def linearRegreSin(url,degree):
[a,b] = getData(url)
trainA = a[0:139]
trainB = b[0:139]
testA = a[140:]
testB = b[140:]
poly = PolynomialFeatures(degree)
trainA = np.float64(poly.fit_transform(trainA))
testA = np.float64(poly.fit_transform(testA))
theta = np.dot(np.dot(np.linalg.inv(np.dot(trainA.T,trainA)),trainA.T),trainB)
plt.figure(1)
plt.xlabel('x')
plt.ylabel('y')
plt.title('data')
plt.plot(trainA[:,1],trainB,"r*")
y=np.dot(trainA, theta)
print(pow(sum((y-trainB)**2),1/2)/140) #print MSE
y=np.dot(testA, theta)
#plt.plot(testA[:,1], testB, "r.")
plt.plot(testA[:,1],y,"k*")
print(pow(sum((y-testB)**2),1/2)/60) #print MSE
plt.show()
print(theta)
def main():
# linear model (using polynomial features)
model = linear_model.Ridge(alpha=0.1)
# get data
out = getData()
X = out["x_train"]
y = out["y_train"]
perc = int(0.75 * len(X))
X_train = X[0:perc]
X_cv = X[perc:]
y_train = y[0:perc]
y_cv = y[perc:]
X_test = out["x_test"]
Id = out["test_id"]
# add bias unit
poly = PolynomialFeatures(degree=3)
X_train = poly.fit_transform(X_train)
# train model
model.fit(X_train, y_train)
# score
X_cv = poly.fit_transform(X_cv)
print model.score(X_cv, y_cv)
# predict
X_test = poly.fit_transform(X_test)
pred = model.predict(X_test)
f = open("submissions/PolyReg3.csv", "w")
f.write("Id,Hazard\n")
for i in xrange(len(pred)):
f.write(str(Id[i]) + "," + str(pred[i]) + "\n")
def batterLife_chargeMoreThan4(chargeTime):
import numpy as np
trainDataArr = np.genfromtxt("trainingdata_batteryLife.txt", delimiter = ",")
trainDataArr = trainDataArr[trainDataArr[ :,0] > 4]
trainData = trainDataArr[:, 0]
trainData = trainData.reshape(-1,1)
trainValue = trainDataArr[:,1]
testData = np.array(chargeTime)
testData = testData.reshape(-1,1)
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
# Plot outputs
import matplotlib.pyplot as plt
plt.scatter(trainData, trainValue, color='black')
plt.xticks(())
plt.yticks(())
plt.show()
# Fit regression model
poly = PolynomialFeatures(degree = 1)
trainData_ = poly.fit_transform(trainData)
testData_ = poly.fit_transform(testData)
clf = linear_model.LinearRegression()
clf.fit(trainData_, trainValue)
return clf.predict(testData_)
def logistic_regression(x,y):
"""
Ierative multifeatures regression.
Find the best theta by changing it in each iteration
Print the training and testing errors for each mapping
"""
errors_training_fmeasure = []
errors_training_accuracy = []
errors_testing_fmeasure = []
errors_testing_accuracy = []
regr = LogisticRegressionKClasses()
poly = PolynomialFeatures(degree=1)
# Cross validation
cv = KFold(len(x), n_folds=10)
for train_idx, test_idx in cv:
x_train = x[train_idx]
x_test = x[test_idx]
y_train = y[train_idx]
y_test = y[test_idx]
x_ = poly.fit_transform(x_train)
x_2 = poly.fit_transform(x_test)
regr.fit(x_,y_train)
# Predict over the testing data and getting the error
predicted_y = regr.predict(x_2)
conf_matrix = confusion_matrix(y_test, predicted_y)
precision, recall, f_measure, accuracy = get_measures(conf_matrix, len(set(y_train)))
print 'Precision:', precision, ' Recall:', recall, ' Accuracy:', accuracy, ' F-Measure:', f_measure
def newtonRaphson(inputFiles):
pol = PolynomialFeatures(2)
errors = []
for File in inputFiles:
data = tools.readData(File)
X = data[:, :-1]
Y = data[:, -1]
kf = KFold(len(Y), n_folds = 10)
trainError = 0
testError = 0
for train, test in kf:
Z = pol.fit_transform(X[train])
row, col = Z.shape
theta = np.empty(col, dtype='float')
meanDiff = 1.0
i = 1
#print "Theta iteration %s: \n%s" % ('0', str(theta))
while abs(meanDiff) > 1.0e-15 :
theta_new = recalculateTheta(theta, Z, Y[train])
diff = np.subtract(theta_new, theta)
meanDiff = np.mean(diff)
#print "Theta iteration %s: \n%s" % (str(i), str(theta_new))
#print "Diff: %s" % str(meanDiff)
theta = theta_new
i += 1
Z_test = pol.fit_transform(X[test])
Y_hat_test = np.dot(Z_test, theta)
Y_hat = np.dot(Z, theta)
trainError += tools.findError(Y_hat, Y[train])
testError += tools.findError(Y_hat_test, Y[test])
trainError = trainError/len(kf)
testError = testError/len(kf)
iterative_error = [trainError, testError]
errors. append(iterative_error)
return np.asarray(errors)
def polyRegressionKFold(inputFiles, deg=2):
print "***************************"
print "Degree: %s" % deg
start_time = time.time()
errors = []
for File in inputFiles:
print "___________________________"
print "Data Set: %s" % File
data = tools.readData(File)
data = data[np.argsort(data[:,0])]
X = data[:, :-1]
Y = data[:, len(data[1,:]) - 1]
kf = KFold(len(data), n_folds = 10, shuffle = True)
TrainError = 0
TestError = 0
for train, test in kf:
pol = PolynomialFeatures(deg)
Z = pol.fit_transform(X[train])
Z_test = pol.fit_transform(X[test])
theta = regress(Z, Y[train])
Y_hat = np.dot(Z, theta)
Y_hat_test = np.dot(Z_test, theta)
TrainError += mean_squared_error(Y[train], Y_hat)
TestError += mean_squared_error(Y[test], Y_hat_test)
TestError /= len(kf)
TrainError /= len(kf)
errors.append([TestError, deg])
print "---------------------------"
print "Test Error: %s" % TestError
print "Train Error: %s" % TrainError
time_taken = start_time - time.time()
print "Time Taken for primal: %s" % str(time_taken)
return np.asarray(errors)
def housing_polynomial_regression():
'''
housing数据1元多次
:return:
'''
X = df[['LSTAT']].values
y = df['MEDV'].values
regr = LinearRegression()
# create polynomial features
quadratic = PolynomialFeatures(degree=2)
cubic = PolynomialFeatures(degree=3)
X_quad = quadratic.fit_transform(X)
X_cubic = cubic.fit_transform(X)
# linear fit
X_fit = np.arange(X.min(), X.max(), 1)[:, np.newaxis]
regr = regr.fit(X, y)
y_lin_fit = regr.predict(X_fit)
linear_r2 = r2_score(y, regr.predict(X))
# quadratic fit
regr = regr.fit(X_quad, y)
y_quad_fit = regr.predict(quadratic.fit_transform(X_fit))
quadratic_r2 = r2_score(y, regr.predict(X_quad))
# cubic fit
regr = regr.fit(X_cubic, y)
y_cubic_fit = regr.predict(cubic.fit_transform(X_fit))
cubic_r2 = r2_score(y, regr.predict(X_cubic))
# plot results
plt.scatter(X, y, label='training points', color='lightgray')
plt.plot(X_fit, y_lin_fit, label='linear (d=1), $R^2=%.2f$' % linear_r2, color='blue', lw=2, linestyle=':')
plt.plot(X_fit, y_quad_fit, label='quadratic (d=2), $R^2=%.2f$' % quadratic_r2, color='red', lw=2, linestyle='-')
plt.plot(X_fit, y_cubic_fit, label='cubic (d=3), $R^2=%.2f$' % cubic_r2, color='green', lw=2, linestyle='--')
plt.xlabel('% lower status of the population [LSTAT]')
plt.ylabel('Price in $1000\'s [MEDV]')
plt.legend(loc='upper right')
plt.show()
def get_cl(tau, consider='EE', degree=5):
if consider == 'EE':
values = values_EE
else:
values = values_BB
v = values#[:100]
p = points#[:100]
poly = PolynomialFeatures(degree=degree)
