
||||||
|
12,140,85,33,0,37.4,0.244,41,0
|
||||||
|
5,147,75,0,0,29.9,0.434,28,0
|
||||||
|
1,97,70,15,0,18.2,0.147,21,0
|
||||||
|
6,107,88,0,0,36.8,0.727,31,0
|
||||||
|
0,189,104,25,0,34.3,0.435,41,1
|
||||||
|
2,83,66,23,50,32.2,0.497,22,0
|
||||||
|
4,117,64,27,120,33.2,0.23,24,0
|
||||||
|
8,108,70,0,0,30.5,0.955,33,1
|
||||||
|
4,117,62,12,0,29.7,0.38,30,1
|
||||||
|
0,180,78,63,14,59.4,2.42,25,1
|
||||||
|
1,100,72,12,70,25.3,0.658,28,0
|
||||||
|
0,95,80,45,92,36.5,0.33,26,0
|
||||||
|
0,104,64,37,64,33.6,0.51,22,1
|
||||||
|
0,120,74,18,63,30.5,0.285,26,0
|
||||||
|
1,82,64,13,95,21.2,0.415,23,0
|
||||||
|
2,134,70,0,0,28.9,0.542,23,1
|
||||||
|
0,91,68,32,210,39.9,0.381,25,0
|
||||||
|
2,119,0,0,0,19.6,0.832,72,0
|
||||||
|
2,100,54,28,105,37.8,0.498,24,0
|
||||||
|
14,175,62,30,0,33.6,0.212,38,1
|
||||||
|
1,135,54,0,0,26.7,0.687,62,0
|
||||||
|
5,86,68,28,71,30.2,0.364,24,0
|
||||||
|
10,148,84,48,237,37.6,1.001,51,1
|
||||||
|
9,134,74,33,60,25.9,0.46,81,0
|
||||||
|
9,120,72,22,56,20.8,0.733,48,0
|
||||||
|
1,71,62,0,0,21.8,0.416,26,0
|
||||||
|
8,74,70,40,49,35.3,0.705,39,0
|
||||||
|
5,88,78,30,0,27.6,0.258,37,0
|
||||||
|
10,115,98,0,0,24,1.022,34,0
|
||||||
|
0,124,56,13,105,21.8,0.452,21,0
|
||||||
|
0,74,52,10,36,27.8,0.269,22,0
|
||||||
|
0,97,64,36,100,36.8,0.6,25,0
|
||||||
|
8,120,0,0,0,30,0.183,38,1
|
||||||
|
6,154,78,41,140,46.1,0.571,27,0
|
||||||
|
1,144,82,40,0,41.3,0.607,28,0
|
||||||
|
0,137,70,38,0,33.2,0.17,22,0
|
||||||
|
0,119,66,27,0,38.8,0.259,22,0
|
||||||
|
7,136,90,0,0,29.9,0.21,50,0
|
||||||
|
4,114,64,0,0,28.9,0.126,24,0
|
||||||
|
0,137,84,27,0,27.3,0.231,59,0
|
||||||
|
2,105,80,45,191,33.7,0.711,29,1
|
||||||
|
7,114,76,17,110,23.8,0.466,31,0
|
||||||
|
8,126,74,38,75,25.9,0.162,39,0
|
||||||
|
4,132,86,31,0,28,0.419,63,0
|
||||||
|
3,158,70,30,328,35.5,0.344,35,1
|
||||||
|
0,123,88,37,0,35.2,0.197,29,0
|
||||||
|
4,85,58,22,49,27.8,0.306,28,0
|
||||||
|
0,84,82,31,125,38.2,0.233,23,0
|
||||||
|
0,145,0,0,0,44.2,0.63,31,1
|
||||||
|
0,135,68,42,250,42.3,0.365,24,1
|
||||||
|
1,139,62,41,480,40.7,0.536,21,0
|
||||||
|
0,173,78,32,265,46.5,1.159,58,0
|
||||||
|
4,99,72,17,0,25.6,0.294,28,0
|
||||||
|
8,194,80,0,0,26.1,0.551,67,0
|
||||||
|
2,83,65,28,66,36.8,0.629,24,0
|
||||||
|
2,89,90,30,0,33.5,0.292,42,0
|
||||||
|
4,99,68,38,0,32.8,0.145,33,0
|
||||||
|
4,125,70,18,122,28.9,1.144,45,1
|
||||||
|
3,80,0,0,0,0,0.174,22,0
|
||||||
|
6,166,74,0,0,26.6,0.304,66,0
|
||||||
|
5,110,68,0,0,26,0.292,30,0
|
||||||
|
2,81,72,15,76,30.1,0.547,25,0
|
