def _compress_data(data, dictionary, *, initial_phrase, compression_end):
result = list()
code_length = len(util.to_binary(len(dictionary.keys())))
phrase = initial_phrase
for ch in data:
phrase += ch
if not (phrase in dictionary.keys()):
result.append(
util.extend_to_length(dictionary[phrase[:-1]],
code_length,
ending_bit=_zero_bit))
# print('stream', phrase[:-1], ':', utilities.extend_to_length(dictionary[phrase[:-1]], code_length))
# print('phrase:', phrase)
if _is_power_of_two(len(dictionary.keys())):
code_length += 1
dictionary_length_binary = util.to_binary(len(dictionary.keys()))
dictionary[phrase] = dictionary_length_binary
# print(phrase, '->', dictionary_length_binary)
phrase = phrase[-1]
if compression_end:
result.append(
util.extend_to_length(dictionary[phrase],
code_length,
ending_bit=_zero_bit))
return _empty_str.join(result), phrase
def translate_a(self, parser):
'''
Generate binary codes for A instructions.
'''
assert(parser.commandType() == parser.A_COMMAND)
smb = parser.symbol()
if not smb.isdigit():
if not self.symbol_table.contains(smb):
self.symbol_table.addEntry(smb, self.symbol_address)
self.symbol_address += 1
smb = self.symbol_table.getAddress(smb)
return "0" + to_binary(smb).zfill(15)
def _omega_code(number):
_ensure_correct_number(number)
result = list()
result.append(_zero_bit)
current_value = number
while not (current_value == 1):
value_bits = util.to_binary(current_value)
result.append(value_bits)
current_value = len(value_bits) - 1
result.reverse()
return _empty_str.join(result)
def main(df,
random_state,
test_size,
first_star,
second_star,
state="Given Data"):
"""
Runs the complete cycle and builds a model. Takes a dataframe,
tokenizes, clean, vectorize and return model
:param df: Pandas dataframe
:param random_state: int
:param test_size: int, split size for test
:param first_star: int, first star rating
:param second_star: int, second star rating
:param state: string, state initials
:return: logistic regression model object
"""
if state:
df = df[df["state"] == state]
df = df[df.stars_rev.isin([first_star, second_star])]
df = df[["text", "stars_rev"]].astype(str)
df = util.to_binary(df, str(first_star), str(second_star))
corpus = df["text"].values #list of reviews
y = df["stars_rev"].values #target
cleaned = util.clean_stem(corpus)
X_train, X_test, y_train, \
y_test = train_test_split(cleaned, y, test_size=test_size,
random_state=random_state)
model = ReviewClassifier() #instantiating model
model.fit(X_train, y_train)
#Estimating metrics
matrix, recall, precision, accuracy = model.metrics_eval(X_test, y_test)
print("Number of reviews: {}".format(len(corpus)), "\n")
print("Confusion matrix")
print(matrix, "\n")
print("Recall: {}%".format(round(recall * 100, 2)))
print("Precision: {}%".format(round(precision * 100, 2)))
print("Accuracy: {}%".format(round(accuracy * 100, 2)))
return model
def _generate_dictionary():
result = {chr(i): util.to_binary(i) for i in range(1, _dictionary_length)}
result[-1] = '0'
return result
def _decompress_data(bits, dictionary, reversed_dictionary, *, initial_phrase):
result = list()
code_length = len(util.to_binary(len(dictionary.keys())))
# print('dictionary length', len(dictionary.keys()))
# print()
i = 0
if initial_phrase == _empty_str:
chunk = bits[:code_length]
decompressed_chunk = reversed_dictionary[_remove_leading_zeros(chunk)]
result.append(decompressed_chunk)
# print('chunk', chunk)
# print('decompressed chunk', decompressed_chunk)
# print()
phrase = decompressed_chunk
i = code_length
if _is_power_of_two(len(dictionary.keys())):
code_length += 1
else:
phrase = initial_phrase
# print('dictionary length', len(dictionary.keys()))
# print()
while i + code_length <= len(bits):
chunk = bits[i:i + code_length]
if _one_bit in chunk:
if _remove_leading_zeros(chunk) in reversed_dictionary.keys():
decompressed_chunk = reversed_dictionary[_remove_leading_zeros(
chunk)]
else:
decompressed_chunk = phrase + phrase[0] # special case
# print('chunk', chunk)
# print('decompressed chunk', decompressed_chunk)
dict_element = phrase + decompressed_chunk[0]
if not (dict_element in dictionary.keys()):
result.append(decompressed_chunk)
# print('{} -> {}'.format(dict_element, util.to_binary(len(dictionary.keys()))))
# print()
dictionary[dict_element] = util.to_binary(
len(dictionary.keys()))
reversed_dictionary[util.to_binary(
len(reversed_dictionary.keys()))] = dict_element
phrase = decompressed_chunk
else:
phrase += decompressed_chunk
i += code_length
if _is_power_of_two(len(dictionary.keys())):
code_length += 1
else:
break
joined_result = _empty_str.join(result)
rest_bits = bits[i:]
return joined_result, rest_bits, phrase
'Supportive Environment Rating', 'Effective School Leadership Rating',
'Strong Family-Community Ties Rating', 'Trust Rating',
'Student Achievement Rating'
]
# Convert Yes/No to to 1/0
binary_columns = ['Community School?']
for col in percent_columns:
se_2016_renamed[col] = util.pct_to_number(se_2016_renamed, col)
for col in money_columns:
se_2016_renamed[col] = util.money_to_number(se_2016_renamed, col)
for col in rating_columns:
se_2016_renamed[col] = util.rating_to_number(se_2016_renamed, col)
for col in binary_columns:
se_2016_renamed[col] = util.to_binary(se_2016_renamed, col)
se_2016_renamed.columns = [
util.sanitize_column_names(c) for c in se_2016_renamed.columns
]
se_2016_renamed.head()
# In[21]:
plt.hist(
se_2016_renamed.loc[se_2016_renamed['average_ela_proficiency'].notnull(),
'average_ela_proficiency'],
bins=20)
plt.show()
plt.hist(
se_2016_renamed.loc[se_2016_renamed['average_math_proficiency'].notnull(),
def _delta_code(number):
_ensure_correct_number(number)
bits = util.to_binary(number)
return _gamma_code(len(bits)) + bits[1:]
def _gamma_code(number):
_ensure_correct_number(number)
bits = util.to_binary(number)
return _zero_bit * (len(bits) - 1) + bits
def jump(self, mnemonic):
'''
Translate jump code into binary.
'''
assert (mnemonic in self._jump_list)
return to_binary(self._jump_list.index(mnemonic)).zfill(3)
def dest(self, mnemonic):
'''
Translate dest code into binary.
'''
assert (mnemonic in self._dest_list)
return to_binary(self._dest_list.index(mnemonic)).zfill(3)
def jump(self, mnemonic):
'''
Translate jump code into binary.
'''
assert(mnemonic in self._jump_list)
return to_binary(self._jump_list.index(mnemonic)).zfill(3)
def dest(self, mnemonic):
'''
Translate dest code into binary.
'''
assert(mnemonic in self._dest_list)
return to_binary(self._dest_list.index(mnemonic)).zfill(3)