# Vandermonde matrix of pre-computed paramter values.
X_ = poly.fit_transform(p.reshape(-1,1))
predict = np.array([tau]).reshape(1,-1)
# Creates matrix of values you want to estimate from the existing
# measurements. Computation speed scales very slowly when you ask for
# estimate of many sets of parameters.
predict_ = poly.fit_transform(predict)
clf = LinearRegression()
estimate = []
for l in range(2, v.shape[1]):
values_l = v[:,l]
clf.fit(X_, values_l)
estimate_l = clf.predict(predict_)
estimate.append(estimate_l)
estimate = np.array(estimate)
ell = np.arange(2, l+1)
Z = 2*np.pi/(ell*(ell+1))
return ell, Z*estimate[:,0]
def polynomial_expansion(self, rank=2): """ Expand the features with polynonial of rank rnank """ pf = PolynomialFeatures(degree=2) self.X_red = pf.fit_transform(self.X_red) self.X_white = pf.fit_transform(self.X_white)
def lassoRegression(X,y):
print("\n### ~~~~~~~~~~~~~~~~~~~~ ###")
print("Lasso Regression")
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
myDegree = 40
polynomialFeatures = PolynomialFeatures(degree=myDegree, include_bias=False)
Xp = polynomialFeatures.fit_transform(X)
myScaler = StandardScaler()
scaled_Xp = myScaler.fit_transform(Xp)
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
lassoRegression = Lasso(alpha=1e-7)
lassoRegression.fit(scaled_Xp,y)
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
dummyX = np.arange(0,2,0.01)
dummyX = dummyX.reshape((dummyX.shape[0],1))
dummyXp = polynomialFeatures.fit_transform(dummyX)
scaled_dummyXp = myScaler.transform(dummyXp)
dummyY = lassoRegression.predict(scaled_dummyXp)
outputFILE = 'plot-lassoRegression.png'
fig, ax = plt.subplots()
fig.set_size_inches(h = 6.0, w = 10.0)
ax.axis([0,2,0,15])
ax.scatter(X,y,color="black",s=10.0)
ax.plot(dummyX, dummyY, color='red', linewidth=1.5)
plt.savefig(filename = outputFILE, bbox_inches='tight', pad_inches=0.2, dpi = 600)
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
return( None )
def myTradingSystem(DATE, OPEN, HIGH, LOW, CLOSE, VOL, OI, P, R, RINFO, exposure, equity, settings):
""" This system uses linear regression to allocate capital into the desired equities"""
# Get parameters from setting
nMarkets = len(settings['markets'])
lookback = settings['lookback']
dimension = settings['dimension']
threshold = settings['threshold']
pos = np.zeros(nMarkets, dtype=np.float)
poly = PolynomialFeatures(degree=dimension)
for market in range(nMarkets):
reg = linear_model.LinearRegression()
try:
reg.fit(poly.fit_transform(np.arange(lookback).reshape(-1, 1)), CLOSE[:, market])
trend = (reg.predict(poly.fit_transform(np.array([[lookback]]))) - CLOSE[-1, market]) / CLOSE[-1, market]
if abs(trend[0]) < threshold:
trend[0] = 0
pos[market] = np.sign(trend)
# for NaN data set position to 0
except ValueError:
pos[market] = .0
return pos, settings
def detrend(data, degree=1):
"""
Take 2D (i.e. image) data and remove the background using a polynomial fit
Eventually this will be generalized to data of any dimension and perhaps
Parameters
----------
data : ndarray (NxM)
data to detrend
degree : int
the degree of the polynomial with which to model the background
Returns
-------
out : tuple of ndarrays (NxM)
(data without background and background)
"""
x = np.arange(data.shape[1])
y = np.arange(data.shape[0])
xx, yy = np.meshgrid(x, y)
# We have to take our 2D data and transform it into a list of 2D
# coordinates
X = np.dstack((xx.ravel(), yy.ravel())).reshape((np.prod(data.shape), 2))
# We have to ravel our data so that it is a list of points
vector = data.ravel()
# now we can continue as before
predict = X
poly = PolynomialFeatures(degree)
X_ = poly.fit_transform(X)
predict_ = poly.fit_transform(predict)
clf = linear_model.RANSACRegressor()
# try the fit a few times, as it seems prone to failure
ntries = 10
for i in range(ntries):
try:
# try the fit
clf.fit(X_, vector)
except ValueError as e:
# except the fit but do nothing
# unless the number of tries has been reached
if i == ntries - 1:
# then raise the error
raise e
else:
# if no error is thrown, break out of the loop.
break
# we have to reshape our fit to mirror our original data
background = clf.predict(predict_).reshape(data.shape)
data_nb = data - background
return data_nb, background
def regression_polynomiale(dicon,value, test, test_value,degre):
X = dicon.values()
poly = PolynomialFeatures(degree=degre) #transform the input to the polynomial model
poly.fit_transform(X)
print(dicon.values())
model = Pipeline([('poly', PolynomialFeatures(degree=degre)),('linear', linear_model.LinearRegression(fit_intercept=False))])
model.fit(X,value.values())
model.named_steps['linear'].coef_
print("prediction du model polynomial : ", model.named_steps['linear'].predict(poly.fit_transform([4.0, 5.0, 9, 2, 91.1, 132.3, 812.1, 12.5, 15.9, 38.0, 5.4])))
def fitting(self,XTrain, YTrain, XTest,YTest ):
###trasformazione non lineare
for degree in range(2,3):
poly = PolynomialFeatures(degree=degree,include_bias=False)
XTrain_transf = poly.fit_transform(XTrain)
XTest_transf = poly.fit_transform(XTest)
##centratura dei dati
XTrain_transf, YTrain_, X_mean, y_mean, X_std = center_data(XTrain_transf, YTrain, fit_intercept=True, normalize = True)
XTest_transf, YTest_ = center_test(XTest_transf,YTest,X_mean,y_mean,X_std)
new_loss, _ =compute_lasso(XTrain_transf, YTrain_, XTest_transf, YTest_,score = "r2_score")
print("loss polinomio grado", str(degree),":", new_loss )
def interactor(df):
""" This function takes in a data frame and creates binary interaction
terms from all numerical and categorical variables as well as the assessment
questions, and outputs a data frame """
my_data_complete = df.dropna()
# interactions can only be done for non-missings
colnames = list(my_data_complete.columns.values)
# id and date columns
id_cols_list = [
x
for x in colnames # only for continuous vars
if not (bool(re.search("_N$", x)) | bool(re.search("_C$", x)) | bool(re.search("_Q$", x)))
]
# actual feature columns - to make interactions from
new_cols_list = [
x
for x in colnames # only for continuous vars
if (bool(re.search("_N$", x)) | bool(re.search("_C$", x)) | bool(re.search("_Q$", x)))
]
othervars = my_data_complete[id_cols_list]
little_df = my_data_complete[new_cols_list]
# computing all binary interaction terms
poly = PolynomialFeatures(degree=2, interaction_only=True)
theints = pd.DataFrame(poly.fit_transform(little_df))
theints = theints.drop(theints.columns[0], axis=1) # dropping the first column
theints.columns = list(new_cols_list + list(itertools.combinations(new_cols_list, 2)))
# concatenating the interaction terms to the original data frame
df = pd.DataFrame(othervars.join(theints))
new_features = theints.columns.values
return df, new_features
def learning_curve(classifier, X, y, cv, sample_sizes,
degree=1, pickle_path=None, verbose=True):
""" Learning curve
"""
learning_curves = []
for i, (train_index, test_index) in enumerate(cv):
X_train = X[train_index]
X_test = X[test_index]
y_train = y[train_index]
y_test = y[test_index]
if degree > 1:
poly = PolynomialFeatures(degree=degree, interaction_only=False, include_bias=True)
X_train = poly.fit_transform(X_train)
X_test = poly.transform(X_test)
lc = []
for sample in sample_sizes:
classifier.fit(X_train[:sample], y_train[:sample])
# apply classifier on test set
y_pred = classifier.predict(X_test)
confusion = metrics.confusion_matrix(y_test, y_pred)
lc.append(balanced_accuracy_expected(confusion))
learning_curves.append(lc)
if verbose: print(i, end=' ')
# pickle learning curve
if pickle_path:
with open(pickle_path, 'wb') as f:
pickle.dump(learning_curves, f, protocol=4)
if verbose: print()
class SoftmaxLayer:
"""Class encapsulating the functionality of the final softmax layer pertaining to a neural network."""
num_neurons = None
neuron_outs = None
learn_rate = 0.
momentum = 0.
num_inputs_per_neuron = 0
z = None
y = None
diffs = None
weights = None
__prev_delta_weights = None
__poly = None
def __init__(self, num_outs, max_num_inputs, learnRate=.0001, momentum=.0005, randomStartWeights=False):
self.neuron_outs = np.zeros(num_outs)
self.num_neurons = num_outs
self.learn_rate = learnRate
self.momentum = momentum
self.num_inputs_per_neuron = max_num_inputs + 1 #1 is added to incorporate the bias weight.