||||||
|
7,195,70,33,145,25.1,0.163,55,1
|
||||||
|
6,154,74,32,193,29.3,0.839,39,0
|
||||||
|
2,117,90,19,71,25.2,0.313,21,0
|
||||||
|
3,84,72,32,0,37.2,0.267,28,0
|
||||||
|
6,0,68,41,0,39,0.727,41,1
|
||||||
|
7,94,64,25,79,33.3,0.738,41,0
|
||||||
|
3,96,78,39,0,37.3,0.238,40,0
|
||||||
|
10,75,82,0,0,33.3,0.263,38,0
|
||||||
|
0,180,90,26,90,36.5,0.314,35,1
|
||||||
|
1,130,60,23,170,28.6,0.692,21,0
|
||||||
|
2,84,50,23,76,30.4,0.968,21,0
|
||||||
|
8,120,78,0,0,25,0.409,64,0
|
||||||
|
12,84,72,31,0,29.7,0.297,46,1
|
||||||
|
0,139,62,17,210,22.1,0.207,21,0
|
||||||
|
9,91,68,0,0,24.2,0.2,58,0
|
||||||
|
2,91,62,0,0,27.3,0.525,22,0
|
||||||
|
3,99,54,19,86,25.6,0.154,24,0
|
||||||
|
3,163,70,18,105,31.6,0.268,28,1
|
||||||
|
9,145,88,34,165,30.3,0.771,53,1
|
||||||
|
7,125,86,0,0,37.6,0.304,51,0
|
||||||
|
13,76,60,0,0,32.8,0.18,41,0
|
||||||
|
6,129,90,7,326,19.6,0.582,60,0
|
||||||
|
2,68,70,32,66,25,0.187,25,0
|
||||||
|
3,124,80,33,130,33.2,0.305,26,0
|
||||||
|
6,114,0,0,0,0,0.189,26,0
|
||||||
|
9,130,70,0,0,34.2,0.652,45,1
|
||||||
|
3,125,58,0,0,31.6,0.151,24,0
|
||||||
|
3,87,60,18,0,21.8,0.444,21,0
|
||||||
|
1,97,64,19,82,18.2,0.299,21,0
|
||||||
|
3,116,74,15,105,26.3,0.107,24,0
|
||||||
|
0,117,66,31,188,30.8,0.493,22,0
|
||||||
|
0,111,65,0,0,24.6,0.66,31,0
|
||||||
|
2,122,60,18,106,29.8,0.717,22,0
|
||||||
|
0,107,76,0,0,45.3,0.686,24,0
|
||||||
|
1,86,66,52,65,41.3,0.917,29,0
|
||||||
|
6,91,0,0,0,29.8,0.501,31,0
|
||||||
|
1,77,56,30,56,33.3,1.251,24,0
|
||||||
|
4,132,0,0,0,32.9,0.302,23,1
|
||||||
|
0,105,90,0,0,29.6,0.197,46,0
|
||||||
|
0,57,60,0,0,21.7,0.735,67,0
|
||||||
|
0,127,80,37,210,36.3,0.804,23,0
|
||||||
|
3,129,92,49,155,36.4,0.968,32,1
|
||||||
|
8,100,74,40,215,39.4,0.661,43,1
|
||||||
|
3,128,72,25,190,32.4,0.549,27,1
|
||||||
|
10,90,85,32,0,34.9,0.825,56,1
|
||||||
|
4,84,90,23,56,39.5,0.159,25,0
|
||||||
|
1,88,78,29,76,32,0.365,29,0
|
||||||
|
8,186,90,35,225,34.5,0.423,37,1
|
||||||
|
5,187,76,27,207,43.6,1.034,53,1
|
||||||
|
4,131,68,21,166,33.1,0.16,28,0
|
||||||
|
1,164,82,43,67,32.8,0.341,50,0
|
||||||
|
4,189,110,31,0,28.5,0.68,37,0
|
||||||
|
1,116,70,28,0,27.4,0.204,21,0
|
||||||
|
3,84,68,30,106,31.9,0.591,25,0
|
||||||
|
6,114,88,0,0,27.8,0.247,66,0
|
||||||
|
1,88,62,24,44,29.9,0.422,23,0
|
||||||
|
1,84,64,23,115,36.9,0.471,28,0
|
||||||
|
7,124,70,33,215,25.5,0.161,37,0
|
||||||
|
1,97,70,40,0,38.1,0.218,30,0
|
||||||
|
8,110,76,0,0,27.8,0.237,58,0
|
||||||
|
11,103,68,40,0,46.2,0.126,42,0
|
||||||
|
11,85,74,0,0,30.1,0.3,35,0
|
||||||
