Last active
December 22, 2020 03:46
-
-
Save knowblesse/1e770aa41fb609189bceaea6f639fd9c to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import sklearn | |
if (sklearn.__version__ != '0.23.2'): | |
raise Exception("scikit-learn package version must be 0.23.2") | |
import os | |
import numpy as np | |
from scipy.io import loadmat | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import classification_report | |
from sklearn.metrics import confusion_matrix | |
import seaborn as snsimport pandas as pd | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import matplotlib | |
matplotlib.rc('font', family='HCR Batang') | |
df = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv') | |
# Select KOR data | |
data_KOR = df.loc[df['iso_code']=='KOR',['date','total_cases','new_cases']].dropna().reset_index(drop=True) | |
# Generate datasets | |
num_consecutive_data = 7 | |
predict_day = 1 | |
data = np.array(data_KOR['new_cases'], dtype=int) | |
num_data = data.shape[0] | |
X = np.zeros([num_data-num_consecutive_data - predict_day + 1, num_consecutive_data], dtype=int) | |
Y = np.zeros([num_data-num_consecutive_data - predict_day + 1], dtype=int) | |
for i in range(num_data-num_consecutive_data - predict_day + 1): | |
X[i, :] = data[i:i+num_consecutive_data] | |
Y[i] = data[i+num_consecutive_data + predict_day - 1] | |
# Divide Train/Test datasets | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1) | |
# Generate Regressor | |
from sklearn.neural_network import MLPRegressor | |
reg = MLPRegressor(activation='relu', max_iter=5000, batch_size=1, learning_rate='constant', learning_rate_init=0.001, alpha=1e-2) | |
reg.fit(X_train, Y_train) | |
# Continuous Graph | |
tick_length = 30 | |
real = np.array(data_KOR['new_cases']) | |
aligned_predicted = np.hstack([np.zeros((num_consecutive_data + predict_day - 2,)),reg.predict(X)]) | |
plt.figure(1, figsize=(5,5)) | |
plt.clf() | |
plt.plot(real[np.arange(100,150)],c='b') | |
plt.plot(aligned_predicted[np.arange(100,150)], c='r') | |
#plt.plot(real,c='b') | |
#plt.plot(aligned_predicted, c='r') | |
plt.show() | |
plt.figure(2, figsize=(5,5)) | |
plt.clf() | |
plt.plot(real[np.arange(200,250)],c='b') | |
plt.plot(aligned_predicted[np.arange(200,250)], c='r') | |
#plt.plot(real,c='b') | |
#plt.plot(aligned_predicted, c='r') | |
plt.scatter(6,reg.predict(real[200:207].reshape(1,-1))) | |
plt.show() | |
# Daily | |
days = 7 | |
start = 323 | |
tick_length = 1 | |
plt.figure(2, figsize=(5,5)) | |
plt.clf() | |
plt.plot(np.array(data_KOR['new_cases'][np.arange(start,start+days)]),c='b') | |
predicted = reg.predict(X)[np.arange(start - num_consecutive_data + 1 , start + days-num_consecutive_data + 2)] | |
print(predicted) | |
#plt.scatter(days, predicted, c='r') | |
plt.scatter(days,predicted[-1], c='r') | |
plt.plot([days-1, days], np.array(data_KOR['new_cases'][np.arange(start+days-1, start+days+1)]),'b--',label='_nolegend_') | |
plt.scatter(days, np.array(data_KOR['new_cases'][start+days]),c='b',label='_nolegend_') | |
plt.title('일일 확진자 수') | |
plt.ylim([600,1200]) | |
plt.legend(['실제값', '예측값']) | |
plt.xticks(np.arange(0,days+1,tick_length),labels=data_KOR['date'][np.arange(start,start+days+1,tick_length)], rotation=45) | |
plt.ylabel('일일 확진자 수(명)') | |
plt.annotate(str(int(np.array(data_KOR['new_cases'][start+days]))),xy=(days,np.array(data_KOR['new_cases'][start+days])-30),c='b') | |
plt.annotate(str(int(predicted[-1])),xy=(days-40,predicted[-1] - 10),c='r') | |
plt.show() | |
print(predicted[-1]) | |
import matplotlib.pyplot as plt | |
# Load .mat data | |
BASE_PATH = r'C:\VCF\Lobster\data\20JUN' | |
datalist = os.listdir(BASE_PATH) | |
data = loadmat(os.path.join(BASE_PATH, datalist[0])) | |
print(datalist[1] + ' is loaded \n') | |
X = data.get('X') | |
Y = data.get('y') | |
Y = np.squeeze(Y) | |
#np.random.shuffle(Y) | |
Y_label = ['Head Entry', 'Avoidance', 'Escape'] | |
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5, stratify=Y) | |
from sklearn.svm import SVC | |
from sklearn.model_selection import GridSearchCV | |
param_grid = {'C' : np.linspace(1,3,21)} | |
scores = ['accuracy','precision', 'recall'] | |
## Parameter search | |
print('Hyper parameter tuning for accuracy') | |
print() | |
search = GridSearchCV(SVC(kernel='rbf', gamma='auto'), iid=False, param_grid=param_grid, cv=5, n_jobs=-1, scoring='accuracy') | |
search.fit(X_train, Y_train) | |
print("Grid scores on development set:") | |
print() | |
means = search.cv_results_['mean_test_score'] | |
stds = search.cv_results_['std_test_score'] | |
for mean, std, params in zip(means, stds, search.cv_results_['params']): | |
print("%0.3f (+/-%0.03f) for %r" | |
% (mean, std * 2, params)) | |
print() | |
print("Detailed classification report:") | |
print() | |
print("The model is trained on the full development set.") | |
print("The scores are computed on the full evaluation set.") | |
print() | |
Y_true, Y_pred = Y_test, search.predict(X_test) | |
print(classification_report(Y_true, Y_pred)) | |
print() | |
print('Best parameter') | |
print(search.best_params_) | |
# Classification Result | |
confusion_mat = confusion_matrix(Y_true, Y_pred,normalize='true') # row is actual. # column is predicted | |
cmap = sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True) | |
f, ax = plt.subplots(figsize=(11, 9)) | |
sns.heatmap(confusion_mat, cmap=cmap, vmin=0, vmax=1, annot=True, square=True, linewidths=.5, cbar_kws={"shrink": .5}, xticklabels=Y_label, yticklabels=Y_label) | |
ax.set_xlabel('predicted') | |
ax.set_ylabel('actual') | |
plt.show() | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment