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2020-7-7-git-master/venv/Scripts/easy_install-script.py

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+#!D:\softwaredate\workspace\venv\Scripts\python.exe
+# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==39.1.0','console_scripts','easy_install'
+__requires__ = 'setuptools==39.1.0'
+import re
+import sys
+from pkg_resources import load_entry_point
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(
+        load_entry_point('setuptools==39.1.0', 'console_scripts', 'easy_install')()
+    )

2020-7-7-git-master/venv/Scripts/pip-script.py

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+#!D:\softwaredate\workspace\venv\Scripts\python.exe
+# EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip'
+__requires__ = 'pip==10.0.1'
+import re
+import sys
+from pkg_resources import load_entry_point
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(
+        load_entry_point('pip==10.0.1', 'console_scripts', 'pip')()
+    )

2020-7-7-git-master/venv/Scripts/pip3-script.py

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+#!D:\softwaredate\workspace\venv\Scripts\python.exe
+# EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3'
+__requires__ = 'pip==10.0.1'
+import re
+import sys
+from pkg_resources import load_entry_point
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(
+        load_entry_point('pip==10.0.1', 'console_scripts', 'pip3')()
+    )

2020-zju-summer-intern-master/libsvm-3.24/python/svm.py

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+#!/usr/bin/env python
+
+from ctypes import *
+from ctypes.util import find_library
+from os import path
+import sys
+
+try:
+	import scipy
+	from scipy import sparse
+except:
+	scipy = None
+	sparse = None
+
+if sys.version_info[0] < 3:
+	range = xrange
+	from itertools import izip as zip
+
+__all__ = ['libsvm', 'svm_problem', 'svm_parameter',
+           'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC',
+           'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS',
+           'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF',
+           'SIGMOID', 'c_double', 'svm_model']
+
+try:
+	dirname = path.dirname(path.abspath(__file__))
+	if sys.platform == 'win32':
+		libsvm = CDLL(path.join(dirname, r'..\windows\libsvm.dll'))
+	else:
+		libsvm = CDLL(path.join(dirname, '../libsvm.so.2'))
+except:
+# For unix the prefix 'lib' is not considered.
+	if find_library('svm'):
+		libsvm = CDLL(find_library('svm'))
+	elif find_library('libsvm'):
+		libsvm = CDLL(find_library('libsvm'))
+	else:
+		raise Exception('LIBSVM library not found.')
+
+C_SVC = 0
+NU_SVC = 1
+ONE_CLASS = 2
+EPSILON_SVR = 3
+NU_SVR = 4
+
+LINEAR = 0
+POLY = 1
+RBF = 2
+SIGMOID = 3
+PRECOMPUTED = 4
+
+PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
+def print_null(s):
+	return
+
+def genFields(names, types):
+	return list(zip(names, types))
+
+def fillprototype(f, restype, argtypes):
+	f.restype = restype
+	f.argtypes = argtypes
+
+class svm_node(Structure):
+	_names = ["index", "value"]
+	_types = [c_int, c_double]
+	_fields_ = genFields(_names, _types)
+
+	def __init__(self, index=-1, value=0):
+		self.index, self.value = index, value
+
+	def __str__(self):
+		return '%d:%g' % (self.index, self.value)
+
+def gen_svm_nodearray(xi, feature_max=None, isKernel=False):
+	if feature_max:
+		assert(isinstance(feature_max, int))
+
+	xi_shift = 0 # ensure correct indices of xi
+	if scipy and isinstance(xi, tuple) and len(xi) == 2\
+			and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector
+		if not isKernel:
+			index_range = xi[0] + 1 # index starts from 1
+		else:
+			index_range = xi[0] # index starts from 0 for precomputed kernel
+		if feature_max:
+			index_range = index_range[scipy.where(index_range <= feature_max)]
+	elif scipy and isinstance(xi, scipy.ndarray):
+		if not isKernel:
+			xi_shift = 1
+			index_range = xi.nonzero()[0] + 1 # index starts from 1
+		else:
+			index_range = scipy.arange(0, len(xi)) # index starts from 0 for precomputed kernel
+		if feature_max:
+			index_range = index_range[scipy.where(index_range <= feature_max)]
+	elif isinstance(xi, (dict, list, tuple)):
+		if isinstance(xi, dict):
+			index_range = xi.keys()
+		elif isinstance(xi, (list, tuple)):
+			if not isKernel:
+				xi_shift = 1
+				index_range = range(1, len(xi) + 1) # index starts from 1
+			else:
+				index_range = range(0, len(xi)) # index starts from 0 for precomputed kernel
+
+		if feature_max:
+			index_range = filter(lambda j: j <= feature_max, index_range)
+		if not isKernel:
+			index_range = filter(lambda j:xi[j-xi_shift] != 0, index_range)
+
+		index_range = sorted(index_range)
+	else:
+		raise TypeError('xi should be a dictionary, list, tuple, 1-d numpy array, or tuple of (index, data)')
+
+	ret = (svm_node*(len(index_range)+1))()
+	ret[-1].index = -1
+
+	if scipy and isinstance(xi, tuple) and len(xi) == 2\
+			and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector
+		for idx, j in enumerate(index_range):
+			ret[idx].index = j
+			ret[idx].value = (xi[1])[idx]
+	else:
+		for idx, j in enumerate(index_range):
+			ret[idx].index = j
+			ret[idx].value = xi[j - xi_shift]
+
+	max_idx = 0
+	if len(index_range) > 0:
+		max_idx = index_range[-1]
+	return ret, max_idx
+
+try:
+	from numba import jit
+	jit_enabled = True
+except:
+	jit = lambda x: x
+	jit_enabled = False
+
+@jit
+def csr_to_problem_jit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start):
+	for i in range(l):
+		b1,e1 = x_rowptr[i], x_rowptr[i+1]
+		b2,e2 = prob_rowptr[i], prob_rowptr[i+1]-1
+		for j in range(b1,e1):
+			prob_ind[j-b1+b2] = x_ind[j]+indx_start
+			prob_val[j-b1+b2] = x_val[j]
+def csr_to_problem_nojit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start):
+	for i in range(l):
+		x_slice = slice(x_rowptr[i], x_rowptr[i+1])
+		prob_slice = slice(prob_rowptr[i], prob_rowptr[i+1]-1)
+		prob_ind[prob_slice] = x_ind[x_slice]+indx_start
+		prob_val[prob_slice] = x_val[x_slice]
+
+def csr_to_problem(x, prob, isKernel):
+	if not x.has_sorted_indices:
+		x.sort_indices()
+
+	# Extra space for termination node and (possibly) bias term
+	x_space = prob.x_space = scipy.empty((x.nnz+x.shape[0]), dtype=svm_node)
+	prob.rowptr = x.indptr.copy()
+	prob.rowptr[1:] += scipy.arange(1,x.shape[0]+1)
+	prob_ind = x_space["index"]
+	prob_val = x_space["value"]
+	prob_ind[:] = -1
+	if not isKernel:
+		indx_start = 1 # index starts from 1
+	else:
+		indx_start = 0 # index starts from 0 for precomputed kernel
+	if jit_enabled:
+		csr_to_problem_jit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start)
+	else:
+		csr_to_problem_nojit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start)
+
+class svm_problem(Structure):
+	_names = ["l", "y", "x"]
+	_types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node))]
+	_fields_ = genFields(_names, _types)
+
+	def __init__(self, y, x, isKernel=False):
+		if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.ndarray))):
+			raise TypeError("type of y: {0} is not supported!".format(type(y)))
+
+		if isinstance(x, (list, tuple)):
+			if len(y) != len(x):
+				raise ValueError("len(y) != len(x)")
+		elif scipy != None and isinstance(x, (scipy.ndarray, sparse.spmatrix)):
+			if len(y) != x.shape[0]:
+				raise ValueError("len(y) != len(x)")
+			if isinstance(x, scipy.ndarray):
+				x = scipy.ascontiguousarray(x) # enforce row-major
+			if isinstance(x, sparse.spmatrix):
+				x = x.tocsr()
+				pass
+		else:
+			raise TypeError("type of x: {0} is not supported!".format(type(x)))
+		self.l = l = len(y)
+
+		max_idx = 0
+		x_space = self.x_space = []
+		if scipy != None and isinstance(x, sparse.csr_matrix):
+			csr_to_problem(x, self, isKernel)
+			max_idx = x.shape[1]
+		else:
+			for i, xi in enumerate(x):
+				tmp_xi, tmp_idx = gen_svm_nodearray(xi,isKernel=isKernel)
+				x_space += [tmp_xi]
+				max_idx = max(max_idx, tmp_idx)
+		self.n = max_idx
+
+		self.y = (c_double * l)()
+		if scipy != None and isinstance(y, scipy.ndarray):
+			scipy.ctypeslib.as_array(self.y, (self.l,))[:] = y
+		else:
+			for i, yi in enumerate(y): self.y[i] = yi
+
+		self.x = (POINTER(svm_node) * l)()
+		if scipy != None and isinstance(x, sparse.csr_matrix):
+			base = addressof(self.x_space.ctypes.data_as(POINTER(svm_node))[0])
+			x_ptr = cast(self.x, POINTER(c_uint64))
+			x_ptr = scipy.ctypeslib.as_array(x_ptr,(self.l,))
+			x_ptr[:] = self.rowptr[:-1]*sizeof(svm_node)+base
+		else:
+			for i, xi in enumerate(self.x_space): self.x[i] = xi
+
+class svm_parameter(Structure):
+	_names = ["svm_type", "kernel_type", "degree", "gamma", "coef0",
+			"cache_size", "eps", "C", "nr_weight", "weight_label", "weight",
+			"nu", "p", "shrinking", "probability"]
+	_types = [c_int, c_int, c_int, c_double, c_double,
+			c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double),
+			c_double, c_double, c_int, c_int]
+	_fields_ = genFields(_names, _types)
+
+	def __init__(self, options = None):
+		if options == None:
+			options = ''
+		self.parse_options(options)
+
+	def __str__(self):
+		s = ''
+		attrs = svm_parameter._names + list(self.__dict__.keys())
+		values = map(lambda attr: getattr(self, attr), attrs)
+		for attr, val in zip(attrs, values):
+			s += (' %s: %s\n' % (attr, val))
+		s = s.strip()
+
+		return s
+
+	def set_to_default_values(self):
+		self.svm_type = C_SVC;
+		self.kernel_type = RBF
+		self.degree = 3
+		self.gamma = 0
+		self.coef0 = 0
+		self.nu = 0.5
+		self.cache_size = 100
+		self.C = 1
+		self.eps = 0.001
+		self.p = 0.1
+		self.shrinking = 1
+		self.probability = 0
+		self.nr_weight = 0
+		self.weight_label = None
+		self.weight = None
+		self.cross_validation = False
+		self.nr_fold = 0
+		self.print_func = cast(None, PRINT_STRING_FUN)
+
+	def parse_options(self, options):
+		if isinstance(options, list):
+			argv = options
+		elif isinstance(options, str):
+			argv = options.split()
+		else:
+			raise TypeError("arg 1 should be a list or a str.")
+		self.set_to_default_values()
+		self.print_func = cast(None, PRINT_STRING_FUN)
+		weight_label = []
+		weight = []
+
+		i = 0
+		while i < len(argv):
+			if argv[i] == "-s":
+				i = i + 1
+				self.svm_type = int(argv[i])
+			elif argv[i] == "-t":
+				i = i + 1
+				self.kernel_type = int(argv[i])
+			elif argv[i] == "-d":
+				i = i + 1
+				self.degree = int(argv[i])
+			elif argv[i] == "-g":
+				i = i + 1
+				self.gamma = float(argv[i])
+			elif argv[i] == "-r":
+				i = i + 1
+				self.coef0 = float(argv[i])
+			elif argv[i] == "-n":
+				i = i + 1
+				self.nu = float(argv[i])
+			elif argv[i] == "-m":
+				i = i + 1
+				self.cache_size = float(argv[i])
+			elif argv[i] == "-c":
+				i = i + 1
+				self.C = float(argv[i])
+			elif argv[i] == "-e":
+				i = i + 1
+				self.eps = float(argv[i])
+			elif argv[i] == "-p":
+				i = i + 1
+				self.p = float(argv[i])
+			elif argv[i] == "-h":
+				i = i + 1
+				self.shrinking = int(argv[i])
+			elif argv[i] == "-b":
+				i = i + 1
+				self.probability = int(argv[i])
+			elif argv[i] == "-q":
+				self.print_func = PRINT_STRING_FUN(print_null)
+			elif argv[i] == "-v":
+				i = i + 1
+				self.cross_validation = 1
+				self.nr_fold = int(argv[i])
+				if self.nr_fold < 2:
+					raise ValueError("n-fold cross validation: n must >= 2")
+			elif argv[i].startswith("-w"):
+				i = i + 1
+				self.nr_weight += 1
+				weight_label += [int(argv[i-1][2:])]
+				weight += [float(argv[i])]
+			else:
+				raise ValueError("Wrong options")
+			i += 1
+
+		libsvm.svm_set_print_string_function(self.print_func)
+		self.weight_label = (c_int*self.nr_weight)()
+		self.weight = (c_double*self.nr_weight)()
+		for i in range(self.nr_weight):
+			self.weight[i] = weight[i]
+			self.weight_label[i] = weight_label[i]
+
+class svm_model(Structure):
+	_names = ['param', 'nr_class', 'l', 'SV', 'sv_coef', 'rho',
+			'probA', 'probB', 'sv_indices', 'label', 'nSV', 'free_sv']
+	_types = [svm_parameter, c_int, c_int, POINTER(POINTER(svm_node)),
+			POINTER(POINTER(c_double)), POINTER(c_double),
+			POINTER(c_double), POINTER(c_double), POINTER(c_int),
+			POINTER(c_int), POINTER(c_int), c_int]
+	_fields_ = genFields(_names, _types)
+
+	def __init__(self):
+		self.__createfrom__ = 'python'
+
+	def __del__(self):
+		# free memory created by C to avoid memory leak
+		if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C':
+			libsvm.svm_free_and_destroy_model(pointer(pointer(self)))
+
+	def get_svm_type(self):
+		return libsvm.svm_get_svm_type(self)
+
+	def get_nr_class(self):
+		return libsvm.svm_get_nr_class(self)
+
+	def get_svr_probability(self):
+		return libsvm.svm_get_svr_probability(self)
+
+	def get_labels(self):
+		nr_class = self.get_nr_class()
+		labels = (c_int * nr_class)()
+		libsvm.svm_get_labels(self, labels)
+		return labels[:nr_class]
+
+	def get_sv_indices(self):
+		total_sv = self.get_nr_sv()
+		sv_indices = (c_int * total_sv)()
+		libsvm.svm_get_sv_indices(self, sv_indices)
+		return sv_indices[:total_sv]
+
+	def get_nr_sv(self):
+		return libsvm.svm_get_nr_sv(self)
+
+	def is_probability_model(self):
+		return (libsvm.svm_check_probability_model(self) == 1)
+
+	def get_sv_coef(self):
+		return [tuple(self.sv_coef[j][i] for j in range(self.nr_class - 1))
+				for i in range(self.l)]
+
+	def get_SV(self):
+		result = []
+		for sparse_sv in self.SV[:self.l]:
+			row = dict()
+
+			i = 0
+			while True:
+				if sparse_sv[i].index == -1:
+					break
+				row[sparse_sv[i].index] = sparse_sv[i].value
+				i += 1
+
+			result.append(row)
+		return result
+
+def toPyModel(model_ptr):
+	"""
+	toPyModel(model_ptr) -> svm_model
+
+	Convert a ctypes POINTER(svm_model) to a Python svm_model
+	"""
+	if bool(model_ptr) == False:
+		raise ValueError("Null pointer")
+	m = model_ptr.contents
+	m.__createfrom__ = 'C'
+	return m
+
+fillprototype(libsvm.svm_train, POINTER(svm_model), [POINTER(svm_problem), POINTER(svm_parameter)])
+fillprototype(libsvm.svm_cross_validation, None, [POINTER(svm_problem), POINTER(svm_parameter), c_int, POINTER(c_double)])
+
+fillprototype(libsvm.svm_save_model, c_int, [c_char_p, POINTER(svm_model)])
+fillprototype(libsvm.svm_load_model, POINTER(svm_model), [c_char_p])
+
+fillprototype(libsvm.svm_get_svm_type, c_int, [POINTER(svm_model)])
+fillprototype(libsvm.svm_get_nr_class, c_int, [POINTER(svm_model)])
+fillprototype(libsvm.svm_get_labels, None, [POINTER(svm_model), POINTER(c_int)])
+fillprototype(libsvm.svm_get_sv_indices, None, [POINTER(svm_model), POINTER(c_int)])
+fillprototype(libsvm.svm_get_nr_sv, c_int, [POINTER(svm_model)])
+fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)])
+
+fillprototype(libsvm.svm_predict_values, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
+fillprototype(libsvm.svm_predict, c_double, [POINTER(svm_model), POINTER(svm_node)])
+fillprototype(libsvm.svm_predict_probability, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
+
+fillprototype(libsvm.svm_free_model_content, None, [POINTER(svm_model)])
+fillprototype(libsvm.svm_free_and_destroy_model, None, [POINTER(POINTER(svm_model))])
+fillprototype(libsvm.svm_destroy_param, None, [POINTER(svm_parameter)])
+
+fillprototype(libsvm.svm_check_parameter, c_char_p, [POINTER(svm_problem), POINTER(svm_parameter)])
+fillprototype(libsvm.svm_check_probability_model, c_int, [POINTER(svm_model)])
+fillprototype(libsvm.svm_set_print_string_function, None, [PRINT_STRING_FUN])

2020-zju-summer-intern-master/libsvm-3.24/python/svmutil.py

@@ -0,0 +1,259 @@
+#!/usr/bin/env python
+
+import os, sys
+sys.path = [os.path.dirname(os.path.abspath(__file__))] + sys.path
+from svm import *
+from svm import __all__ as svm_all
+from svm import scipy, sparse
+from commonutil import *
+from commonutil import __all__ as common_all
+
+if sys.version_info[0] < 3:
+	range = xrange
+	from itertools import izip as zip
+	_cstr = lambda s: s.encode("utf-8") if isinstance(s,unicode) else str(s)
+else:
+	_cstr = lambda s: bytes(s, "utf-8")        
+        
+__all__ = ['svm_load_model', 'svm_predict', 'svm_save_model', 'svm_train'] + svm_all + common_all
+
+
+def svm_load_model(model_file_name):
+	"""
+	svm_load_model(model_file_name) -> model
+
+	Load a LIBSVM model from model_file_name and return.
+	"""
+	model = libsvm.svm_load_model(_cstr(model_file_name))
+	if not model:
+		print("can't open model file %s" % model_file_name)
+		return None
+	model = toPyModel(model)
+	return model
+
+def svm_save_model(model_file_name, model):
+	"""
+	svm_save_model(model_file_name, model) -> None
+
+	Save a LIBSVM model to the file model_file_name.
+	"""
+	libsvm.svm_save_model(_cstr(model_file_name), model)
+
+def svm_train(arg1, arg2=None, arg3=None):
+	"""
+	svm_train(y, x [, options]) -> model | ACC | MSE
+
+	y: a list/tuple/ndarray of l true labels (type must be int/double).
+
+	x: 1. a list/tuple of l training instances. Feature vector of
+	      each training instance is a list/tuple or dictionary.
+
+	   2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
+
+	svm_train(prob [, options]) -> model | ACC | MSE
+	svm_train(prob, param) -> model | ACC| MSE
+
+	Train an SVM model from data (y, x) or an svm_problem prob using
+	'options' or an svm_parameter param.
+	If '-v' is specified in 'options' (i.e., cross validation)
+	either accuracy (ACC) or mean-squared error (MSE) is returned.
+	options:
+	    -s svm_type : set type of SVM (default 0)
+	        0 -- C-SVC		(multi-class classification)
+	        1 -- nu-SVC		(multi-class classification)
+	        2 -- one-class SVM
+	        3 -- epsilon-SVR	(regression)
+	        4 -- nu-SVR		(regression)
+	    -t kernel_type : set type of kernel function (default 2)
+	        0 -- linear: u'*v
+	        1 -- polynomial: (gamma*u'*v + coef0)^degree
+	        2 -- radial basis function: exp(-gamma*|u-v|^2)
+	        3 -- sigmoid: tanh(gamma*u'*v + coef0)
+	        4 -- precomputed kernel (kernel values in training_set_file)
+	    -d degree : set degree in kernel function (default 3)
+	    -g gamma : set gamma in kernel function (default 1/num_features)
+	    -r coef0 : set coef0 in kernel function (default 0)
+	    -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
+	    -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
+	    -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
+	    -m cachesize : set cache memory size in MB (default 100)
+	    -e epsilon : set tolerance of termination criterion (default 0.001)
+	    -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
+	    -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
+	    -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
+	    -v n: n-fold cross validation mode
+	    -q : quiet mode (no outputs)
+	"""
+	prob, param = None, None
+	if isinstance(arg1, (list, tuple)) or (scipy and isinstance(arg1, scipy.ndarray)):
+		assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (scipy.ndarray, sparse.spmatrix)))
+		y, x, options = arg1, arg2, arg3
+		param = svm_parameter(options)
+		prob = svm_problem(y, x, isKernel=(param.kernel_type == PRECOMPUTED))
+	elif isinstance(arg1, svm_problem):
+		prob = arg1
+		if isinstance(arg2, svm_parameter):
+			param = arg2
+		else:
+			param = svm_parameter(arg2)
+	if prob == None or param == None:
+		raise TypeError("Wrong types for the arguments")
+
+	if param.kernel_type == PRECOMPUTED:
+		for i in range(prob.l):
+			xi = prob.x[i]
+			idx, val = xi[0].index, xi[0].value
+			if idx != 0:
+				raise ValueError('Wrong input format: first column must be 0:sample_serial_number')
+			if val <= 0 or val > prob.n:
+				raise ValueError('Wrong input format: sample_serial_number out of range')
+
+	if param.gamma == 0 and prob.n > 0:
+		param.gamma = 1.0 / prob.n
+	libsvm.svm_set_print_string_function(param.print_func)
+	err_msg = libsvm.svm_check_parameter(prob, param)
+	if err_msg:
+		raise ValueError('Error: %s' % err_msg)
+
+	if param.cross_validation:
+		l, nr_fold = prob.l, param.nr_fold
+		target = (c_double * l)()
+		libsvm.svm_cross_validation(prob, param, nr_fold, target)
+		ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
+		if param.svm_type in [EPSILON_SVR, NU_SVR]:
+			print("Cross Validation Mean squared error = %g" % MSE)
+			print("Cross Validation Squared correlation coefficient = %g" % SCC)
+			return MSE
+		else:
+			print("Cross Validation Accuracy = %g%%" % ACC)
+			return ACC
+	else:
+		m = libsvm.svm_train(prob, param)
+		m = toPyModel(m)
+
+		# If prob is destroyed, data including SVs pointed by m can remain.
+		m.x_space = prob.x_space
+		return m
+
+def svm_predict(y, x, m, options=""):
+	"""
+	svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)
+
+	y: a list/tuple/ndarray of l true labels (type must be int/double).
+	   It is used for calculating the accuracy. Use [] if true labels are
+	   unavailable.
+
+	x: 1. a list/tuple of l training instances. Feature vector of
+	      each training instance is a list/tuple or dictionary.
+
+	   2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
+
+	Predict data (y, x) with the SVM model m.
+	options:
+	    -b probability_estimates: whether to predict probability estimates,
+	        0 or 1 (default 0); for one-class SVM only 0 is supported.
+	    -q : quiet mode (no outputs).
+
+	The return tuple contains
+	p_labels: a list of predicted labels
+	p_acc: a tuple including  accuracy (for classification), mean-squared
+	       error, and squared correlation coefficient (for regression).
+	p_vals: a list of decision values or probability estimates (if '-b 1'
+	        is specified). If k is the number of classes, for decision values,
+	        each element includes results of predicting k(k-1)/2 binary-class
+	        SVMs. For probabilities, each element contains k values indicating
+	        the probability that the testing instance is in each class.
+	        Note that the order of classes here is the same as 'model.label'
+	        field in the model structure.
+	"""
+
+	def info(s):
+		print(s)
+
+	if scipy and isinstance(x, scipy.ndarray):
+		x = scipy.ascontiguousarray(x) # enforce row-major
+	elif sparse and isinstance(x, sparse.spmatrix):
+		x = x.tocsr()
+	elif not isinstance(x, (list, tuple)):
+		raise TypeError("type of x: {0} is not supported!".format(type(x)))
+
+	if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.ndarray))):
+		raise TypeError("type of y: {0} is not supported!".format(type(y)))
+
+	predict_probability = 0
+	argv = options.split()
+	i = 0
+	while i < len(argv):
+		if argv[i] == '-b':
+			i += 1
+			predict_probability = int(argv[i])
+		elif argv[i] == '-q':
+			info = print_null
+		else:
+			raise ValueError("Wrong options")
+		i+=1
+
+	svm_type = m.get_svm_type()
+	is_prob_model = m.is_probability_model()
+	nr_class = m.get_nr_class()
+	pred_labels = []
+	pred_values = []
+
+	if scipy and isinstance(x, sparse.spmatrix):
+		nr_instance = x.shape[0]
+	else:
+		nr_instance = len(x)
+
+	if predict_probability:
+		if not is_prob_model:
+			raise ValueError("Model does not support probabiliy estimates")
+
+		if svm_type in [NU_SVR, EPSILON_SVR]:
+			info("Prob. model for test data: target value = predicted value + z,\n"
+			"z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g" % m.get_svr_probability());
+			nr_class = 0
+
+		prob_estimates = (c_double * nr_class)()
+		for i in range(nr_instance):
+			if scipy and isinstance(x, sparse.spmatrix):
+				indslice = slice(x.indptr[i], x.indptr[i+1])
+				xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED))
+			else:
+				xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED))
+			label = libsvm.svm_predict_probability(m, xi, prob_estimates)
+			values = prob_estimates[:nr_class]
+			pred_labels += [label]
+			pred_values += [values]
+	else:
+		if is_prob_model:
+			info("Model supports probability estimates, but disabled in predicton.")
+		if svm_type in (ONE_CLASS, EPSILON_SVR, NU_SVC):
+			nr_classifier = 1
+		else:
+			nr_classifier = nr_class*(nr_class-1)//2
+		dec_values = (c_double * nr_classifier)()
+		for i in range(nr_instance):
+			if scipy and isinstance(x, sparse.spmatrix):
+				indslice = slice(x.indptr[i], x.indptr[i+1])
+				xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED))
+			else:
+				xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED))
+			label = libsvm.svm_predict_values(m, xi, dec_values)
+			if(nr_class == 1):
+				values = [1]
+			else:
+				values = dec_values[:nr_classifier]
+			pred_labels += [label]
+			pred_values += [values]
+
+	if len(y) == 0:
+		y = [0] * nr_instance
+	ACC, MSE, SCC = evaluations(y, pred_labels)
+
+	if svm_type in [EPSILON_SVR, NU_SVR]:
+		info("Mean squared error = %g (regression)" % MSE)
+		info("Squared correlation coefficient = %g (regression)" % SCC)
+	else:
+		info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(round(nr_instance*ACC/100)), nr_instance))
+
+	return pred_labels, (ACC, MSE, SCC), pred_values

2020-zju-summer-intern-master/libsvm-3.24/tools/README

@@ -0,0 +1,210 @@
+This directory includes some useful codes:
+
+1. subset selection tools.
+2. parameter selection tools.
+3. LIBSVM format checking tools
+
+Part I: Subset selection tools
+
+Introduction
+============
+
+Training large data is time consuming. Sometimes one should work on a
+smaller subset first. The python script subset.py randomly selects a
+specified number of samples. For classification data, we provide a
+stratified selection to ensure the same class distribution in the
+subset.
+
+Usage: subset.py [options] dataset number [output1] [output2]
+
+This script selects a subset of the given data set.
+
+options:
+-s method : method of selection (default 0)
+     0 -- stratified selection (classification only)
+     1 -- random selection
+
+output1 : the subset (optional)
+output2 : the rest of data (optional)
+
+If output1 is omitted, the subset will be printed on the screen.
+
+Example
+=======
+
+> python subset.py heart_scale 100 file1 file2
+
+From heart_scale 100 samples are randomly selected and stored in
+file1. All remaining instances are stored in file2.
+
+
+Part II: Parameter Selection Tools
+
+Introduction
+============
+
+grid.py is a parameter selection tool for C-SVM classification using
+the RBF (radial basis function) kernel. It uses cross validation (CV)
+technique to estimate the accuracy of each parameter combination in
+the specified range and helps you to decide the best parameters for
+your problem.
+
+grid.py directly executes libsvm binaries (so no python binding is needed)
+for cross validation and then draw contour of CV accuracy using gnuplot.
+You must have libsvm and gnuplot installed before using it. The package
+gnuplot is available at http://www.gnuplot.info/
+
+On Mac OSX, the precompiled gnuplot file needs the library Aquarterm,
+which thus must be installed as well. In addition, this version of
+gnuplot does not support png, so you need to change "set term png
+transparent small" and use other image formats. For example, you may
+have "set term pbm small color".
+
+Usage: grid.py [grid_options] [svm_options] dataset
+
+grid_options :
+-log2c {begin,end,step | "null"} : set the range of c (default -5,15,2)
+    begin,end,step -- c_range = 2^{begin,...,begin+k*step,...,end}
+    "null"         -- do not grid with c
+-log2g {begin,end,step | "null"} : set the range of g (default 3,-15,-2)
+    begin,end,step -- g_range = 2^{begin,...,begin+k*step,...,end}
+    "null"         -- do not grid with g
+-v n : n-fold cross validation (default 5)
+-svmtrain pathname : set svm executable path and name
+-gnuplot {pathname | "null"} :
+    pathname -- set gnuplot executable path and name
+    "null"   -- do not plot
+-out {pathname | "null"} : (default dataset.out)
+    pathname -- set output file path and name
+    "null"   -- do not output file
+-png pathname : set graphic output file path and name (default dataset.png)
+-resume [pathname] : resume the grid task using an existing output file (default pathname is dataset.out)
+    Use this option only if some parameters have been checked for the SAME data.
+
+svm_options : additional options for svm-train
+
+The program conducts v-fold cross validation using parameter C (and gamma)
+= 2^begin, 2^(begin+step), ..., 2^end.
+
+You can specify where the libsvm executable and gnuplot are using the
+-svmtrain and -gnuplot parameters.
+
+For windows users, please use pgnuplot.exe. If you are using gnuplot
+3.7.1, please upgrade to version 3.7.3 or higher. The version 3.7.1
+has a bug. If you use cygwin on windows, please use gunplot-x11.
+
+If the task is terminated accidentally or you would like to change the
+range of parameters, you can apply '-resume' to save time by re-using
+previous results.  You may specify the output file of a previous run
+or use the default (i.e., dataset.out) without giving a name. Please
+note that the same condition must be used in two runs. For example,
+you cannot use '-v 10' earlier and resume the task with '-v 5'.
+
+The value of some options can be "null." For example, `-log2c -1,0,1
+-log2 "null"' means that C=2^-1,2^0,2^1 and g=LIBSVM's default gamma
+value. That is, you do not conduct parameter selection on gamma.
+
+Example
+=======
+
+> python grid.py -log2c -5,5,1 -log2g -4,0,1 -v 5 -m 300 heart_scale
+
+Users (in particular MS Windows users) may need to specify the path of
+executable files. You can either change paths in the beginning of
+grid.py or specify them in the command line. For example,
+
+> grid.py -log2c -5,5,1 -svmtrain "c:\Program Files\libsvm\windows\svm-train.exe" -gnuplot c:\tmp\gnuplot\binary\pgnuplot.exe -v 10 heart_scale
+
+Output: two files
+dataset.png: the CV accuracy contour plot generated by gnuplot
+dataset.out: the CV accuracy at each (log2(C),log2(gamma))
+
+The following example saves running time by loading the output file of a previous run.
+
+> python grid.py -log2c -7,7,1 -log2g -5,2,1 -v 5 -resume heart_scale.out heart_scale
+
+Parallel grid search
+====================
+
+You can conduct a parallel grid search by dispatching jobs to a
+cluster of computers which share the same file system. First, you add
+machine names in grid.py:
+
+ssh_workers = ["linux1", "linux5", "linux5"]
+
+and then setup your ssh so that the authentication works without
+asking a password.
+
+The same machine (e.g., linux5 here) can be listed more than once if
+it has multiple CPUs or has more RAM. If the local machine is the
+best, you can also enlarge the nr_local_worker. For example:
+
+nr_local_worker = 2
+
+Example:
+
+> python grid.py heart_scale
+[local] -1 -1 78.8889  (best c=0.5, g=0.5, rate=78.8889)
+[linux5] -1 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
+[linux5] 5 -1 77.037  (best c=0.5, g=0.0078125, rate=83.3333)
+[linux1] 5 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
+.
+.
+.
+
+If -log2c, -log2g, or -v is not specified, default values are used.
+
+If your system uses telnet instead of ssh, you list the computer names
+in telnet_workers.
+
+Calling grid in Python
+======================
+
+In addition to using grid.py as a command-line tool, you can use it as a
+Python module.
+
+>>> rate, param = find_parameters(dataset, options)
+
+You need to specify `dataset' and `options' (default ''). See the following example.
+
+> python
+
+>>> from grid import *
+>>> rate, param = find_parameters('../heart_scale', '-log2c -1,1,1 -log2g -1,1,1')
+[local] 0.0 0.0 rate=74.8148 (best c=1.0, g=1.0, rate=74.8148)
+[local] 0.0 -1.0 rate=77.037 (best c=1.0, g=0.5, rate=77.037)
+.
+.
+[local] -1.0 -1.0 rate=78.8889 (best c=0.5, g=0.5, rate=78.8889)
+.
+.
+>>> rate
+78.8889
+>>> param
+{'c': 0.5, 'g': 0.5}
+
+
+Part III: LIBSVM format checking tools
+
+Introduction
+============
+
+`svm-train' conducts only a simple check of the input data. To do a
+detailed check, we provide a python script `checkdata.py.'
+
+Usage: checkdata.py dataset
+
+Exit status (returned value): 1 if there are errors, 0 otherwise.
+
+This tool is written by Rong-En Fan at National Taiwan University.
+
+Example
+=======
+
+> cat bad_data
+1 3:1 2:4
+> python checkdata.py bad_data
+line 1: feature indices must be in an ascending order, previous/current features 3:1 2:4
+Found 1 lines with error.
+
+