|
6,125,76,0,0,33.8,0.121,54,1
|
||||||
|
0,198,66,32,274,41.3,0.502,28,1
|
||||||
|
1,87,68,34,77,37.6,0.401,24,0
|
||||||
|
6,99,60,19,54,26.9,0.497,32,0
|
||||||
|
0,91,80,0,0,32.4,0.601,27,0
|
||||||
|
2,95,54,14,88,26.1,0.748,22,0
|
||||||
|
1,99,72,30,18,38.6,0.412,21,0
|
||||||
|
6,92,62,32,126,32,0.085,46,0
|
||||||
|
4,154,72,29,126,31.3,0.338,37,0
|
||||||
|
0,121,66,30,165,34.3,0.203,33,1
|
||||||
|
3,78,70,0,0,32.5,0.27,39,0
|
||||||
|
2,130,96,0,0,22.6,0.268,21,0
|
||||||
|
3,111,58,31,44,29.5,0.43,22,0
|
||||||
|
2,98,60,17,120,34.7,0.198,22,0
|
||||||
|
1,143,86,30,330,30.1,0.892,23,0
|
||||||
|
1,119,44,47,63,35.5,0.28,25,0
|
||||||
|
6,108,44,20,130,24,0.813,35,0
|
||||||
|
2,118,80,0,0,42.9,0.693,21,1
|
||||||
|
10,133,68,0,0,27,0.245,36,0
|
||||||
|
2,197,70,99,0,34.7,0.575,62,1
|
||||||
|
0,151,90,46,0,42.1,0.371,21,1
|
||||||
|
6,109,60,27,0,25,0.206,27,0
|
||||||
|
12,121,78,17,0,26.5,0.259,62,0
|
||||||
|
8,100,76,0,0,38.7,0.19,42,0
|
||||||
|
8,124,76,24,600,28.7,0.687,52,1
|
||||||
|
1,93,56,11,0,22.5,0.417,22,0
|
||||||
|
8,143,66,0,0,34.9,0.129,41,1
|
||||||
|
6,103,66,0,0,24.3,0.249,29,0
|
||||||
|
3,176,86,27,156,33.3,1.154,52,1
|
||||||
|
0,73,0,0,0,21.1,0.342,25,0
|
||||||
|
11,111,84,40,0,46.8,0.925,45,1
|
||||||
|
2,112,78,50,140,39.4,0.175,24,0
|
||||||
|
3,132,80,0,0,34.4,0.402,44,1
|
||||||
|
2,82,52,22,115,28.5,1.699,25,0
|
||||||
|
6,123,72,45,230,33.6,0.733,34,0
|
||||||
|
0,188,82,14,185,32,0.682,22,1
|
||||||
|
0,67,76,0,0,45.3,0.194,46,0
|
||||||
|
1,89,24,19,25,27.8,0.559,21,0
|
||||||
|
1,173,74,0,0,36.8,0.088,38,1
|
||||||
|
1,109,38,18,120,23.1,0.407,26,0
|
||||||
|
1,108,88,19,0,27.1,0.4,24,0
|
||||||
|
6,96,0,0,0,23.7,0.19,28,0
|
||||||
|
1,124,74,36,0,27.8,0.1,30,0
|
||||||
|
7,150,78,29,126,35.2,0.692,54,1
|
||||||
|
4,183,0,0,0,28.4,0.212,36,1
|
||||||
|
1,124,60,32,0,35.8,0.514,21,0
|
||||||
|
1,181,78,42,293,40,1.258,22,1
|
||||||
|
1,92,62,25,41,19.5,0.482,25,0
|
||||||
|
0,152,82,39,272,41.5,0.27,27,0
|
||||||
|
1,111,62,13,182,24,0.138,23,0
|
||||||
|
3,106,54,21,158,30.9,0.292,24,0
|
||||||
|
3,174,58,22,194,32.9,0.593,36,1
|
||||||
|
7,168,88,42,321,38.2,0.787,40,1
|
||||||
|
6,105,80,28,0,32.5,0.878,26,0
|
||||||
|
11,138,74,26,144,36.1,0.557,50,1
|
||||||
|
3,106,72,0,0,25.8,0.207,27,0
|
||||||
|
6,117,96,0,0,28.7,0.157,30,0
|
||||||
|
2,68,62,13,15,20.1,0.257,23,0
|
||||||
|
9,112,82,24,0,28.2,1.282,50,1
|
||||||
|
0,119,0,0,0,32.4,0.141,24,1
|
||||||
|
2,112,86,42,160,38.4,0.246,28,0
|
||||||
|
2,92,76,20,0,24.2,1.698,28,0
|