if randomStartWeights is True:
self.weights = np.random.rand(self.num_neurons, self.num_inputs_per_neuron)
else:
self.weights = np.ones([self.num_neurons, self.num_inputs_per_neuron])
self.__prev_delta_weights = np.zeros([self.num_neurons, self.num_inputs_per_neuron])
self.__poly = PolynomialFeatures(1)
def load_Zs(self, z):
self.z = copy.deepcopy(self.__poly.fit_transform(z))
def load_Ys(self, y):
self.y = copy.deepcopy(y)
def softmax(self, arr, j):
if arr[j] < 10:
res = np.exp(arr[j]) / np.sum(np.exp(arr))
else:
arr -= arr.max()
res = np.exp(arr[j]) / np.sum(np.exp(arr))
return res
def estimate_Ys(self, z=None):
if z is not None:
self.load_Zs(z)
prods = np.dot(self.z, self.weights.T)
self.neuron_outs = np.array([[self.softmax(prod, i) for i in xrange(prods.shape[1])] for prod in prods])
def train_layer(self):
self.diffs = self.neuron_outs - self.y
delta_weights = np.array([np.sum([d[i] * self.z[i] for i in xrange(d.shape[0])], axis=0) for d in self.diffs.T])
self.weights -= delta_weights * self.learn_rate + self.__prev_delta_weights * self.momentum
self.__prev_delta_weights = copy.deepcopy(delta_weights)
def predict(self, x):
## as it is trained on polynominal features, we need to transform x
poly = PolynomialFeatures(degree=self.degree)
polynominal_features = poly.fit_transform(x)[0]
print polynominal_features.reshape
return self.model.predict(polynominal_features)
def main():
testfile = sys.argv[1]
modelfile = sys.argv[2]
polyorder = int(sys.argv[3])
testweeks = sys.argv[4]
test_data = np.genfromtxt(testfile, delimiter=',', skip_header=1)
X = test_data[:,:-1]
y = test_data[:,-1]
poly = PolynomialFeatures(degree=polyorder)
Xpoly = poly.fit_transform(X)
with open(modelfile, 'rb') as model, open(testweeks) as weeks:
lr = pickle.load(model)
games_per_week = (int(line) for line in weeks)
ranges = []
pos = 0
for week in games_per_week:
newpos = pos + week
ranges.append( (pos, newpos) )
pos = newpos
print('W\tL\tPoints')
weekly_results = (evaluate_week(week, Xpoly, y, lr) for week in ranges)
for result in weekly_results:
print('\t'.join(str(piece) for piece in result))
def mvr(data):
x = data[:, 0:len(data[0])-1]
y = data[:, -1]
minTestingError = np.inf
for dim in xrange(1,3):
if(dim > 1):
print("Mapping into higher dimension of {} \n".format(dim))
else:
evaluateGradientDesc(data)
print("Explicit solution\n")
poly = PolynomialFeatures(dim)
z = poly.fit_transform(x)
theta = fitModel(z , y)
print("Intercept : {} \nCoefficients : {}\n".format(theta[0], theta[1:]))
testingError, sol = evaluateModel(z,y, False)
if(dim == 1):
print "Testing Error :", testingError
if (testingError < minTestingError):
minTestingError = testingError
optimalDimension = dim
optSol = sol
print "Optimal Dimension : {}, Testing Error : {} ".format(optimalDimension, minTestingError)
return optSol
def test_polynomial_fits(x, y, n_comps, model, k_folds=3):
for i in range(1,6):
poly = PolynomialFeatures(degree=i)
poly_x = poly.fit_transform(x)
r2_mean, r2_std, mse_mean, mse_std = run_conventional_linkage(poly_x, y, n_comps, model)
print r2_mean, r2_std, mse_mean, mse_std
print
def computeZs(w, x_train):
z = []
for i in xrange(x_train.shape[0]):
z.append(np.array([sigmoid(w[j], x_train[i]) for j in xrange(w.shape[0])]))
poly = PolynomialFeatures(1)
z = poly.fit_transform(z)
return np.array(z)
def prob_max(x):
poly=PolynomialFeatures(degree=2)
x=poly.fit_transform(x)
####define best fit coefficient arrays
theta_0=np.array([5.017034759466216798e+00,-4.953976374628412532e-02,-5.853604893727188709e-03,-1.732076056200582692e-01,4.646876717720006822e-02,-2.787195959859810248e-04,-1.222728739255723981e-07,6.120106921025333935e-02,4.604924515407455714e-06,-1.475861223279032741e-01,-4.060326310707941784e-09,1.177855732870812001e-02,3.113699082333943463e-02,-8.110887996756119586e-12,-1.113811480228766704e-05,-1.501651909640449069e-07,-2.190797370951344465e-06,-1.718990505473245339e-05,-1.199898098055512375e-13,-2.571924773608319866e-07,-2.147269697093823931e-12,-3.256296536440236682e-05,-2.581007347409745425e-05,1.392377894191479523e-03,-4.129157456238496948e-02,-1.811677361205055875e-02,-7.083807139833804416e-06,4.116671309652412958e-02,3.361594896247442773e-04,-8.223201336497298203e-03,-1.862209709284966395e-07,1.527880447451521184e-02,-3.672245027121902317e-02,-4.975817315933817863e-10,-6.237344094335352815e-04,-1.217106713769066128e-05,-1.489610233924158246e-04,-1.156461881655085214e-03,-5.159561821638818347e-12,-1.884192981459143558e-05,-1.825179242529750414e-10,-5.438522396156177874e-07,4.167833399722946711e-05,5.607144654864255374e-03,-3.093787958451529527e-02,-2.041422430639949412e-04,7.895983583095988675e-03,1.293062803926413491e-02,5.899640081165494730e-03,-1.021176015149306061e-05,8.486220614842233598e-03,5.822368958314040610e-03,-2.243937133831174112e-08,-8.464968966797879399e-03,-1.906386791427585779e-04,-1.795243901952780228e-03,-1.046895210502369993e-02,-3.330917120202175767e-10,-4.235251180738666644e-04,-5.694559236692822056e-09,-1.583929993116185621e-03,1.629024063907276165e-01,-6.967989967191325247e-03,-3.673107962032413740e-06,-2.280088579624509337e-01,1.726846693277796316e-04,1.013912471248917396e-01,-7.647706080406362405e-08,-3.240179256710575273e-01,1.214811767523774205e-01,-3.401281050759457049e-10,-1.670938331047893612e-07,-7.369899627351106136e-06,-9.856333774434332797e-05,-4.534506039623955074e-05,-9.599184784444142215e-12,-5.151527253102048208e-06,-1.030689454605035745e-10,4.646876717720006822e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.787195959859810248e-04,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.222728739255723981e-07,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,6.120106921025333935e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,4.604924515407455714e-06,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.475861223279032741e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-4.060326310707941784e-09,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,1.177855732870812001e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,3.113699082333943463e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.110887996756119586e-12,0.000000000000000000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theta_1=np.array([-9.861773710361221745e+00,1.930173314618176539e-01,6.178220889126870112e-03,9.590504030349773779e-02,-2.071552578564340164e-01,1.061401114719375260e-01,3.276754675639340086e-02,-1.761411784826558136e-02,-1.219468120911304441e-06,-9.174348236081142360e-02,2.132582599900693932e-02,-1.168137887722866912e-02,1.014151234082172059e-01,9.598987135568204463e-04,-4.542651588690940767e-02,-7.514592183950008714e-04,1.113651743862166532e-03,3.535033504077587929e-02,1.348878960300472899e-06,4.158088521609443200e-02,1.744377835470558925e-06,-8.830070079582454969e-04,-6.118986593694282407e-05,-4.784785490618059583e-04,6.231388236713353984e-02,1.984193321394733464e-02,3.807758267577563555e-02,-1.857758661936751918e-02,-8.902117282652563328e-05,1.684544497032977118e-03,-4.354918224284388961e-02,8.135671785350261087e-03,1.838040795364327554e-03,4.648089395429296639e-02,1.603282923510754299e-02,-5.706248765095311287e-02,6.474737189221846378e-02,-1.666585875194382532e-02,5.800179529291954185e-05,6.960244357250958136e-02,1.482721160150063508e-04,-5.299760763074679222e-07,-4.512899253144341872e-05,-9.330422825892547602e-04,-3.692049341246863322e-04,-7.641113350637301687e-04,-3.553288559473667197e-04,-3.424266483519060756e-03,4.323086081437536800e-04,-4.955185382381825611e-04,-5.468633412309427573e-03,3.023053081335558886e-04,2.032432933463332054e-03,-1.868881428527514009e-04,5.907286677952040300e-03,1.224575926635180362e-03,1.491552037995557810e-03,3.744487993794240379e-03,-1.585824627682363985e-03,4.626090019667926378e-03,2.914276434916693195e-04,-6.421237001048539506e-04,1.343912634023189216e-02,1.202887078507273999e-02,4.579648647433440592e-03,-4.573005453417482836e-05,-2.603037492365091118e-02,1.093608117200833424e-01,3.532167048002045617e-01,-1.790610728587208392e-02,-7.755213616683120925e-02,-5.213887650785711293e-03,-1.747560651202587356e-01,-4.635745132339050972e-02,-5.689835106400319142e-02,1.079103168240419384e-04,8.490464847112829186e-03,8.373013610258914587e-05,-2.071552578564340164e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,1.061401114719375260e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,3.276754675639340086e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.761411784826558136e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.219468120911304441e-06,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-9.174348236081142360e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,2.132582599900693932e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.168137887722866912e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,1.014151234082172059