2021sp-final-project-mdhor-master/docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

2021sp-final-project-mdhor-master/docs/conclusion.rst

@@ -0,0 +1,45 @@
+
+Conclusion and Main Learnings
+==============================
+
+#.  Developing pj-scraper
+
+     *  The libraries requests and BeautifulSoup4 has been used to mine unstructured data from prisjakt.no
+
+     *  Two main functions have been developed: Getting all products from a category and getting all retailers and prices for a product or list of products
+
+     *  The tools sdist and Twine has been used to publish pj-scraper to PyPi for easy installation
+
+#.  Organizing the workflow into Luigi Tasks
+
+      *  Four main tasks has been developed that encapsulates the full data science pipeline
+
+      *  The first two tasks handles the scraping of products, retailers and prices, utilizing the pj-scraper library
+
+      *  The last two tasks handles appending the scraped data to the Django database, using Django commands
+
+#.  Creating a Django database
+
+      *  A Django database has been developed which contains two tables: Products and Prices
+      *  The Prices table is a fact table containing prices for each product and retailer for a given timestamp
+      *  The Products table is a dim-table containing information about products, like product name and category
+
+#.  Developing a simple Django web app
+
+      *  A simple web app has been developed, to showcase how the system could be used in practice
+
+      *  To avoid unneseccary boilerplate and simplify visuals creation, mpld3 has been used
+
+      *  Two simple visuals has been made, that shows two interesting analyses
+
+
+
+
+Future Work
+-------------------------------------
+
+Before this project would have real value, two main things are missing:
+
+#.  Move the system to the cloud and schedule Luigi to run with certain intervals
+
+#.  Create insightful analyses based on inputs from potential users of the system

2021sp-final-project-mdhor-master/docs/conf.py

@@ -0,0 +1,80 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+
+import django
+from sphinx.ext.apidoc import main
+
+sys.path.insert(0, os.path.abspath(".."))
+
+# -- Django setup ------------------------------------------------------------
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.local")
+django.setup()
+
+
+# -- Project information -----------------------------------------------------
+
+project = "Final Project, Mattias Hornum, CSCI E-29"
+copyright = "2021, Mattias Hornum"
+author = "Mattias Hornum"
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = ["sphinx.ext.napoleon"]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_rtd_theme"
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+
+main(
+    [
+        "-e",
+        "-f",
+        "-o",
+        ".",
+        "..",
+        "../final_project/contrib*",
+        "../final_project/conftest*",
+        "../final_project/users*",
+        "../final_project/tests*",
+        "../final_project/utils*",
+        "../prisjakt/migrations*",
+        "../conftest*",
+        "../manage*",
+        "../config*",
+        "../modules*",
+    ]
+)

2021sp-final-project-mdhor-master/docs/django.rst

@@ -0,0 +1,39 @@
+
+Introduction
+==================
+
+
+
+
+Django Database
+-----------------
+
+The database is structured as a star schema. The fact table contains prices per retailer per timestamp per product.
+The unique identifier for this table is the combination of timestamp, retailer and product. In future, this schema is
+easily expandable with new dim-tables containing information on e.g. the retailers. Here is the schema:
+
+.. image:: ./images/database.png
+  :width: 800
+
+
+
+
+
+Django Web-App
+------------------
+
+The web app for now only contains two simple visuals:
+
+#. A line chart showing the historical prices of an iPhone 12, for the 10 retailers with lowest price today
+
+#. A scatter plot showing retailer rating on the x-axis and price in the y-axis
+
+The visuals have been created using the following procedure:
+
+#. Query the DB to get only the needed data for the visual
+
+#. Load data into a pandas dataframe
+
+#. Use matplotlib to create the graph
+
+#. Use mpld3 to convert the graph into html

2021sp-final-project-mdhor-master/docs/final_project.cli.rst

@@ -0,0 +1,7 @@
+final\_project.cli module
+=========================
+
+.. automodule:: final_project.cli
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/final_project.django_target.rst

@@ -0,0 +1,7 @@
+final\_project.django\_target module
+====================================
+
+.. automodule:: final_project.django_target
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/final_project.rst

@@ -0,0 +1,20 @@
+final\_project package
+======================
+
+Submodules
+----------
+
+.. toctree::
+   :maxdepth: 4
+
+   final_project.cli
+   final_project.django_target
+   final_project.tasks
+
+Module contents
+---------------
+
+.. automodule:: final_project
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/final_project.tasks.rst

@@ -0,0 +1,7 @@
+final\_project.tasks module
+===========================
+
+.. automodule:: final_project.tasks
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/index.rst

@@ -0,0 +1,54 @@
+.. Final Project, CSCI E-29 documentation master file, created by
+   sphinx-quickstart on Wed Apr 28 18:13:52 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+
+
+
+
+
+Welcome to my Final Project
+====================================================================
+
+.. start-badges
+
+.. list-table::
+    :stub-columns: 1
+    :widths: 10 40
+
+    * - Build
+      - | |travis| |codeclimate| |codeclimate2|
+    * - Docs
+      - | |readthedocs|
+
+.. |travis| image:: https://travis-ci.com/mdhor/2021sp-final-project-mdhor.svg?branch=master
+    :alt: Travis-CI Build Status
+    :target: https://travis-ci.com/github/mdhor/2021sp-final-project-mdhor
+
+.. |codeclimate| image:: https://api.codeclimate.com/v1/badges/7469f8aaac5c1798b9e4/maintainability
+   :target: https://codeclimate.com/github/csci-e-29/2021sp-final-project-mdhor
+   :alt: CodeClimate Quality Status
+
+.. |codeclimate2| image:: https://api.codeclimate.com/v1/badges/7469f8aaac5c1798b9e4/test_coverage
+   :target: https://codeclimate.com/github/csci-e-29/2021sp-final-project-mdhor
+   :alt: CodeClimate Coverage Status
+
+.. |readthedocs| image:: https://readthedocs.org/projects/2021sp-final-project-mdhor/badge/?version=latest
+   :target: https://2021sp-final-project-mdhor.readthedocs.io/en/latest/?badge=latest
+   :alt: Documentation Status
+
+.. end-badges
+
+
+
+.. toctree::
+   :maxdepth: 4
+   :caption: Contents:
+
+   intro
+   pj_scraper
+   luigi_workflow
+   django_main
+   conclusion
+   #modules

2021sp-final-project-mdhor-master/docs/intro.rst

@@ -0,0 +1,75 @@
+
+Introduction
+============
+
+Pricing and assortment data is highly valuable but can be difficult and expensive to obtain, especially for smaller retailers.
+
+Because of digitalization, the importance of pricing and assortment data has increased. An insight into a companys online presence is crucial for success.
+This is especially true after COVID-19, where smaller retailers are forced to differentiate themselves online.
+Without large IT-budgets, gaining good data surrounding ones own as well as competitors pricing and assortment can be difficult.
+
+Data on pricing and assortment can be used for several insightful analyses. One example is pricing analysis to answer questions like "Where do our prices differ from competitors?".
+Another example is assortment analysis; "How does our assortment compare to competitors?".
+
+prisjakt.no is a leading actor within price comparison in Norway. It acts as an "all-inclusive" shopping mall, so the consumer does not have to browse many different website, by comparing pricing
+from many different retailers. The products range widely from electronics, to clothing, to vehicles.
+
+
+
+Quick-Start
+-------------------------------------
+
+If you want to test the project quickly, first clone the repo and create an environment using the Pipfile.
+Then execute the following commands:
+
+   .. code-block:: bash
+
+      pipenv run python manage.py migrate
+      pipenv run python -m final_project
+      pipenv run python manage.py runserver
+
+Now you can open the server and check out a visual.
+
+
+
+
+
+Aim of Project
+-------------------------------------
+
+The project aims to create a full stack data science pipeline, from mining prisjakt.no data all the way to showing results in a web application.
+The project can be split into four main workflows:
+
+#.  Developing pj-scraper, a library that will act as a simplified interface for scraping of prisjakt.no
+
+#.  Organizing the pipeline into Luigi Tasks, all the way from handling scraping to appending data in a Django database
+
+#.  Creating a Django database that will contain the data that has been scraped from prisjakt.no
+
+#.  Developing a simple Django web app to show a simple example of how the data can be used for analysis
+
+
+
+High-Level Workflow
+----------------------------------
+
+
+.. image:: ./images/workflow.png
+  :width: 800
+
+
+
+
+
+Autogeneration of the Documentation
+-------------------------------------
+
+To autogenerate the documentation i did the following three things:
+
+#.  Link ReadTheDocs with the Github repo, so a new RTD build will be executed on every new commit
+
+#.  Add a requirements.txt to the docs/ directory, to enable RTD to create the documentation within the right environment
+
+#.  Run sphinx-apidoc from within docs/conf.py, so that apidoc will run before the html output is created (although this is a cool feature, I decided not to use it in the end, but I've left it in for others to learn)
+
+It should be noted that some of the autogenerated docs are blank because csci_utils can't be installed, because it is a private repo.

2021sp-final-project-mdhor-master/docs/luigi_cli.rst

@@ -0,0 +1,9 @@
+Autodocs: CLI
+================
+
+
+
+.. automodule:: final_project.cli
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/luigi_django_target.rst

@@ -0,0 +1,21 @@
+Django Target
+=================
+
+
+I created a new target class, called DjangoModelTarget. The purpose of the class, is to tell Luigi whether the target
+keys in a database table already exist or not. I've included the source code here:
+
+
+.. literalinclude:: ../final_project/django_target.py
+   :language: python
+   :linenos:
+
+
+
+Autodocs
+-------------------
+
+.. automodule:: final_project.django_target
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/luigi_intro.rst

@@ -0,0 +1,39 @@
+
+
+Introduction
+=================
+
+The workflow consists of four main tasks:
+
+#. Scrape products from a list of categories
+
+#. Scrape retailers and prices for all products
+
+#. Append any new products to database
+
+#. Append all prices to the database with a new timestamp
+
+The two tasks handling scraping both write to a parquet target which is salted. The salt is a function of the parameters,
+as well the current day of the year, to avoid scraping prices more than once a day. This could in future be changed to be once per hour or even per minute.
+
+Below are descriptions of each tasks, with pseudo code.
+
+**Scrape product IDs**
+
+.. image:: ./images/product_ids.png
+  :width: 800
+
+**Scrape retailers and prices**
+
+.. image:: ./images/scrape_prices.png
+  :width: 800
+
+**Load product IDs to DB**
+
+.. image:: ./images/task_load_prods.png
+  :width: 800
+
+**Load retailers and prices to DB**
+
+.. image:: ./images/task_load_prices.png
+  :width: 800

2021sp-final-project-mdhor-master/docs/luigi_tasks.rst

@@ -0,0 +1,7 @@
+Autodocs: Luigi Tasks
+===============
+
+.. automodule:: final_project.tasks
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/luigi_workflow.rst

@@ -0,0 +1,10 @@
+Luigi Workflow
+======================
+
+.. toctree::
+   :maxdepth: 4
+
+   luigi_intro
+   luigi_tasks
+   luigi_django_target
+   luigi_cli

2021sp-final-project-mdhor-master/docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

2021sp-final-project-mdhor-master/docs/modules.rst

@@ -0,0 +1,8 @@
+2021sp-final-project-mdhor
+==========================
+
+.. toctree::
+   :maxdepth: 4
+
+   final_project
+   prisjakt

2021sp-final-project-mdhor-master/docs/pj_scraper.rst

@@ -0,0 +1,25 @@
+
+
+pj-scraper
+==========
+
+The pj-scraper library is centered around the use of a single class, with methods that handle to main tasks:
+
+#. Getting all products from a category, e.g. all from the category "smartphones"
+
+#. Getting all retailers and prices for a product, e.g. all from the product "iPhone 12"
+
+See repo on Github here: https://github.com/mdhor/pj-scraper#overview
+
+
+
+
+
+
+pj_scraper package
+---------------------
+
+.. autoclass:: pj_scraper.scraper.Scraper
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/prisjakt.admin.rst

@@ -0,0 +1,7 @@
+prisjakt.admin module
+=====================
+
+.. automodule:: prisjakt.admin
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/prisjakt.apps.rst

@@ -0,0 +1,7 @@
+prisjakt.apps module
+====================
+
+.. automodule:: prisjakt.apps
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/prisjakt.models.rst

@@ -0,0 +1,7 @@
+prisjakt.models module
+======================
+
+.. automodule:: prisjakt.models
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/prisjakt.rst

@@ -0,0 +1,22 @@
+prisjakt package
+================
+
+Submodules
+----------
+
+.. toctree::
+   :maxdepth: 4
+
+   prisjakt.admin
+   prisjakt.apps
+   prisjakt.models
+   prisjakt.urls
+   prisjakt.views
+
+Module contents
+---------------
+
+.. automodule:: prisjakt
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/prisjakt.urls.rst

@@ -0,0 +1,7 @@
+prisjakt.urls module
+====================
+
+.. automodule:: prisjakt.urls
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/prisjakt.views.rst

@@ -0,0 +1,7 @@
+prisjakt.views module
+=====================
+
+.. automodule:: prisjakt.views
+   :members:
+   :undoc-members:
+   :show-inheritance:

2021sp-final-project-mdhor-master/docs/requirements.txt

@@ -0,0 +1,123 @@
+#
+# These requirements were autogenerated by pipenv
+# To regenerate from the project's Pipfile, run:
+#
+#    pipenv lock --requirements --dev
+#
+
+# Note: in pipenv 2020.x, "--dev" changed to emit both default and development
+# requirements. To emit only development requirements, pass "--dev-only".
+
+-i https://pypi.org/simple
+aiobotocore==1.3.0; python_version >= '3.6'
+aiohttp==3.7.4.post0; python_version >= '3.6'
+aioitertools==0.7.1; python_version >= '3.6'
+alabaster==0.7.12
+appdirs==1.4.4
+asgiref==3.3.4; python_version >= '3.6'
+async-timeout==3.0.1; python_full_version >= '3.5.3'
+atomicwrites==1.4.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+attrs==21.2.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
+babel==2.9.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+beautifulsoup4==4.9.3
+botocore==1.20.49; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'
+canvasapi==2.2.0
+certifi==2020.12.5
+cffi==1.14.5
+cfgv==3.2.0; python_full_version >= '3.6.1'
+chardet==4.0.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
+cloudpickle==1.6.0; python_version >= '3.5'
+coverage==5.5; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' and python_version < '4'
+cramjam==2.3.0
+cryptography==3.4.7
+cycler==0.10.0
+dask[dataframe]==2021.4.1; python_version >= '3.7'
+defusedxml==0.7.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
+distlib==0.3.1
+django-allauth==0.44.0
+django-crispy-forms==1.11.2
+django-debug-toolbar==3.2.1
+django-environ==0.4.5
+django-extensions==3.1.3
+django-model-utils==4.1.1
+django==3.2.2
+docutils==0.17.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
+environs==9.3.2; python_version >= '3.6'
+et-xmlfile==1.1.0; python_version >= '3.6'
+factory-boy==3.2.0
+faker==8.1.2; python_version >= '3.6'
+fastparquet==0.6.0.post1
+filelock==3.0.12
+fsspec==2021.4.0; python_version >= '3.6'
+gitdb==4.0.7; python_version >= '3.4'
+gitpython==3.1.14; python_version >= '3.4'
+identify==2.2.4; python_full_version >= '3.6.1'
+idna==2.10; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+imagesize==1.2.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+iniconfig==1.1.1
+jinja2==2.11.3; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
+jmespath==0.10.0; python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'
+kiwisolver==1.3.1; python_version >= '3.6'
+llvmlite==0.36.0; python_version < '3.10' and python_version >= '3.6'
+locket==0.2.1
+lockfile==0.12.2
+luigi==3.0.3
+markupsafe==1.1.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+marshmallow==3.11.1; python_version >= '3.5'
+matplotlib==3.4.2; python_version >= '3.7'
+mpld3==0.5.2
+multidict==5.1.0; python_version >= '3.6'
+nodeenv==1.6.0
+numba==0.53.1; python_version < '3.10' and python_version >= '3.6'
+numpy==1.20.2
+oauthlib==3.1.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+openpyxl==3.0.7; python_version >= '3.6'
+packaging==20.9; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+pandas==1.2.4
+partd==1.2.0; python_version >= '3.5'
+pillow==8.2.0; python_version >= '3.6'
+pj-scraper==0.0.4
+pluggy==0.13.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+pre-commit==2.12.1
+py==1.10.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+pycparser==2.20; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+pygments==2.9.0; python_version >= '3.5'
+pyjwt[crypto]==2.1.0; python_version >= '3.6'
+pyparsing==2.4.7; python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'
+pytest-cov==2.11.1
+pytest-django==4.2.0
+pytest==6.2.4
+python-daemon==2.3.0
+python-dateutil==2.8.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+python-dotenv==0.17.1
+python3-openid==3.2.0
+pytz==2021.1
+pyyaml==5.4.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'
+requests-oauthlib==1.3.0
+requests==2.25.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
+s3fs==2021.4.0; python_version >= '3.6'
+scipy==1.6.3; python_version < '3.10' and python_version >= '3.7'
+seaborn==0.11.1
+six==1.16.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+smmap==4.0.0; python_version >= '3.5'
+snowballstemmer==2.1.0
+soupsieve==2.2.1; python_version >= '3.0'
+sphinx==4.0.0
+sphinxcontrib-applehelp==1.0.2; python_version >= '3.5'
+sphinxcontrib-devhelp==1.0.2; python_version >= '3.5'
+sphinxcontrib-htmlhelp==1.0.3; python_version >= '3.5'
+sphinxcontrib-jsmath==1.0.1; python_version >= '3.5'
+sphinxcontrib-qthelp==1.0.3; python_version >= '3.5'
+sphinxcontrib-serializinghtml==1.1.4; python_version >= '3.5'
+sqlparse==0.4.1; python_version >= '3.5'
+tenacity==6.3.1
+text-unidecode==1.3
+thrift==0.13.0
+toml==0.10.2; python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'
+toolz==0.11.1; python_version >= '3.5'
+tornado==6.1; python_version >= '3.5'
+typing-extensions==3.10.0.0
+urllib3==1.26.4; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' and python_version < '4'
+virtualenv==20.4.6; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+wrapt==1.12.1
+yarl==1.6.3; python_version >= '3.6'

2021sp-final-project-mdhor-master/final_project/__init__.py

@@ -0,0 +1,7 @@
+__version__ = "0.1.0"
+__version_info__ = tuple(
+    [
+        int(num) if num.isdigit() else num
+        for num in __version__.replace("-", ".", 1).split(".")
+    ]
+)

2021sp-final-project-mdhor-master/final_project/__main__.py

@@ -0,0 +1,4 @@
+from .cli import main
+
+if __name__ == "__main__":
+    main()

2021sp-final-project-mdhor-master/final_project/cli.py

@@ -0,0 +1,55 @@
+import os
+from unittest.mock import MagicMock
+
+import django
+from csci_utils.canvas.canvas import SubmissionHelper
+from environs import Env
+from luigi import WrapperTask, build
+from luigi.util import requires
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.local")
+django.setup()
+
+
+from .tasks import LoadPricesToDatabase, LoadProductsToDatabase
+
+
+@requires(LoadProductsToDatabase, LoadPricesToDatabase)
+class FinalProjectWrapper(WrapperTask):
+    pass
+
+
+def main():
+    """ Main for running project and submitting """
+    # Fix for removing unwanted logging by requests/bs4 in Travis
+    import os
+    import sys
+
+    f = open(os.devnull, "w")
+    sys.stdout = f
+
+    build(
+        [
+            FinalProjectWrapper(
+                categories=["mobiltelefoner", "smartklokker", "hodetelefoner"]
+            )
+        ],
+        local_scheduler=True,
+        log_level="INFO",
+    )
+
+    env = Env()
+    env.read_env()
+
+    submission = SubmissionHelper.quick_start(
+        env.str("CANVAS_URL"),
+        env.str("CANVAS_TOKEN"),
+        "CSCI E-29",
+        "Final Project",
+    )
+
+    quiz_submission = MagicMock()
+    quiz_submission.id = ""
+    quiz_submission.attempt = ""
+
+    submission.submit_assignment(quiz_submission, submit=True)

2021sp-final-project-mdhor-master/final_project/conftest.py

@@ -0,0 +1,14 @@
+import pytest
+
+from final_project.users.models import User
+from final_project.users.tests.factories import UserFactory
+
+
+@pytest.fixture(autouse=True)
+def media_storage(settings, tmpdir):
+    settings.MEDIA_ROOT = tmpdir.strpath
+
+
+@pytest.fixture
+def user() -> User:
+    return UserFactory()

2021sp-final-project-mdhor-master/final_project/django_target.py

@@ -0,0 +1,39 @@
+from luigi import Target
+
+
+class DjangoModelTarget(Target):
+    """ Luigi target for Django models """
+
+    def __init__(self, model, **unique):
+        """Input args for Django target
+
+        Args:
+            model: A Django model
+            unique: The keys to target (should only be one field)
+
+        Example:
+            target = DjangoModelTarget(Model, unique_key=[1,2,3])
+            This will target the values "1, 2, and 3" in field "unique_key" in model "Model"
+        """
+        self.model = model
+        if len(unique) == 1:
+            self.unique = unique
+        else:
+            raise RuntimeError("More than one unique key")
+
+    def get(self):
+        """ Get the objects matching the keys """
+        return self.model.objects.filter(
+            **{f"{key}__in": value for key, value in self.unique.items()}
+        )
+
+    def exists(self):
+        try:
+            m = self.get()
+            return (
+                True
+                if len(m) == len([val for val in self.unique.values()][0])
+                else False
+            )
+        except:
+            return False

2021sp-final-project-mdhor-master/final_project/tasks.py

@@ -0,0 +1,133 @@
+import os
+from contextlib import contextmanager
+from datetime import datetime, timedelta
+
+import pandas as pd
+from csci_utils.luigi.target import SuffixPreservingLocalTarget
+from csci_utils.luigi.task import Requirement, Requires, TargetOutput
+from luigi import IntParameter, ListParameter, Task
+from luigi.contrib.external_program import ExternalProgramTask
+from luigi.util import inherits
+from pj_scraper.scraper import Scraper
+
+from final_project.django_target import DjangoModelTarget
+from prisjakt.models import Prices, Products
+
+
+class ScrapeProducts(Task):
+
+    categories = ListParameter(default=["mobiltelefoner", "smartklokker"])
+    no_pages = IntParameter(default=1)
+
+    now = datetime.now() + timedelta(days=1)
+    output = TargetOutput(
+        "./data/",
+        file_pattern="day"
+        + str(now.timetuple().tm_yday)
+        + "-{task.__class__.__name__}-{salt}{self.ext}",
+        target_class=SuffixPreservingLocalTarget,
+        salted=True,
+        ext=".parquet",
+    )
+
+    def run(self):
+        s = Scraper()
+        all_products = pd.DataFrame()
+
+        for category in self.categories:
+            temp_df = s.get_all_products_from_category(category, no_pages=self.no_pages)
+            all_products = all_products.append(temp_df)
+
+        with self.output().temporary_path() as fp:
+            all_products.to_parquet(fp, compression="gzip")
+
+
+@inherits(ScrapeProducts)
+class ScrapePrices(Task):
+
+    products = Requirement(ScrapeProducts)
+    requires = Requires()
+
+    now = datetime.now() + timedelta(days=1)
+    output = TargetOutput(
+        "./data/",
+        file_pattern="day"
+        + str(now.timetuple().tm_yday)
+        + "-{task.__class__.__name__}-{salt}{self.ext}",
+        target_class=SuffixPreservingLocalTarget,
+        salted=True,
+        ext=".parquet",
+    )
+
+    def run(self):
+        s = Scraper()
+        products = pd.read_parquet(
+            self.products.output().path, columns=["product_number"]
+        )
+        products = products.astype(str)
+        prices_and_retailers = s.get_sellers_and_prices_of_product_list(
+            products.product_number
+        )
+        prices_and_retailers["timestamp"] = self.now
+        with self.output().temporary_path() as fp:
+            prices_and_retailers.to_parquet(fp, compression="gzip")
+
+
+@inherits(ScrapePrices)
+class LoadPricesToDatabase(ExternalProgramTask):
+
+    prices = Requirement(ScrapePrices)
+    requires = Requires()
+
+    def program_args(self):
+        return f"python manage.py load_prices --input_path {self.prices.output().path}".split(
+            " "
+        )
+
+    def run(self):
+        super().run()
+
+    def output(self):
+        ids = pd.read_parquet(
+            self.prices.output().path,
+            columns=["product_number", "seller_id", "timestamp"],
+        )
+        ids = [
+            str(entry.product_number)
+            + str(entry.seller_id)
+            + str(entry.timestamp.timetuple().tm_yday)
+            for entry in ids.itertuples()
+        ]
+        return DjangoModelTarget(Prices, unique_identifier=list(set(ids)))
+
+
+@inherits(ScrapeProducts)
+class LoadProductsToDatabase(ExternalProgramTask):
+
+    products = Requirement(ScrapeProducts)
+    requires = Requires()
+
+    def program_args(self):
+        return f"python manage.py load_products --input_path {self.products.output().path}".split(
+            " "
+        )
+
+    def run(self):
+        super().run()
+
+    def output(self):
+        ids = pd.read_parquet(self.products.output().path, columns=["product_number"])
+        ids = ids.product_number.values
+        return DjangoModelTarget(Products, product_number=list(set(ids)))
+
+
+@contextmanager
+def change_dir(relative_dir):
+    """ Context manager for temporarily entering another dir """
+    cwd = os.getcwd()
+    try:
+        os.chdir("./" + relative_dir)
+        yield
+    finally:
+        if os.getcwd() is not cwd:
+            os.chdir(cwd)

2021sp-final-project-mdhor-master/final_project/templates/account/email_confirm.html

@@ -0,0 +1,31 @@
+{% extends "account/base.html" %}
+
+{% load i18n %}
+{% load account %}
+
+{% block head_title %}{% trans "Confirm E-mail Address" %}{% endblock %}
+
+
+{% block inner %}
+<h1>{% trans "Confirm E-mail Address" %}</h1>
+
+{% if confirmation %}
+
+{% user_display confirmation.email_address.user as user_display %}
+
+<p>{% blocktrans with confirmation.email_address.email as email %}Please confirm that <a href="mailto:{{ email }}">{{ email }}</a> is an e-mail address for user {{ user_display }}.{% endblocktrans %}</p>
+
+<form method="post" action="{% url 'account_confirm_email' confirmation.key %}">
+{% csrf_token %}
+    <button class="btn btn-primary" type="submit">{% trans 'Confirm' %}</button>
+</form>
+
+{% else %}
+
+{% url 'account_email' as email_url %}
+
+<p>{% blocktrans %}This e-mail confirmation link expired or is invalid. Please <a href="{{ email_url }}">issue a new e-mail confirmation request</a>.{% endblocktrans %}</p>
+
+{% endif %}
+
+{% endblock %}

2021sp-final-project-mdhor-master/final_project/templates/account/login.html

@@ -0,0 +1,47 @@
+{% extends "account/base.html" %}
+
+{% load i18n %}
+{% load account socialaccount %}
+{% load crispy_forms_tags %}
+
+{% block head_title %}{% trans "Sign In" %}{% endblock %}
+
+{% block inner %}
+
+<h1>{% trans "Sign In" %}</h1>
+
+{% get_providers as socialaccount_providers %}
+
+{% if socialaccount_providers %}
+<p>{% blocktrans with site.name as site_name %}Please sign in with one
+of your existing third party accounts. Or, <a href="{{ signup_url }}">sign up</a>
+for a {{ site_name }} account and sign in below:{% endblocktrans %}</p>
+
+<div class="socialaccount_ballot">
+
+  <ul class="socialaccount_providers">
+    {% include "socialaccount/snippets/provider_list.html" with process="login" %}
+  </ul>
+
+  <div class="login-or">{% trans 'or' %}</div>
+
+</div>
+
+{% include "socialaccount/snippets/login_extra.html" %}
+
+{% else %}
+<p>{% blocktrans %}If you have not created an account yet, then please
+<a href="{{ signup_url }}">sign up</a> first.{% endblocktrans %}</p>
+{% endif %}
+
+<form class="login" method="POST" action="{% url 'account_login' %}">
+  {% csrf_token %}
+  {{ form|crispy }}
+  {% if redirect_field_value %}
+  <input type="hidden" name="{{ redirect_field_name }}" value="{{ redirect_field_value }}" />
+  {% endif %}
+  <a class="button secondaryAction" href="{% url 'account_reset_password' %}">{% trans "Forgot Password?" %}</a>
+  <button class="primaryAction btn btn-primary" type="submit">{% trans "Sign In" %}</button>
+</form>
+
+{% endblock %}

2021sp-final-project-mdhor-master/final_project/templates/account/logout.html

@@ -0,0 +1,19 @@
+{% extends "account/base.html" %}
+
+{% load i18n %}
+
+{% block head_title %}{% trans "Sign Out" %}{% endblock %}
+
+{% block inner %}
+<h1>{% trans "Sign Out" %}</h1>
+
+<p>{% trans 'Are you sure you want to sign out?' %}</p>
+
+<form method="post" action="{% url 'account_logout' %}">
+  {% csrf_token %}
+  {% if redirect_field_value %}
+  <input type="hidden" name="{{ redirect_field_name }}" value="{{ redirect_field_value }}"/>
+  {% endif %}
+  <button class="btn btn-danger" type="submit">{% trans 'Sign Out' %}</button>
+</form>
+{% endblock %}