||||||
|
6,183,94,0,0,40.8,1.461,45,0
|
||||||
|
0,94,70,27,115,43.5,0.347,21,0
|
||||||
|
2,108,64,0,0,30.8,0.158,21,0
|
||||||
|
4,90,88,47,54,37.7,0.362,29,0
|
||||||
|
0,125,68,0,0,24.7,0.206,21,0
|
||||||
|
0,132,78,0,0,32.4,0.393,21,0
|
||||||
|
5,128,80,0,0,34.6,0.144,45,0
|
||||||
|
4,94,65,22,0,24.7,0.148,21,0
|
||||||
|
7,114,64,0,0,27.4,0.732,34,1
|
||||||
|
0,102,78,40,90,34.5,0.238,24,0
|
||||||
|
2,111,60,0,0,26.2,0.343,23,0
|
||||||
|
1,128,82,17,183,27.5,0.115,22,0
|
||||||
|
10,92,62,0,0,25.9,0.167,31,0
|
||||||
|
13,104,72,0,0,31.2,0.465,38,1
|
||||||
|
5,104,74,0,0,28.8,0.153,48,0
|
||||||
|
2,94,76,18,66,31.6,0.649,23,0
|
||||||
|
7,97,76,32,91,40.9,0.871,32,1
|
||||||
|
1,100,74,12,46,19.5,0.149,28,0
|
||||||
|
0,102,86,17,105,29.3,0.695,27,0
|
||||||
|
4,128,70,0,0,34.3,0.303,24,0
|
||||||
|
6,147,80,0,0,29.5,0.178,50,1
|
||||||
|
4,90,0,0,0,28,0.61,31,0
|
||||||
|
3,103,72,30,152,27.6,0.73,27,0
|
||||||
|
2,157,74,35,440,39.4,0.134,30,0
|
||||||
|
1,167,74,17,144,23.4,0.447,33,1
|
||||||
|
0,179,50,36,159,37.8,0.455,22,1
|
||||||
|
11,136,84,35,130,28.3,0.26,42,1
|
||||||
|
0,107,60,25,0,26.4,0.133,23,0
|
||||||
|
1,91,54,25,100,25.2,0.234,23,0
|
||||||
|
1,117,60,23,106,33.8,0.466,27,0
|
||||||
|
5,123,74,40,77,34.1,0.269,28,0
|
||||||
|
2,120,54,0,0,26.8,0.455,27,0
|
||||||
|
1,106,70,28,135,34.2,0.142,22,0
|
||||||
|
2,155,52,27,540,38.7,0.24,25,1
|
||||||
|
2,101,58,35,90,21.8,0.155,22,0
|
||||||
|
1,120,80,48,200,38.9,1.162,41,0
|
||||||
|
11,127,106,0,0,39,0.19,51,0
|
||||||
|
3,80,82,31,70,34.2,1.292,27,1
|
||||||
|
10,162,84,0,0,27.7,0.182,54,0
|
||||||
|
1,199,76,43,0,42.9,1.394,22,1
|
||||||
|
8,167,106,46,231,37.6,0.165,43,1
|
||||||
|
9,145,80,46,130,37.9,0.637,40,1
|
||||||
|
6,115,60,39,0,33.7,0.245,40,1
|
||||||
|
1,112,80,45,132,34.8,0.217,24,0
|
||||||
|
4,145,82,18,0,32.5,0.235,70,1
|
||||||
|
10,111,70,27,0,27.5,0.141,40,1
|
||||||
|
6,98,58,33,190,34,0.43,43,0
|
||||||
|
9,154,78,30,100,30.9,0.164,45,0
|
||||||
|
6,165,68,26,168,33.6,0.631,49,0
|
||||||
|
1,99,58,10,0,25.4,0.551,21,0
|
||||||
|
10,68,106,23,49,35.5,0.285,47,0
|
||||||
|
3,123,100,35,240,57.3,0.88,22,0
|
||||||
|
8,91,82,0,0,35.6,0.587,68,0
|
||||||
|
6,195,70,0,0,30.9,0.328,31,1
|
||||||
|
9,156,86,0,0,24.8,0.23,53,1
|
||||||
|
0,93,60,0,0,35.3,0.263,25,0
|
||||||
|
3,121,52,0,0,36,0.127,25,1
|
||||||
|
2,101,58,17,265,24.2,0.614,23,0
|
||||||
|
2,56,56,28,45,24.2,0.332,22,0
|
||||||
|
0,162,76,36,0,49.6,0.364,26,1
|
||||||
|
0,95,64,39,105,44.6,0.366,22,0
|
||||||
|
4,125,80,0,0,32.3,0.536,27,1