e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,9.598987135568204463e-04,0.000000000000000000e+00,0.00000000000000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theta_2=np.array([-2.604982997969506187e+01,2.522175474048852784e-01,6.275718741926675920e-03,1.273176496282046599e-01,-1.716361908427019300e-01,-8.312891928874267811e-02,4.068642760504040390e-02,-2.445951924349220458e-02,-8.746331909292573688e-07,-1.542657353435612777e-02,-1.765684782956331370e-02,-3.195224775777173168e-04,2.484350665759416446e-02,4.813993958703906978e-03,1.759866699719525307e-01,5.747258345660388864e-04,-1.022129045161229450e-03,5.567310929387970370e-02,-9.063835339582872293e-07,-8.930479773136143495e-02,-9.138473645722535673e-07,-7.459379939882724523e-04,-4.125238423403655301e-05,-4.278814974555324602e-04,1.234252674940865789e-02,4.708747007997553247e-02,2.657070242802176546e-02,6.926664427951148562e-02,-6.384822293781164664e-05,4.964280033292678418e-02,9.853135356553717472e-02,-2.621681271491586862e-02,6.630289966406467672e-02,-2.208061355155441774e-01,4.922574438806641417e-02,4.310173077725486246e-02,-5.622794820973487512e-02,1.006576646572381883e-01,-3.897449196020566275e-05,-7.080593340274707326e-03,-7.767702598866720021e-05,-3.990070230109308789e-07,-1.651061082255117919e-05,1.537024690049966936e-03,8.005698436542285070e-04,8.994568249232704014e-04,5.470196351385650481e-04,-2.455970000128474082e-03,4.988277998095904915e-04,1.262763556509414152e-03,4.601679612131920685e-03,-1.194497842888761268e-04,2.882224654372331132e-03,5.875401491502118233e-04,-2.458015081252763658e-03,5.859965255224170106e-04,-2.547687917446368093e-04,-2.516120690268733393e-03,2.300462784971263851e-03,-2.423523210845587861e-03,-1.539288004294190964e-04,-1.260645266526524456e-02,-2.136594669075533859e-02,-1.240381092246360673e-02,1.775253607050698845e-02,-3.279874465984122252e-05,1.667948986384345557e-03,-1.177656364439296638e-01,-8.947706286380961412e-04,5.282554584883104691e-03,9.528953029071411673e-02,-1.953324553475337816e-03,1.692159896831275101e-01,6.332910268512657870e-02,-3.059270306265245501e-02,-7.251068271668771679e-05,-2.748819360572268139e-02,-4.386467349947201168e-05,-1.716361908427019300e-01,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.312891928874267811e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,4.068642760504040390e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.445951924349220458e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.746331909292573688e-07,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.542657353435612777e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.765684782956331370e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-3.195224775777173168e-04,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,2.484350665759416446e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,4.813993958703906978e-03,0.000000000000000000e+00,0.000000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theta_3=np.array([-2.972725322795918140e-02,-2.504227156229747453e-01,-9.722118342779062158e-03,1.229149113213241912e-01,2.039923850853684467e-02,-1.805107341267933943e-02,7.334563069172345476e-03,-6.475321568310828764e-04,-8.474944289249388250e-08,9.714545617984883855e-05,-2.075035998257516458e-07,-1.221820060933139164e-05,-1.714475447964966190e-02,-2.129377838506303893e-03,-1.277321374533818017e-06,-2.380156363764723250e-06,-5.273025783548628525e-06,-1.984111789391731009e-03,-1.335426121119178434e-07,3.339996589013074558e-04,-2.141039464532945376e-07,2.576647414932395786e-03,2.945797215512836505e-06,-1.895000552612198606e-04,-1.462288947682845522e-02,-1.951654268095479733e-02,-1.630737487857820273e-02,-5.655678104343885015e-02,-6.186709331152055607e-06,-2.000570956968860184e-02,-1.460798045208372536e-05,-9.892777618470509349e-04,-6.134943418829629652e-02,5.204243735868804843e-02,-6.976997540713989398e-05,-1.924795146172466416e-04,-3.585644621246710678e-04,-8.751744876445026466e-02,-5.742332320812468485e-06,-2.493414480788682872e-02,-1.819883544853002577e-05,1.673188626427698019e-06,-2.199004526442708865e-05,3.929065891591175703e-03,3.411106034336343351e-03,4.689455918427083009e-03,-1.623583183295337906e-02,-2.379764356421232188e-04,4.563617516957610247e-03,-5.845377676580722996e-04,-5.550332977089329420e-03,5.817926665026248653e-03,3.489254807540806587e-03,-8.968364091790517831e-04,-2.770023159211470760e-03,-4.227833625220314175e-03,1.685174688793472349e-03,-3.707142912226834078e-04,-5.865829701672146956e-03,-5.678036659941369801e-04,8.344188249964974876e-05,-2.863247383273172242e-02,-6.482258485425367728e-03,-4.199374526758931081e-02,-1.256077522453134809e-02,-3.178104108468527999e-06,-3.440396173308768457e-02,-9.901849306080791411e-06,-3.180423753092536477e-05,-7.452030759889874401e-02,-6.907406950607837548e-02,5.971308793973397274e-07,-1.155260382492086013e-04,-2.332571299853613211e-04,1.410515664042338024e-01,-1.068340896895342663e-05,2.499449671921087357e-01,-1.027698942975813230e-05,2.039923850853684467e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.805107341267933943e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,7.334563069172345476e-03,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-6.475321568310828764e-04,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-8.474944289249388250e-08,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,9.714545617984883855e-05,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.075035998257516458e-07,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.221820060933139164e-05,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-1.714475447964966190e-02,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,-2.129377838506303893e-03,0.000000000000000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#####calculate probabilities
z_0=np.sum(x*theta_0)
z_1=np.sum(x*theta_1)
z_2=np.sum(x*theta_2)
z_3=np.sum(x*theta_3)
p_0=1./(1.+np.exp(-z_0))
p_1=1./(1.+np.exp(-z_1))
p_2=1./(1.+np.exp(-z_2))
p_3=1./(1.+np.exp(-z_3))
prob_arra=np.array([p_0, p_1, p_2, p_3])
return prob_arra.argmax()
def init_predict(mode):
""" 整理为用于预测的 X
i: features
o: X
"""
import scipy.io as sio
import scipy as sp
from sklearn.preprocessing import PolynomialFeatures
uid_ave = sio.loadmat('predict_cut_uid_ave.mat')['X']
poly = PolynomialFeatures(degree=2)
poly_uid_ave = poly.fit_transform(uid_ave)
combined_list = [sp.sparse.csc_matrix(poly_uid_ave)]
if mode == 'f':
X_words = sio.loadmat('predict_cut_Xf.mat')['X']
elif mode == 'c':
X_words = sio.loadmat('predict_cut_Xc.mat')['X']
else:
X_words = sio.loadmat('predict_cut_Xl.mat')['X']
#transformer = TfidfTransformer()
#X_tfidf = transformer.fit_transform(X_words)
combined_list.append(X_words)
X = sp.sparse.hstack(combined_list)
print(X.shape)
return X
def predict(self, X, coefs):
# first column of Z is time
# we will replace the other columns with regressed data
# clean-up from before
Z = self.X.copy()
print type(Z), Z.head()
print type(coefs), coefs.head()
poly = PolynomialFeatures(degree=self.n)
for trial_index, (coefficients, x) in enumerate(izip(coefs, Z)):
print trial_index, coefficients.shape, x.shape
# reshape required by t
t = poly.fit_transform((x[:,0]).reshape(-1,1))
# only regress on data past reference time
t = t[self.reference_time:]
z = np.zeros(x.shape)
# first column is time
z[:,0] = x[:,0]
# columns up to reference time are just 0 and were not regressed
z[:self.reference_time, 1:] = 0
# columns after reference_time were regressed with coefficients
print t.shape, z.shape, coefficients.shape
z[self.reference_time:, 1:] = np.dot(t, coefficients)
Z.iloc[trial_index] = z
return Z
def set_pdynome_degree(degree, lis):
lis = [lis]
ploy = PolynomialFeatures(degree)
result = ploy.fit_transform(lis)
result = result.tolist()
result = result[0]
return result
def prepare(file, survived_info=True):
df = pd.read_csv(file, header=0)
df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked')], axis=1)
df = pd.concat([df, pd.get_dummies(df['Sex'], prefix='Sex')], axis=1)
df = pd.concat([df, pd.get_dummies(df['Pclass'], prefix='Pclass')], axis=1)
df = df.fillna(value={'Age': df['Age'].dropna().median(), 'Fare': df['Fare'].dropna().median()})
survived = None
if survived_info:
survived = df['Survived'].values
df = df.drop(['Survived'], axis=1)
ids = df['PassengerId'].values
df = df.drop(['PassengerId', 'Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1)
poly = PolynomialFeatures(interaction_only=True)
polydata = poly.fit_transform(df)
cols = np.hstack((['1s'], df.columns, [None]*(polydata.shape[1] - len(df.columns) -1)))
polydf = pd.DataFrame.from_records(polydata, columns=cols)
if survived_info: polydf['Survived'] = survived
return (polydf, ids)
def log_reg_kclass(x, y, nfolds=4, degree=1, limit=None):
"""Performs logistic regression experiments on Iris dataset for k class discrimination."""