|
||||||
|
5,136,82,0,0,0,0.64,69,0
|
||||||
|
2,129,74,26,205,33.2,0.591,25,0
|
||||||
|
3,130,64,0,0,23.1,0.314,22,0
|
||||||
|
1,107,50,19,0,28.3,0.181,29,0
|
||||||
|
1,140,74,26,180,24.1,0.828,23,0
|
||||||
|
1,144,82,46,180,46.1,0.335,46,1
|
||||||
|
8,107,80,0,0,24.6,0.856,34,0
|
||||||
|
13,158,114,0,0,42.3,0.257,44,1
|
||||||
|
2,121,70,32,95,39.1,0.886,23,0
|
||||||
|
7,129,68,49,125,38.5,0.439,43,1
|
||||||
|
2,90,60,0,0,23.5,0.191,25,0
|
||||||
|
7,142,90,24,480,30.4,0.128,43,1
|
||||||
|
3,169,74,19,125,29.9,0.268,31,1
|
||||||
|
0,99,0,0,0,25,0.253,22,0
|
||||||
|
4,127,88,11,155,34.5,0.598,28,0
|
||||||
|
4,118,70,0,0,44.5,0.904,26,0
|
||||||
|
2,122,76,27,200,35.9,0.483,26,0
|
||||||
|
6,125,78,31,0,27.6,0.565,49,1
|
||||||
|
1,168,88,29,0,35,0.905,52,1
|
||||||
|
2,129,0,0,0,38.5,0.304,41,0
|
||||||
|
4,110,76,20,100,28.4,0.118,27,0
|
||||||
|
6,80,80,36,0,39.8,0.177,28,0
|
||||||
|
10,115,0,0,0,0,0.261,30,1
|
||||||
|
2,127,46,21,335,34.4,0.176,22,0
|
||||||
|
9,164,78,0,0,32.8,0.148,45,1
|
||||||
|
2,93,64,32,160,38,0.674,23,1
|
||||||
|
3,158,64,13,387,31.2,0.295,24,0
|
||||||
|
5,126,78,27,22,29.6,0.439,40,0
|
||||||
|
10,129,62,36,0,41.2,0.441,38,1
|
||||||
|
0,134,58,20,291,26.4,0.352,21,0
|
||||||
|
3,102,74,0,0,29.5,0.121,32,0
|
||||||
|
7,187,50,33,392,33.9,0.826,34,1
|
||||||
|
3,173,78,39,185,33.8,0.97,31,1
|
||||||
|
10,94,72,18,0,23.1,0.595,56,0
|
||||||
|
1,108,60,46,178,35.5,0.415,24,0
|
||||||
|
5,97,76,27,0,35.6,0.378,52,1
|
||||||
|
4,83,86,19,0,29.3,0.317,34,0
|
||||||
|
1,114,66,36,200,38.1,0.289,21,0
|
||||||
|
1,149,68,29,127,29.3,0.349,42,1
|
||||||
|
5,117,86,30,105,39.1,0.251,42,0
|
||||||
|
1,111,94,0,0,32.8,0.265,45,0
|
||||||
|
4,112,78,40,0,39.4,0.236,38,0
|
||||||
|
1,116,78,29,180,36.1,0.496,25,0
|
||||||
|
0,141,84,26,0,32.4,0.433,22,0
|
||||||
|
2,175,88,0,0,22.9,0.326,22,0
|
||||||
|
2,92,52,0,0,30.1,0.141,22,0
|
||||||
|
3,130,78,23,79,28.4,0.323,34,1
|
||||||
|
8,120,86,0,0,28.4,0.259,22,1
|
||||||
|
2,174,88,37,120,44.5,0.646,24,1
|
||||||
|
2,106,56,27,165,29,0.426,22,0
|
||||||
|
2,105,75,0,0,23.3,0.56,53,0
|
||||||
|
4,95,60,32,0,35.4,0.284,28,0
|
||||||
|
0,126,86,27,120,27.4,0.515,21,0
|
||||||
|
8,65,72,23,0,32,0.6,42,0
|
||||||
|
2,99,60,17,160,36.6,0.453,21,0
|
||||||
|
1,102,74,0,0,39.5,0.293,42,1
|
||||||
|
11,120,80,37,150,42.3,0.785,48,1
|
||||||
|
3,102,44,20,94,30.8,0.4,26,0
|
||||||
|
1,109,58,18,116,28.5,0.219,22,0
|
||||||
|
9,140,94,0,0,32.7,0.734,45,1
|
||||||
|
13,153,88,37,140,40.6,1.174,39,0
|
||||||
|
12,100,84,33,105,30,0.488,46,0
|
||||||
|
1,147,94,41,0,49.3,0.358,27,1
|
||||||
|
1,81,74,41,57,46.3,1.096,32,0
|
||||||
|
3,187,70,22,200,36.4,0.408,36,1
|
||||||
|
6,162,62,0,0,24.3,0.178,50,1
|
||||||
|
4,136,70,0,0,31.2,1.182,22,1
|
||||||
|
1,121,78,39,74,39,0.261,28,0
|
||||||
|
3,108,62,24,0,26,0.223,25,0
|
||||||
|
0,181,88,44,510,43.3,0.222,26,1
|
||||||
|
8,154,78,32,0,32.4,0.443,45,1
|
||||||
|
1,128,88,39,110,36.5,1.057,37,1
|
||||||
|
7,137,90,41,0,32,0.391,39,0
|
||||||
|
0,123,72,0,0,36.3,0.258,52,1
|
||||||
|
1,106,76,0,0,37.5,0.197,26,0
|
||||||
|
6,190,92,0,0,35.5,0.278,66,1
|
||||||
|
2,88,58,26,16,28.4,0.766,22,0
|
||||||
|
9,170,74,31,0,44,0.403,43,1
|
||||||
|
9,89,62,0,0,22.5,0.142,33,0
|
||||||
|
10,101,76,48,180,32.9,0.171,63,0
|
||||||
|
2,122,70,27,0,36.8,0.34,27,0
|
||||||
|
5,121,72,23,112,26.2,0.245,30,0
|
||||||
|
1,126,60,0,0,30.1,0.349,47,1
|
||||||
|
1,93,70,31,0,30.4,0.315,23,0
|
||||||
|
|
|
@ -0,0 +1,156 @@
|
||||||
|
import torch
|
||||||
|
import pandas as pd
|
||||||
|
from torch import nn, optim
|
||||||
|
from torch.autograd.variable import Variable
|
||||||
|
from torchvision import transforms, datasets
|
||||||
|
from data_treatment import DataSet, DataAtts
|
||||||
|
from discriminator import *
|
||||||
|
from generator import *
|
||||||
|
import os
|
||||||
|
# import ipywidgets as widgets
|
||||||
|
# from IPython.display import display
|
||||||
|
# import matplotlib.pyplot as plt
|
||||||
|
import glob
|
||||||
|
from utils import *
|
||||||
|
|
||||||
|
|
||||||
|
class Architecture():
|
||||||
|
def __init__(self, learning_rate, batch_size, loss, hidden_layers, name):
|
||||||
|
self.learning_rate=learning_rate
|
||||||
|
self.batch_size=batch_size
|
||||||
|
self.loss=loss
|
||||||
|
self.hidden_layers=hidden_layers
|
||||||
|
self.name=name
|
||||||
|
|
||||||
|
def save_model(name, epoch, attributes, dictionary, optimizer_dictionary, loss_function, db_name, arch_name):
|
||||||
|
torch.save({
|
||||||
|
'epoch': epoch,
|
||||||
|
'model_attributes': attributes,
|
||||||
|
'model_state_dict': dictionary,
|
||||||
|
'optimizer_state_dict': optimizer_dictionary,
|
||||||
|
'loss': loss_function
|
||||||
|
}, "models/" + db_name + "/" + name + "_" + arch_name + ".pt")
|
||||||
|
|
||||||
|
|
||||||
|
# Check if creditcard.csv exists and if so, create a scalonated version of it
|
||||||
|
# escalonate_creditcard_db()
|
||||||
|
if not os.path.isfile('./original_data/diabetes.csv'):
|
||||||
|
print("Database creditcard.csv not found, exiting...")