#print 'Training k Class classifier on Iris dataset'
if limit is not None:
print 'Considering only', limit, ' datapoints'
x = x[:limit]
y = y[:limit]
#x /= x.max(axis=0)
poly = PolynomialFeatures(degree)
x = poly.fit_transform(x)
num_classes = len(set(y))
avg_accuracy = 0.; avg_precision = np.zeros(num_classes); avg_recall = np.zeros(num_classes); avg_fscore = np.zeros(num_classes); avg_conf_mat = np.zeros([num_classes, num_classes])
kf = KFold(y.shape[0], n_folds=nfolds, shuffle=True)
for train_ids, test_ids in kf:
thetas = log_reg_trainer_kclass(x[train_ids], y[train_ids])
y_pred = log_reg_pred_kclass(thetas, x[test_ids])
acc = accuracy_score(y[test_ids], y_pred)
avg_accuracy += acc
precision1, recall1, fscore1, supp1 = precision_recall_fscore_support(y[test_ids], y_pred)
conf_mat = confusion_matrix(y[test_ids], y_pred)
avg_precision += precision1; avg_recall += recall1; avg_fscore += fscore1; avg_conf_mat += conf_mat
lol=0
return avg_accuracy / nfolds, avg_precision / nfolds, avg_recall / nfolds, avg_fscore / nfolds, avg_conf_mat / nfolds
def poly_model(ins,outs,degrees):
poly = PolynomialFeatures(degree=degrees)
X = poly.fit_transform(ins)
regr = linear_model.LinearRegression()
regr.fit(X, outs)
print_model("poly-"+str(degrees), regr, X, outs)
def hidden_layer(self, X, w):
# The dimension of matrix Z is (R + 1) * m. The extra dimension is constant
# extra 1 dimension for bias.
Z = sigmoid(np.dot(X, w.T))
p = PolynomialFeatures(degree = 1)
Z = p.fit_transform(Z)
return Z
dataset = pd.read_csv("Position_Salaries.csv")
#preparing attributes and dependent values
X = dataset.iloc[:, 1:2].values #attributes
Y = dataset.iloc[:, 2:3].values #dependent values
#__________________________________________POLYNOMIAL REGRESSION__________________________________________
# >>> Fitting first linear regression to the dataset
lin_reg = LinearRegression()
lin_reg.fit(X, Y)
# >>> Fitting Polynomial regression to the dataset
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
# >>> Fitting second linear regression to the polynomial dataset
lin_reg2 = LinearRegression()
lin_reg2.fit(X_poly, Y)
#__________________________________________VISUALIZATION__________________________________________
# >>> Linear regression plot
plt.scatter(X, Y, color="red")
plt.plot(X, lin_reg.predict(X))
plt.title("Linear Regression")
plt.xlabel("Position")
plt.ylabel("Salary")
plt.show()
Y = dataset.iloc[:, 2:].values
#Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, Y)
#Fitting Polynomial Regression to the dataset
#Below polymorphic object is a transformer tool
#that will transform our matrix of features X into
#a new matrix of features named X_poly
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=3)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, Y)
#Once this new matrix of polynomial features X_poly was created
#Near linear regression object that we fitted to this new matrix X_poly and Y
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, Y)
#Visualising the Linear Regression Results
plt.scatter(X, Y, color="red")
plt.plot(X, lin_reg.predict(X), color="blue")
plt.title("Truth or Bluff (Linear Regression)")
plt.xlabel("Position Level")
plt.ylabel("Salary")
plt.show
#Reseting the dataset1
dataset1 = dataset1.reset_index()
#Spliting the Date Coloumn
dataset1['Date'] = pd.to_datetime(dataset1['Date'])
dataset1.insert(1, "year", dataset1.Date.dt.year, True)
dataset1.insert(2, "month", dataset1.Date.dt.month, True)
dataset1.insert(3, "Day", dataset1.Date.dt.day, True)
#Droping the Date coloumn
dataset1.drop('Date', axis=1, inplace=True)
#Dividing the dataset1 into X and Y Matrix
X = dataset1.filter(['year', 'month', 'Day'])
Y = dataset1.filter(['Total cases'])
#Dividing the dataset1 into training and testing data
#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, random_state = 0)
#Training the Model with train data
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, Y)
#Predecting the Model by giving Values
Y_pred = lin_reg_2.predict(poly_reg.fit_transform([['2020', '3', '10']]))
p = dataset.loc[(dataset.Source == 'GCAG'), ['Year']]
q = dataset.loc[(dataset.Source == 'GCAG'), ['Mean']]
# Splitting the dataset into the Training set and Test set
"""from sklearn.cross_validation import train_test_split
p_train, p_test, q_train, q_test = train_test_split(p, q, test_size = 0.2, random_state = 0)"""
# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(p, q)
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=4)
p_poly = poly_reg.fit_transform(p)
poly_reg.fit(p_poly, q)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(p_poly, q)
# Visualising the Linear Regression results
plt.scatter(p, q, color='brown') # Creating Scatter Plot
plt.plot(p, lin_reg.predict(p), color='orange',
label='Best Fit Line') # Creating Best Fit Line
tnrfont = {'fontname': 'Times New Roman'} # Setting the font "Times New Roman"
plt.title('Linear Regression for GCAG', **tnrfont) # Setting the Title
plt.xlabel('Year', **tnrfont) # Labelling x-axis
plt.ylabel('Mean Temperature', **tnrfont) #Labelling y-axis
plt.grid(color='grey', linestyle='-', linewidth=0.25,
alpha=0.5) # Creating Grid
leg = plt.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)
from sklearn.preprocessing import PolynomialFeatures from sklearn.grid_search import GridSearchCV X = [[6], [8], [10], [14], [18]] Y = [[7], [9], [13], [17.5], [18]] poly2 = PolynomialFeatures(degree=2) print(poly2.fit_transform(X))
from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) sc_y = StandardScaler() Y_train = sc_y.fit_transform(y_train.reshape(-1, 1)) #Linear regression from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(X_train, y_train) #poly regression from sklearn.preprocessing import PolynomialFeatures poly_reg = PolynomialFeatures(degree=4) X_poly = poly_reg.fit_transform(X) poly_reg.fit(X_poly, y) lin_reg_2 = LinearRegression() lin_reg_2.fit(X_poly, y) lin_reg_2.predict(poly_reg.fit_transform([[6.5]])) #svr from sklearn.svm import SVR regressor = SVR(kernel='rbf') regressor.fit(X, y) #decision tree regression from sklearn.tree import DecisionTreeRegressor regressor = DecisionTreeRegressor(random_state=0) regressor.fit(X, y)
# Polynomial Regression
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Position_salaries.csv')
X = dataset.iloc[:, 1:2].values
Y = dataset.iloc[:, 2].values
# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, Y)
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, Y)
# Visualising the Polynomial Regression and Linear Regression together
plt.scatter(X, Y, color = 'red')
plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')
plt.plot(X, lin_reg.predict(X), color = 'violet')
plt.xlabel('Position Level of an Employee')
plt.ylabel('Salary')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.show()
y = dataset.iloc[:, 2].values #y is vector #check the kind of relationship so obtained plt.plot(x, y) #do not divide into train and test set, because less data and more accuracy is necessary #make linear regression model just as a reference from sklearn.linear_model import LinearRegression linreg = LinearRegression() linreg.fit(x, y) #make polynomial regression from sklearn.preprocessing import PolynomialFeatures polyreg = PolynomialFeatures(degree=4) x_poly = polyreg.fit_transform(x) linreg2 = LinearRegression() linreg2.fit(x_poly, y) #visualising linear model plt.scatter(x, y, color='red') plt.plot(x, linreg.predict(x), color='blue') plt.show() #visualising polynomialmodel plt.scatter(x, y, color='red') #do not use x_poly as y here because it's already defined and we want to generalise the model for all inputs plt.plot(x, linreg2.predict(polyreg.fit_transform(x)), color='blue') plt.show()
elanet = ElasticNet(alpha=1.0, l1_ratio=0.5)
# if we set the l1_ratio to 1.0, elanet would be equal to Lasso
"""# Turning a linear regression model into a curve - polynomial regression"""
# AN EXAMPLE
# First adding a second degree polynomial term
from sklearn.preprocessing import PolynomialFeatures
X = np.array([258.0, 270.0, 294.0, 320.0, 342.0,
368.0, 396.0, 446.0, 480.0, 586.0])[:, np.newaxis]
y = np.array([236.4, 234.4, 252.8, 298.6, 314.2,
342.2, 360.8, 368.0, 391.2, 390.8])
lr = LinearRegression()
pr = LinearRegression()
quadratic = PolynomialFeatures(degree=2)
X_quad = quadratic.fit_transform(X)
# Fitting a linear regression model for comparison
lr.fit(X, y)
X_fit = np.arange(250, 600, 10)[:, np.newaxis]
y_lin_fit = lr.predict(X_fit)
# Fitting a multiple regression model on the transformed features for
# polynomial regression
pr.fit(X_quad, y)
y_quad_fit = pr.predict(quadratic.fit_transform(X_fit))
# Plotting the results
plt.scatter(X, y, label='Training Points')
plt.plot(X_fit, y_lin_fit, label='Linear Fit', linestyle='--')
plt.plot(X_fit, y_quad_fit, label='Quadratic Fit')
@author: lopes
"""
import pandas as pd
import numpy as np
df = pd.read_csv('house_prices.csv')
X = df.iloc[:, 3:19].values
y = df.iloc[:, 2].values
from sklearn.model_selection import train_test_split
X_treinamento, X_teste, y_treinamento, y_teste = train_test_split(
X, y, random_state=0)
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=4)
X_treinamento_poly = poly.fit_transform(X_treinamento)
X_teste_poly = poly.transform(X_teste)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_treinamento_poly, y_treinamento)
score = regressor.score(X_teste_poly, y_teste)
previsoes = regressor.predict(X_teste_poly)
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(y_teste, previsoes)
threshold = 2
df_label = df_label[(z < threshold)]
# Reset the index for the polynomial features merge
df_label = df_label.reset_index(drop=True)
# Get polynomial features
polyTrans = PolynomialFeatures(degree=2, include_bias=False)
df_label_Num = df_label[[
"level", "temperature", "usage", "Brightness", "RAM"
]]
df_label = df_label.drop(
["level", "temperature", "usage", "Brightness", "RAM"],
axis=1) # Drop them to get back later the poly Trans of them
polyData_Num = polyTrans.fit_transform(df_label_Num)
columnNames = polyTrans.get_feature_names(
["level", "temperature", "usage", "Brightness", "RAM"])
df_label_Num = pandas.DataFrame(polyData_Num, columns=columnNames)
for column in columnNames:
df_label[column] = pandas.Series(df_label_Num[column])
# Get dataframes
y_label = df_label["output"]
X_label = df_label.drop(["output"], axis=1)
# Split data training and testing ...
X_train_label, X_test_label, y_train_label, y_test_label = train_test_split(
X_label, y_label, test_size=0.25, random_state=42)
for i in range(0,train_rows):
row = raw_input().split(" ")
row = [float(i) for i in row]
train_features.append(row[0:2])
train_value.append(row[-1])
test_rows = int(raw_input())
for i in range(0,test_rows):
row = raw_input().split(" ")
row = [float(i) for i in row]
test_features.append(row[0:2])
poly_feature = PolynomialFeatures(degree=3)
train_features = poly_feature.fit_transform(train_features)
model = LinearRegression().fit(train_features,train_value)
test_features = poly_feature.fit_transform(test_features)
prediction=model.predict(test_features)
for i in prediction:
print round(i,2)
data_set = pd.read_csv('./Position_Salaries.csv')
print(data_set)
X = data_set.iloc[:, 1:2].values
y = data_set.iloc[:, 2:3].values
print(X)
print(y)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
poly_reg = PolynomialFeatures(degree=10)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)
linear_reg = LinearRegression()
linear_reg.fit(X_poly, y)
print(X)
pred_y = linear_reg.predict(X_poly)
print(pred_y)
plt.scatter(X, y, color='red')
plt.plot(X, pred_y, color='green')
plt.show()
def get_poly_features(X, degree):
poly = PolynomialFeatures(degree, interaction_only=True)
X2 = poly.fit_transform(X)
return X2
df = pd.read_csv("Position_Salaries.csv")
X = df.iloc[:, 1:2].values
y = df.iloc[:, 2].values
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X, y)
"""nessa parte PolynomialFeatures é uma ferramenta
usada para transformar uma matriz em forma polynomial, ou seja,
criando os fatores ao quadrado.
Feito isso, voce cria outro objeto linear com a matrix polynomial
"""
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)
regressor_2 = LinearRegression()
regressor_2.fit(X_poly, y)
plt.scatter(X, y, color='red')
plt.plot(X, regressor.predict(X), color='blue')
plt.show()
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color='red')
plt.plot(X_grid,
regressor_2.predict(poly_reg.fit_transform(X_grid)),
color='blue')
plt.show()
class ContinuousToPolynomialBasisHypergridAdapter(HypergridAdapter):
""" Adds polynomial basis function features for each continuous dimension in the adaptee hypergrid using
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html.
All non-continuous adaptee dimensions will be present in the target hypergrid.
Beware: Because HierarchicalHypergrids may have NaN values for some points, these NaNs will be replaced by zeros.
Parameters
----------
degree: integer
The degree of the polynomial features.
Default = 2.
interaction_only: boolean
If true, only interaction features are produced: features that are products of at most degree distinct input features
(so not x[1] ** 2, x[0] * x[2] ** 3, etc.).
Default = False
include_bias: boolean
If True, then include a bias column, the feature in which all polynomial powers are zero
(i.e. a column of ones - acts as an intercept term in a linear model).
Default = True
"""
def __init__(self,
adaptee: Hypergrid,
degree: int = 2,
include_bias: bool = True,
interaction_only: bool = False):
if not HypergridAdapter.is_like_simple_hypergrid(adaptee):
raise ValueError("Adaptee must implement a Hypergrid Interface.")
HypergridAdapter.__init__(self,
name=adaptee.name,
random_state=adaptee.random_state)
self._adaptee: Hypergrid = adaptee
self._polynomial_features_kwargs = {
'degree': degree,
'interaction_only': interaction_only,
'include_bias': include_bias,
'order': 'C'
}
self._target: Hypergrid = None
if self._adaptee.is_hierarchical():
self._adaptee = HierarchicalToFlatHypergridAdapter(
adaptee=self._adaptee)
# Record which adaptee dimensions are continuous
self._adaptee_contains_dimensions_to_transform = False
self._adaptee_dimension_names_to_transform = []
for adaptee_dimension in self._adaptee.dimensions:
if isinstance(adaptee_dimension, ContinuousDimension):
self._adaptee_dimension_names_to_transform.append(
adaptee_dimension.name)
self._num_dimensions_to_transform = len(
self._adaptee_dimension_names_to_transform)
self._adaptee_contains_dimensions_to_transform = self._num_dimensions_to_transform > 0
# see definition of _get_polynomial_feature_names() for usage
self._internal_feature_name_terminal_char = '_'
# Since sklearn PolynomialFeatures does not accept NaNs and these may appear in data frames from hierarchical hypergrids,
# the NaNs will be replaced with an imputed (finite) value. The following sets the value used.
self._nan_imputed_finite_value = 0
# instantiate sklearn's polynomial features instance
self._polynomial_features = PolynomialFeatures(
**self._polynomial_features_kwargs)
# because the exact number of additional dimensions that will be added depends on the parameters to sklearn's PF,
# *and* the sklearn PF instance above doesn't determine this information until after the .fit() method is called (requiring a dataframe),
# *and* the target hypergrid can not be constructed without knowing the resulting number of continuous dimensions,
# a trivial dataframe is constructed (all 1s) and .fit_transform() of _polynomial_features instance is called.
trivial_continuous_dim_x = np.ones(
(1, self._num_dimensions_to_transform))
trivial_polynomial_features_y = self._polynomial_features.fit_transform(
trivial_continuous_dim_x)
self._polynomial_features_powers = self._polynomial_features.powers_
self._num_polynomial_basis_dimensions_in_target = trivial_polynomial_features_y.shape[
1]
self._target_polynomial_feature_map = {
} # keys are target dimension names, values are index in features
self._build_simple_hypergrid_target()
def _build_simple_hypergrid_target(self) -> None:
self._target = SimpleHypergrid(name=self._adaptee.name,
dimensions=None,
random_state=self._adaptee.random_state)
# Add non-transformed adaptee dimensions to the target
for adaptee_dimension in self._adaptee.dimensions:
if adaptee_dimension.name not in self._adaptee_dimension_names_to_transform:
self._target.add_dimension(adaptee_dimension.copy())
if not self._adaptee_contains_dimensions_to_transform:
return
# add new dimensions to be created by sklearn PolynomialFeatures
# construct target dim names using adaptee dim names and polynomial feature powers matrix
# This logic is worked out explicitly here so we have control over the derived dimension names.
# Currently, the code only substitutes adaptee feature names into the default feature_names produced by
# sklearn's PolynomialFeatures .get_feature_names() method.
poly_feature_dim_names = self._get_polynomial_feature_names()
for i, poly_feature_name in enumerate(poly_feature_dim_names):
ith_terms_powers = self._polynomial_features_powers[i]
if not self._polynomial_features_kwargs[
'include_bias'] and ith_terms_powers.sum() == 0:
# the constant term is skipped
continue
else:
# replace adaptee dim names for poly feature name {x0_, x1_, ...} representatives
target_dim_name = poly_feature_name
for j, adaptee_dim_name in enumerate(
self._adaptee_dimension_names_to_transform):
adaptee_dim_power = ith_terms_powers[j]
if adaptee_dim_power == 0:
continue
if adaptee_dim_power == 1:
poly_feature_adaptee_dim_name_standin = f'x{j}{self._internal_feature_name_terminal_char}'
adaptee_dim_replacement_name = adaptee_dim_name
else:
# power > 1 cases
poly_feature_adaptee_dim_name_standin = f'x{j}{self._internal_feature_name_terminal_char}^{adaptee_dim_power}'
adaptee_dim_replacement_name = f'{adaptee_dim_name}^{adaptee_dim_power}'
target_dim_name = target_dim_name.replace(
poly_feature_adaptee_dim_name_standin,
adaptee_dim_replacement_name)
# add target dimension
# min and max are placed at -Inf and +Inf since .random() on the target hypergrid is generated on the original
# hypergrid and passed through the adapters.
self._target.add_dimension(
ContinuousDimension(name=target_dim_name,
min=-math.inf,
max=math.inf))
self._target_polynomial_feature_map[target_dim_name] = i
@property
def adaptee(self) -> Hypergrid:
return self._adaptee
@property
def target(self) -> Hypergrid:
return self._target
@property
def polynomial_features_kwargs(self) -> dict:
return self._polynomial_features_kwargs
@property
def nan_imputed_finite_value(self):
return self._nan_imputed_finite_value
def get_column_names_for_polynomial_features(self, degree=None):
# column names ordered by target dimension index as this coincides with the polynomial_features.powers_ table
sorted_by_column_index = {
k: v
for k, v in sorted(self._target_polynomial_feature_map.items(),
key=lambda item: item[1])
}
if degree is None:
return list(sorted_by_column_index.keys())
dim_names = []
for ith_terms_powers, poly_feature_name in zip(
self._polynomial_features_powers,
self._get_polynomial_feature_names()):
if ith_terms_powers.sum() == degree:
dim_names.append(poly_feature_name)
return dim_names
def get_polynomial_feature_powers_table(self):
return self._polynomial_features_powers
def get_num_polynomial_features(self):
return self._polynomial_features_powers.shape[0]
def _get_polynomial_feature_names(self):
# The default polynomial feature feature names returned from .get_feature_names() look like: ['1', 'x0', 'x1', 'x0^2', 'x0 x1', 'x1^2']
# They are altered below by adding a terminal char so string substitutions don't confuse
# a derived feature named 'x1 x12' with another potentially derived feature named 'x10 x124'
replaceable_feature_names = []
for i in range(len(self._adaptee_dimension_names_to_transform)):
replaceable_feature_names.append(
f'x{i}{self._internal_feature_name_terminal_char}')
return self._polynomial_features.get_feature_names(
replaceable_feature_names)
def _project_dataframe(self, df: DataFrame, in_place=True) -> DataFrame:
if not in_place:
df = df.copy(deep=True)
# replace NaNs with zeros
df.fillna(self._nan_imputed_finite_value, inplace=True)
# Transform the continuous columns and add the higher order columns to the df
# Filtering columns to transform b/c dataframes coming from hierarchical hypergrid points
# may not contain all possible dimensions knowable from hypergrid
x_to_transform = np.zeros(
(len(df.index), len(self._adaptee_dimension_names_to_transform)))
for i, dim_name in enumerate(
self._adaptee_dimension_names_to_transform):
if dim_name in df.columns.values:
x_to_transform[:, i] = df[dim_name]
all_poly_features = self._polynomial_features.transform(x_to_transform)
for target_dim_name in self._target_polynomial_feature_map:
target_dim_index = self._target_polynomial_feature_map[
target_dim_name]
df[target_dim_name] = all_poly_features[:, target_dim_index]
return df
def _unproject_dataframe(self, df: DataFrame, in_place=True) -> DataFrame:
if not in_place:
df = df.copy(deep=True)
# unproject simply drops the monomial columns whose degree is not 1
polynomial_feature_powers = self.get_polynomial_feature_powers_table()
column_names_to_drop = []
for target_dim_name, powers_table_index in self._target_polynomial_feature_map.items(
):
target_powers = polynomial_feature_powers[powers_table_index]
if target_powers.sum() == 1:
continue
column_names_to_drop.append(target_dim_name)
df.drop(columns=column_names_to_drop, inplace=True)
return df
# 최소값에서 최대값까지 1씩 증가 데이터 생성
X_test = np.arange(X_train.min(), X_train.max(), 1)[:, np.newaxis]
#X_test = np.arange(X_train.min(), X_train.max(), 1).reshape(-1,1)
#print(X_test)
# Linear regression
model_boston = LinearRegression()
model_boston.fit(X_train, y_train)
linear_pred = model_boston.predict(X_test)
# Polynomial regression Degress 2
poly_linear_model2 = LinearRegression()
polynomial2 = PolynomialFeatures(degree=2)
#다차에 맞게 데이터 변형
X_train_transformed2 = polynomial2.fit_transform(X_train)
#print("X_train_transformed.shape :", X_train_transformed.shape)
poly_linear_model2.fit(X_train_transformed2, y_train)
# 훈련 데이터 적용
X_test_transformed2 = polynomial2.fit_transform(X_test)
pre2 = poly_linear_model2.predict(X_test_transformed2)
# Polynomial regression Degress 5
poly_linear_model5 = LinearRegression()
polynomial5 = PolynomialFeatures(degree=3)
X_train_transformed5 = polynomial5.fit_transform(X_train)
#print("X_train_transformed.shape :", X_train_transformed.shape)
poly_linear_model5.fit(X_train_transformed5, y_train)
def map_feature(x, degree=2):
poly = PolynomialFeatures(degree)
if len(x.shape) == 1:
x = x.reshape(len(x), 1)
return poly.fit_transform(x)
# TODO: Add import statements
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
# Assign the data to ppolredictor and outcome variables
# TODO: Load the data
train_data = pd.read_csv('poly.csv')
X = train_data['Var_X'].values.reshape(-1, 1)
y = train_data['Var_Y'].values
# Create polynomial features
# TODO: Create a PolynomialFeatures object, then fit and transform the
# predictor feature
poly_feat = PolynomialFeatures(degree=4)
X_poly = poly_feat.fit_transform(X)
# Make and fit the polynomial regression model
# TODO: Create a LinearRegression object and fit it to the polynomial predictor
# features
poly_model = LinearRegression(fit_intercept=False).fit(X_poly, y)
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)"""
# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 10)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
# Visualizing the Linear Regression results
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg.predict(X), color = 'blue')
plt.title('Truth or Bluff (Linear Regression)')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()
# Visualizing the polynomial Regression results
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
#Y = df.pop("Loan_Status")
#t= df.pop("Loan_ID")
a1, b1 = df.shape
###################################################### Below imp
#x_col = ['Gender','Married','Education','Self_Employed','Property_Area']
cat_feature = [
"Gender", "Married", "Dependents", "Education", "Self_Employed",
"Loan_Amount_Term", "Credit_History", "Property_Area"
]
hotencoder = OneHotEncoder(sparse=False)
onehot = hotencoder.fit_transform(df[cat_feature])
onehotpd = pd.DataFrame(onehot)
poly = PolynomialFeatures(12)
dfnum = pd.DataFrame(poly.fit_transform(df[num_feature]))
X1 = pd.concat([onehotpd, dfnum], axis=1)
X11 = X1.head(a1 - a)
X12 = X1.tail(a)
Xres, Yres = smote_learn(X11, Y)
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X11,
Y,
test_size=0.3,
random_state=42)
print("Nimai 1")
################################################################################################## XGBoost
start_time = time.time()
y_pred_test = lm.predict(X_test)
print('LINEAR REGRESSION')
print('\nDesempenho no conjunto de treinamento:')
print('MSE = %.3f' % mean_squared_error(Y_train, y_pred_train))
print('RMSE = %.3f' % math.sqrt(mean_squared_error(Y_train, y_pred_train)))
print('R2 = %.3f' % r2_score(Y_train, y_pred_train))
print('\nDesempenho no conjunto de teste:')
print('MSE = %.3f' % mean_squared_error(y_test, y_pred_test))
print('RMSE = %.3f' % math.sqrt(mean_squared_error(y_test, y_pred_test)))
print('R2 = %.3f' % r2_score(y_test, y_pred_test))
#################################################################################
## PolynomialFeatures + LinearRegression #######################################
from sklearn.preprocessing import PolynomialFeatures
pf = PolynomialFeatures(2)
X_train_poly = pf.fit_transform(X_train)
X_test_poly = pf.fit_transform(X_test)
lm = LinearRegression()
lm.fit(X_train_poly, Y_train)
y_pred_train = lm.predict(X_train_poly)
y_pred_test = lm.predict(X_test_poly)
print('POLINOMIAL LINEAR REGRESSION')
print('\nDesempenho no conjunto de treinamento:')
print('MSE = %.3f' % mean_squared_error(Y_train, y_pred_train))
print('RMSE = %.3f' % math.sqrt(mean_squared_error(Y_train, y_pred_train)))
print('R2 = %.3f' % r2_score(Y_train, y_pred_train))
print('\nDesempenho no conjunto de teste:')
print('MSE = %.3f' % mean_squared_error(y_test, y_pred_test))
print('RMSE = %.3f' % math.sqrt(mean_squared_error(y_test, y_pred_test)))
print('R2 = %.3f' % r2_score(y_test, y_pred_test))
#################################################################################
cat_impute = SimpleImputer(strategy='constant')
X_train[:, i] = cat_impute.fit_transform(X_train[:, i].reshape(-1, 1)).reshape(-1)
onehot = OneHotEncoder()
onehot_model = onehot.fit(X_train[:, i].reshape(-1, 1))
onehot_cats.append(onehot_model.categories_)
tmp_l = list()
for l in onehot_cats:
tmp_l.append(l[0].tolist())
X_train = preprocessing(X_train, categories=tmp_l)
X_test = preprocessing(X_test, categories=tmp_l)
X_pred = preprocessing(data_set["pred_X"].values, categories=tmp_l)
poly = PolynomialFeatures(degree=2)
X_train = poly.fit_transform(X_train)
X_test = poly.fit_transform(X_test)
X_pred = poly.fit_transform(X_pred)
linear = LinearRegression(fit_intercept=False, normalize=False)
# ridge = RidgeCV()
lasso = LassoCV(n_alphas=1000)
model = lasso
model = model.fit(X_train, y_train)
train = model.predict(X_train)
test = model.predict(X_test)
error1 = mean_squared_error(y_train, train)
print "R2 score =", round(sm.r2_score(y_test, y_test_pred), 2)
print "\nRIDGE:"
print "Mean absolute error =", round(
sm.mean_absolute_error(y_test, y_test_pred_ridge), 2)
print "Mean squared error =", round(
sm.mean_squared_error(y_test, y_test_pred_ridge), 2)
print "Median absolute error =", round(
sm.median_absolute_error(y_test, y_test_pred_ridge), 2)
print "Explained variance score =", round(
sm.explained_variance_score(y_test, y_test_pred_ridge), 2)
print "R2 score =", round(sm.r2_score(y_test, y_test_pred_ridge), 2)
# Polynomial regression
from sklearn.preprocessing import PolynomialFeatures
polynomial = PolynomialFeatures(degree=10)
X_train_transformed = polynomial.fit_transform(X_train)
datapoint = [0.39, 2.78, 7.11]
poly_datapoint = polynomial.fit_transform(datapoint)
poly_linear_model = linear_model.LinearRegression()
poly_linear_model.fit(X_train_transformed, y_train)
print "\nLinear regression:\n", linear_regressor.predict(datapoint)
print "\nPolynomial regression:\n", poly_linear_model.predict(poly_datapoint)
# Stochastic Gradient Descent regressor
sgd_regressor = linear_model.SGDRegressor(loss='huber', n_iter=50)
sgd_regressor.fit(X_train, y_train)
print "\nSGD regressor:\n", sgd_regressor.predict(datapoint)
import pickle
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
dataset = pd.read_csv('losatank50mg.csv')
X = pd.DataFrame({})
y = pd.DataFrame({})
tempX_set = {'x': []}
tempY_set = {'y': []}
count = 0
for index, row in dataset.iterrows():
for i in range(7):
tempX_set['x'].append(count)
tempY_set['y'].append(row[i + 1])
count = count + 1
X = pd.DataFrame(tempX_set)
Y = pd.DataFrame(tempY_set)
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)
poly.fit(X_poly, y)
regressor = LinearRegression()
regressor.fit(X_poly, Y)
pickle.dump(regressor, open('losatank_model.pkl', 'wb'))
#print(x_train.shape)
#print(y_train.shape)
#print(x_test.shape)
#print(y_test.shape)
# In[4]:
train_err = []
test_err = []
# In[5]:
#Due to memory limitation I performed it in manual fashion. Recoreded and polulatated errors in the array.
gc.collect()
poly = PolynomialFeatures(degree=10)
x_train = poly.fit_transform(x_train)
y_train = poly.fit_transform(y_train)
x_test = poly.fit_transform(x_test)
y_test = poly.fit_transform(y_test)
alpha_vals = np.linspace(1, 20, 20)
for alpha_v in alpha_vals:
regr = Lasso(alpha=alpha_v, normalize=True, max_iter=10e5)
regr.fit(x_train, y_train)
train_err.append(
math.sqrt(mean_squared_error(y_train, regr.predict(x_train))))
test_err.append(math.sqrt(mean_squared_error(y_test,
regr.predict(x_test))))
# In[6]:
X = dataset[['Level']].values
y = dataset['Salary'].values
plt.scatter(X, y)
plt.show()
# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
y_pred_lin = lin_reg.predict(X)
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=2)
poly_X = poly_reg.fit_transform(
X) #Fitting the value of X in polynomial fashion
poly_reg.fit(poly_X, y)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(poly_X, y)
y_pred_poly = lin_reg_2.predict(poly_X)
# Visualising the Linear Regression results
plt.scatter(X, y)
plt.plot(X, y_pred_lin)
plt.title('Linear Regression')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
# Visualising the Polynomial Regression results
plt.scatter(X, y)
from sklearn.preprocessing import Imputer imp = Imputer(missing_values='NaN', strategy='mean', axis=0) imp.fit([[1, 2], [np.nan, 3], [7, 6]]) X = [[np.nan, 2], [6, np.nan], [7, 6]] print(imp.transform(X)) import scipy.sparse as sp X = sp.csc_matrix([[1, 2], [0, 3], [7, 6]]) imp = Imputer(missing_values=0, strategy='mean', axis=0) imp.fit(X) X_test = sp.csc_matrix([[0, 2], [6, 0], [7, 6]]) print(imp.transform(X_test)) ''' 很多情况下，考虑输入数据中的非线性特征来增加模型的复杂性是非常有效的。一个简单常用的方法就是使用多项式特征，它能捕捉到特征中高阶和相互作用的项。 ''' from sklearn.preprocessing import PolynomialFeatures x = np.arange(6).reshape(3, 2) poly = PolynomialFeatures(2) print(poly.fit_transform(x)) # 特征向量X从:math:(X_1, X_2) 被转换成:math:(1, X_1, X_2, X_1^2, X_1X_2, X_2^2)。 # 在一些情况中,我们只需要特征中的相互作用项 interaction_only=True x = np.arange(9).reshape(3, 3) poly = PolynomialFeatures(degree=3, interaction_only=True) print(poly.fit_transform(x)) # 装换器定制 from sklearn.preprocessing import FunctionTransformer transformer = FunctionTransformer(np.log1p) x = np.array([[0, 1], [2, 3]]) transformer.transform(x)
train_data = pd.DataFrame(lines) #train
train_data = train_data.apply(toFloat,
axis=1) #convert data from string to float
X_pred = pd.DataFrame(lines2).apply(
toFloat, axis=1) #predict the price (y) for each row
####################Preprocessing##############333
#polynomial features#####
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
X_train = train_data.iloc[:, :-1]
Y_train = train_data.iloc[:, -1]
poly = PolynomialFeatures(degree=3)
poly_features = poly.fit_transform(X_train) #polynomial features for training
X_pred_poly = poly.fit_transform(X_pred)
#####model######
model = LinearRegression()
model.fit(poly_features, Y_train) #training the model
#make predictions#####
#print(X_pred)
predictions = model.predict(X_pred_poly)
for i in predictions:
print(i)
# matrix of features (independent variables)
X = dataset.iloc[:, 1:2].values
# dependant variable vector
y = dataset.iloc[:, -1].values
# Fitting Linear regression
from sklearn.linear_model import LinearRegression
linear_regressor = LinearRegression()
linear_regressor.fit(X, y)
# Fitting Polynomial Regression
from sklearn.preprocessing import PolynomialFeatures
poly_regressor = PolynomialFeatures(degree=4)
X_poly = poly_regressor.fit_transform(X)
lin_poly_reg = LinearRegression()
lin_poly_reg.fit(X_poly, y)
# Visualize the results
# 1.
plt.scatter(X, y, color='red')
plt.plot(X, linear_regressor.predict(X), color='green')
plt.title('Linear Regression plot')
plt.xlabel('Position level')
plt.ylabel('Salary')
# 2.
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))