|
||||||
|
exit()
|
||||||
|
|
||||||
|
file_names=["original_data/diabetes.csv"]
|
||||||
|
num_epochs=[500]
|
||||||
|
learning_rate=[0.0002]
|
||||||
|
batch_size=[5]
|
||||||
|
number_of_experiments = 5
|
||||||
|
#hidden_layers=[[256, 512]]
|
||||||
|
hidden_layers=[[256, 512], [256], [128, 256], [128]]
|
||||||
|
# hidden_layers=[[256]]
|
||||||
|
|
||||||
|
#create the different architetures
|
||||||
|
architectures=[]
|
||||||
|
count=0
|
||||||
|
for lr in learning_rate:
|
||||||
|
for b_size in batch_size:
|
||||||
|
for hidden in hidden_layers:
|
||||||
|
for i in range(number_of_experiments):
|
||||||
|
name = "id-" + str(count)
|
||||||
|
name += "_epochs-" + str(num_epochs[0])
|
||||||
|
name += "_layer-" + str(len(hidden))
|
||||||
|
name += "_lr-" + str(lr)
|
||||||
|
name += "_batch-" + str(b_size)
|
||||||
|
name += "_arc-" + ','.join(map(str, hidden))
|
||||||
|
architectures.append( Architecture(
|
||||||
|
learning_rate=lr,
|
||||||
|
batch_size=b_size,
|
||||||
|
loss=nn.BCELoss(),
|
||||||
|
hidden_layers=hidden,
|
||||||
|
name=name
|
||||||
|
)
|
||||||
|
)
|
||||||
|
count+=1
|
||||||
|
|
||||||
|
|
||||||
|
#training process
|
||||||
|
for file_name, epochs in zip(file_names, num_epochs):
|
||||||
|
dataAtts = DataAtts(file_name)
|
||||||
|
database = DataSet (csv_file=file_name, root_dir=".", shuffle_db=False)
|
||||||
|
|
||||||
|
for arc in architectures:
|
||||||
|
if ("escalonated" in file_name):
|
||||||
|
esc = torch.nn.Sigmoid()
|
||||||
|
else:
|
||||||
|
esc = False
|
||||||
|
|
||||||
|
generatorAtts = {
|
||||||
|
'out_features':dataAtts.class_len,
|
||||||
|
'leakyRelu':0.2,
|
||||||
|
'hidden_layers':arc.hidden_layers,
|
||||||
|
'in_features':100,
|
||||||
|
'escalonate':esc
|
||||||
|
}
|
||||||
|
generator = GeneratorNet(**generatorAtts)
|
||||||
|
|
||||||
|
discriminatorAtts = {
|
||||||
|
'in_features':dataAtts.class_len,
|
||||||
|
'leakyRelu':0.2,
|
||||||
|
'dropout':0.3,
|
||||||
|
'hidden_layers':arc.hidden_layers[::-1]
|
||||||
|
|
||||||
|
}
|
||||||
|
discriminator = DiscriminatorNet(**discriminatorAtts)
|
||||||
|
|
||||||
|
if torch.cuda.is_available():
|
||||||
|
discriminator.cuda()
|
||||||
|
generator.cuda()
|
||||||
|
d_optimizer = optim.Adam(discriminator.parameters(), lr=arc.learning_rate)
|
||||||
|
g_optimizer = optim.Adam(generator.parameters(), lr=arc.learning_rate)
|
||||||
|
loss = arc.loss
|
||||||
|
data_loader = torch.utils.data.DataLoader(database, batch_size=arc.batch_size, shuffle=True)
|
||||||
|
num_batches = len(data_loader)
|
||||||
|
|
||||||
|
print(dataAtts.fname)
|
||||||
|
print(arc.name)
|
||||||
|
for epoch in range(epochs):
|
||||||
|
if (epoch % 100 == 0):
|
||||||
|
print("Epoch ", epoch)
|
||||||
|
|
||||||
|
for n_batch, real_batch in enumerate(data_loader):
|
||||||
|
# 1. Train DdataAtts.fnameiscriminator
|
||||||
|
real_data = Variable(real_batch).float()
|
||||||
|
if torch.cuda.is_available():
|
||||||
|
real_data = real_data.cuda()
|
||||||
|
# Generate fake data
|
||||||
|
fake_data = generator(random_noise(real_data.size(0))).detach()
|
||||||
|
# Train D
|
||||||
|
d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, discriminator, loss, real_data, fake_data)
|
||||||
|
|
||||||
|
# 2. Train Generator
|
||||||
|
# Generate fake data
|
||||||
|
fake_data = generator(random_noise(real_batch.size(0)))
|
||||||
|
# Train G
|
||||||
|
g_error = train_generator(g_optimizer, discriminator, loss, fake_data)
|
||||||
|
|
||||||
|
# Display Progress
|
||||||
|
|
||||||
|
#if (n_batch) % print_interval == 0:
|
||||||
|
|
||||||
|
# From this line on it's just the saving
|
||||||
|
# save_model("generator", epoch, generatorAtts, generator.state_dict(), g_optimizer.state_dict(), loss, dataAtts.fname, arc.name)
|
||||||
|
# save_model("discriminator", epoch, discriminatorAtts, discriminator.state_dict(), d_optimizer.state_dict(), loss, dataAtts.fname, arc.name)
|
||||||
|
|
||||||
|
torch.save({
|
||||||
|
'epoch': epoch,
|
||||||
|
'model_attributes': generatorAtts,
|
||||||
|
'model_state_dict': generator.state_dict(),
|
||||||
|
'optimizer_state_dict': g_optimizer.state_dict(),
|
||||||
|
'loss': loss
|
||||||
|
}, "models/" + dataAtts.fname + "/generator_" + arc.name + ".pt")
|
||||||
|
|
||||||
|
torch.save({
|
||||||
|
'epoch': epoch,
|
||||||
|
'model_attributes': discriminatorAtts,
|
||||||
|
'model_state_dict': discriminator.state_dict(),
|
||||||
|
'optimizer_state_dict': d_optimizer.state_dict(),
|
||||||
|
'loss': loss
|
||||||
|
}, "models/" + dataAtts.fname + "/discriminator_" + arc.name + ".pt")
|
||||||
|
|
@ -0,0 +1,24 @@
|
||||||
|
from torch.autograd.variable import Variable
|
||||||
|
import torch
|
||||||
|
|
||||||
|
def random_noise(size):
|
||||||
|
n = Variable(torch.randn(size, 100))
|
||||||
|
if torch.cuda.is_available():
|
||||||
|
return n.cuda()
|
||||||
|
return n
|
||||||
|
|
||||||
|
def real_data_target(size):
|
||||||
|
'''
|
||||||
|
Tensor containing ones, with shape = size
|
||||||
|
'''
|
||||||
|
data = Variable(torch.ones(size, 1))
|
||||||
|
if torch.cuda.is_available(): return data.cuda()
|
||||||
|
return data
|
||||||
|
|
||||||
|
def fake_data_target(size):
|
||||||
|
'''
|
||||||
|
Tensor containing zeros, with shape = size
|
||||||
|
'''
|
||||||
|
data = Variable(torch.zeros(size, 1))
|
||||||
|
if torch.cuda.is_available(): return data.cuda()
|
||||||
|
return data
|
||||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue