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def get_api_key():
' Get secret api key from a file on filesystem '
paren_dir = os.path.dirname(os.path.realpath(__file__))
api_path = os.path.join(paren_dir, 'weather_api.txt')
with open(api_path, 'r') as file:
api_key = file.read().replace('\n', '')
return api_key | 239,451,994,047,830,600 | Get secret api key from a file on filesystem | .config/polybar/weather/weather.py | get_api_key | NearHuscarl/dotfiles | python | def get_api_key():
' '
paren_dir = os.path.dirname(os.path.realpath(__file__))
api_path = os.path.join(paren_dir, 'weather_api.txt')
with open(api_path, 'r') as file:
api_key = file.read().replace('\n', )
return api_key |
def get_city_id():
' Workaround to get city id based on my schedule '
region_code = {'TPHCM': 1580578, 'TPHCM2': 1566083, 'Hai Duong': 1581326, 'Tan An': 1567069}
hour = int(datetime.datetime.now().strftime('%H'))
weekday = datetime.datetime.now().strftime('%a')
if (((hour >= 17) and (weekday == 'Fri')) or (weekday == 'Sat') or ((hour < 17) and (weekday == 'Sun'))):
return region_code['Tan An']
return region_code['Hai Duong'] | -7,807,401,822,778,362,000 | Workaround to get city id based on my schedule | .config/polybar/weather/weather.py | get_city_id | NearHuscarl/dotfiles | python | def get_city_id():
' '
region_code = {'TPHCM': 1580578, 'TPHCM2': 1566083, 'Hai Duong': 1581326, 'Tan An': 1567069}
hour = int(datetime.datetime.now().strftime('%H'))
weekday = datetime.datetime.now().strftime('%a')
if (((hour >= 17) and (weekday == 'Fri')) or (weekday == 'Sat') or ((hour < 17) and (weekday == 'Sun'))):
return region_code['Tan An']
return region_code['Hai Duong'] |
def update_weather(city_id, units, api_key):
' Update weather by using openweather api '
url = 'http://api.openweathermap.org/data/2.5/weather?id={}&appid={}&units={}'
temp_unit = ('C' if (units == 'metric') else 'K')
error_icon = color_polybar('\ue2c1', 'red')
try:
req = requests.get(url.format(city_id, api_key, units))
try:
description = req.json()['weather'][0]['description'].capitalize()
except ValueError:
print(error_icon, flush=True)
raise MyInternetIsShitty
temp_value = round(req.json()['main']['temp'])
temp = ((str(temp_value) + '°') + temp_unit)
thermo_icon = color_polybar(get_thermo_icon(temp_value, units), 'main')
weather_id = req.json()['weather'][0]['id']
weather_icon = color_polybar(get_weather_icon(weather_id), 'main')
print('{} {} {} {}'.format(weather_icon, description, thermo_icon, temp), flush=True)
except (HTTPError, Timeout, ConnectionError):
print(error_icon, flush=True)
raise MyInternetIsShitty | 6,975,599,857,060,252,000 | Update weather by using openweather api | .config/polybar/weather/weather.py | update_weather | NearHuscarl/dotfiles | python | def update_weather(city_id, units, api_key):
' '
url = 'http://api.openweathermap.org/data/2.5/weather?id={}&appid={}&units={}'
temp_unit = ('C' if (units == 'metric') else 'K')
error_icon = color_polybar('\ue2c1', 'red')
try:
req = requests.get(url.format(city_id, api_key, units))
try:
description = req.json()['weather'][0]['description'].capitalize()
except ValueError:
print(error_icon, flush=True)
raise MyInternetIsShitty
temp_value = round(req.json()['main']['temp'])
temp = ((str(temp_value) + '°') + temp_unit)
thermo_icon = color_polybar(get_thermo_icon(temp_value, units), 'main')
weather_id = req.json()['weather'][0]['id']
weather_icon = color_polybar(get_weather_icon(weather_id), 'main')
print('{} {} {} {}'.format(weather_icon, description, thermo_icon, temp), flush=True)
except (HTTPError, Timeout, ConnectionError):
print(error_icon, flush=True)
raise MyInternetIsShitty |
def main():
' main function '
arg = get_args()
if (arg.log == 'debug'):
set_up_logging()
units = arg.unit
api_key = get_api_key()
city_id = get_city_id()
while True:
try:
update_weather(city_id, units, api_key)
except MyInternetIsShitty:
logging.info(cb('update failed: ', 'red'))
time.sleep(3)
else:
logging.info(cb('update success', 'green'))
time.sleep(700) | -4,733,848,066,255,199,000 | main function | .config/polybar/weather/weather.py | main | NearHuscarl/dotfiles | python | def main():
' '
arg = get_args()
if (arg.log == 'debug'):
set_up_logging()
units = arg.unit
api_key = get_api_key()
city_id = get_city_id()
while True:
try:
update_weather(city_id, units, api_key)
except MyInternetIsShitty:
logging.info(cb('update failed: ', 'red'))
time.sleep(3)
else:
logging.info(cb('update success', 'green'))
time.sleep(700) |
def run_server(server_port):
'Run the UDP pinger server\n '
with Socket(socket.AF_INET, socket.SOCK_DGRAM) as server_socket:
server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_socket.bind(('', server_port))
print('Ping server ready on port', server_port)
while True:
(_, client_address) = server_socket.recvfrom(1024)
server_socket.sendto(''.encode(), client_address)
return 0 | 6,592,167,236,038,547,000 | Run the UDP pinger server | ping/ping.py | run_server | akshayrb22/Kurose-and-Ross-socket-programming-exercises | python | def run_server(server_port):
'\n '
with Socket(socket.AF_INET, socket.SOCK_DGRAM) as server_socket:
server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_socket.bind((, server_port))
print('Ping server ready on port', server_port)
while True:
(_, client_address) = server_socket.recvfrom(1024)
server_socket.sendto(.encode(), client_address)
return 0 |
def run_client(server_address, server_port):
'Ping a UDP pinger server running at the given address\n '
raise NotImplementedError
return 0 | -7,235,053,255,711,612,000 | Ping a UDP pinger server running at the given address | ping/ping.py | run_client | akshayrb22/Kurose-and-Ross-socket-programming-exercises | python | def run_client(server_address, server_port):
'\n '
raise NotImplementedError
return 0 |
def _calculate_reciprocal_rank(self, hypothesis_ids: np.ndarray, reference_id: int) -> float:
'Calculate the reciprocal rank for a given hypothesis and reference\n \n Params:\n hypothesis_ids: Iterator of hypothesis ids (as numpy array) ordered by its relevance\n reference_id: Reference id (as a integer) of the correct id of response\n Returns:\n reciprocal rank\n '
hypothesis_ids = np.asarray(hypothesis_ids)
try:
rank = (np.where((hypothesis_ids == reference_id))[0][0] + 1)
except IndexError:
rank = (self.max_rank + 1)
if (rank > self.max_rank):
reciprocal_rank = 0.0
else:
reciprocal_rank = (1.0 / rank)
return reciprocal_rank | -8,653,979,882,358,909,000 | Calculate the reciprocal rank for a given hypothesis and reference
Params:
hypothesis_ids: Iterator of hypothesis ids (as numpy array) ordered by its relevance
reference_id: Reference id (as a integer) of the correct id of response
Returns:
reciprocal rank | tasks/retriever/mrr.py | _calculate_reciprocal_rank | platiagro/tasks | python | def _calculate_reciprocal_rank(self, hypothesis_ids: np.ndarray, reference_id: int) -> float:
'Calculate the reciprocal rank for a given hypothesis and reference\n \n Params:\n hypothesis_ids: Iterator of hypothesis ids (as numpy array) ordered by its relevance\n reference_id: Reference id (as a integer) of the correct id of response\n Returns:\n reciprocal rank\n '
hypothesis_ids = np.asarray(hypothesis_ids)
try:
rank = (np.where((hypothesis_ids == reference_id))[0][0] + 1)
except IndexError:
rank = (self.max_rank + 1)
if (rank > self.max_rank):
reciprocal_rank = 0.0
else:
reciprocal_rank = (1.0 / rank)
return reciprocal_rank |
def forward(self, batch_hypothesis_ids: List[np.ndarray], batch_reference_id: List[int]) -> float:
'Score the mean reciprocal rank for the batch\n \n Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank\n \n >>> batch_hypothesis_ids = [[1, 0, 2], [0, 2, 1], [1, 0, 2]]\n >>> batch_reference_id = [2, 2, 1]\n >>> mrr = MRR()\n >>> mrr(batch_hypothesis_ids, batch_reference_id)\n 0.61111111111111105\n\n Args:\n batch_hypothesis_ids: Batch of hypothesis ids (as numpy array) ordered by its relevance\n reference_id: Batch of reference id (as a integer) of the correct id of response\n Returns:\n Mean reciprocal rank (MRR)\n '
assert (len(batch_hypothesis_ids) == len(batch_reference_id)), 'Hypothesis batch and reference batch must have same length.'
batch_size = len(batch_hypothesis_ids)
mrr = 0
for (hypothesis_ids, reference_id) in zip(batch_hypothesis_ids, batch_reference_id):
reciprocal_rank = self._calculate_reciprocal_rank(hypothesis_ids, reference_id)
mrr += (reciprocal_rank / batch_size)
return mrr | -4,246,488,428,252,922,400 | Score the mean reciprocal rank for the batch
Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank
>>> batch_hypothesis_ids = [[1, 0, 2], [0, 2, 1], [1, 0, 2]]
>>> batch_reference_id = [2, 2, 1]
>>> mrr = MRR()
>>> mrr(batch_hypothesis_ids, batch_reference_id)
0.61111111111111105
Args:
batch_hypothesis_ids: Batch of hypothesis ids (as numpy array) ordered by its relevance
reference_id: Batch of reference id (as a integer) of the correct id of response
Returns:
Mean reciprocal rank (MRR) | tasks/retriever/mrr.py | forward | platiagro/tasks | python | def forward(self, batch_hypothesis_ids: List[np.ndarray], batch_reference_id: List[int]) -> float:
'Score the mean reciprocal rank for the batch\n \n Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank\n \n >>> batch_hypothesis_ids = [[1, 0, 2], [0, 2, 1], [1, 0, 2]]\n >>> batch_reference_id = [2, 2, 1]\n >>> mrr = MRR()\n >>> mrr(batch_hypothesis_ids, batch_reference_id)\n 0.61111111111111105\n\n Args:\n batch_hypothesis_ids: Batch of hypothesis ids (as numpy array) ordered by its relevance\n reference_id: Batch of reference id (as a integer) of the correct id of response\n Returns:\n Mean reciprocal rank (MRR)\n '
assert (len(batch_hypothesis_ids) == len(batch_reference_id)), 'Hypothesis batch and reference batch must have same length.'
batch_size = len(batch_hypothesis_ids)
mrr = 0
for (hypothesis_ids, reference_id) in zip(batch_hypothesis_ids, batch_reference_id):
reciprocal_rank = self._calculate_reciprocal_rank(hypothesis_ids, reference_id)
mrr += (reciprocal_rank / batch_size)
return mrr |
def get_oof_pred_proba(self, X, normalize=None, **kwargs):
'X should be the same X passed to `.fit`'
y_oof_pred_proba = self._get_oof_pred_proba(X=X, **kwargs)
if (normalize is None):
normalize = self.normalize_pred_probas
if normalize:
y_oof_pred_proba = normalize_pred_probas(y_oof_pred_proba, self.problem_type)
y_oof_pred_proba = y_oof_pred_proba.astype(np.float32)
return y_oof_pred_proba | -8,436,748,996,598,062,000 | X should be the same X passed to `.fit` | tabular/src/autogluon/tabular/models/knn/knn_model.py | get_oof_pred_proba | taesup-aws/autogluon | python | def get_oof_pred_proba(self, X, normalize=None, **kwargs):
y_oof_pred_proba = self._get_oof_pred_proba(X=X, **kwargs)
if (normalize is None):
normalize = self.normalize_pred_probas
if normalize:
y_oof_pred_proba = normalize_pred_probas(y_oof_pred_proba, self.problem_type)
y_oof_pred_proba = y_oof_pred_proba.astype(np.float32)
return y_oof_pred_proba |
def _fit_with_samples(self, X, y, time_limit, start_samples=10000, max_samples=None, sample_growth_factor=2, sample_time_growth_factor=8):
'\n Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.\n\n X and y must already be preprocessed.\n\n Parameters\n ----------\n X : np.ndarray\n The training data features (preprocessed).\n y : Series\n The training data ground truth labels.\n time_limit : float, default = None\n Time limit in seconds to adhere to when fitting model.\n start_samples : int, default = 10000\n Number of samples to start with. This will be multiplied by sample_growth_factor after each model fit to determine the next number of samples.\n For example, if start_samples=10000, sample_growth_factor=2, then the number of samples per model fit would be [10000, 20000, 40000, 80000, ...]\n max_samples : int, default = None\n The maximum number of samples to use.\n If None or greater than the number of rows in X, then it is set equal to the number of rows in X.\n sample_growth_factor : float, default = 2\n The rate of growth in sample size between each model fit. If 2, then the sample size doubles after each fit.\n sample_time_growth_factor : float, default = 8\n The multiplier to the expected fit time of the next model. If `sample_time_growth_factor=8` and a model took 10 seconds to train, the next model fit will be expected to take 80 seconds.\n If an expected time is greater than the remaining time in `time_limit`, the model will not be trained and the method will return early.\n '
time_start = time.time()
num_rows_samples = []
if (max_samples is None):
num_rows_max = len(X)
else:
num_rows_max = min(len(X), max_samples)
num_rows_cur = start_samples
while True:
num_rows_cur = min(num_rows_cur, num_rows_max)
num_rows_samples.append(num_rows_cur)
if (num_rows_cur == num_rows_max):
break
num_rows_cur *= sample_growth_factor
num_rows_cur = math.ceil(num_rows_cur)
if ((num_rows_cur * 1.5) >= num_rows_max):
num_rows_cur = num_rows_max
def sample_func(chunk, frac):
n = max(math.ceil((len(chunk) * frac)), 1)
return chunk.sample(n=n, replace=False, random_state=0)
if (self.problem_type != REGRESSION):
y_df = y.to_frame(name='label').reset_index(drop=True)
else:
y_df = None
time_start_sample_loop = time.time()
time_limit_left = (time_limit - (time_start_sample_loop - time_start))
model_type = self._get_model_type()
idx = None
for (i, samples) in enumerate(num_rows_samples):
if (samples != num_rows_max):
if (self.problem_type == REGRESSION):
idx = np.random.choice(num_rows_max, size=samples, replace=False)
else:
idx = y_df.groupby('label', group_keys=False).apply(sample_func, frac=(samples / num_rows_max)).index
X_samp = X[idx, :]
y_samp = y.iloc[idx]
else:
X_samp = X
y_samp = y
idx = None
self.model = model_type(**self._get_model_params()).fit(X_samp, y_samp)
time_limit_left_prior = time_limit_left
time_fit_end_sample = time.time()
time_limit_left = (time_limit - (time_fit_end_sample - time_start))
time_fit_sample = (time_limit_left_prior - time_limit_left)
time_required_for_next = (time_fit_sample * sample_time_growth_factor)
logger.log(15, f' {round(time_fit_sample, 2)}s = Train Time (Using {samples}/{num_rows_max} rows) ({round(time_limit_left, 2)}s remaining time)')
if ((time_required_for_next > time_limit_left) and (i != (len(num_rows_samples) - 1))):
logger.log(20, f' Not enough time to train KNN model on all training rows. Fit {samples}/{num_rows_max} rows. (Training KNN model on {num_rows_samples[(i + 1)]} rows is expected to take {round(time_required_for_next, 2)}s)')
break
if (idx is not None):
idx = set(idx)
self._X_unused_index = [i for i in range(num_rows_max) if (i not in idx)]
return self.model | 7,872,693,222,056,237,000 | Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.
X and y must already be preprocessed.
Parameters
----------
X : np.ndarray
The training data features (preprocessed).
y : Series
The training data ground truth labels.
time_limit : float, default = None
Time limit in seconds to adhere to when fitting model.
start_samples : int, default = 10000
Number of samples to start with. This will be multiplied by sample_growth_factor after each model fit to determine the next number of samples.
For example, if start_samples=10000, sample_growth_factor=2, then the number of samples per model fit would be [10000, 20000, 40000, 80000, ...]
max_samples : int, default = None
The maximum number of samples to use.
If None or greater than the number of rows in X, then it is set equal to the number of rows in X.
sample_growth_factor : float, default = 2
The rate of growth in sample size between each model fit. If 2, then the sample size doubles after each fit.
sample_time_growth_factor : float, default = 8
The multiplier to the expected fit time of the next model. If `sample_time_growth_factor=8` and a model took 10 seconds to train, the next model fit will be expected to take 80 seconds.
If an expected time is greater than the remaining time in `time_limit`, the model will not be trained and the method will return early. | tabular/src/autogluon/tabular/models/knn/knn_model.py | _fit_with_samples | taesup-aws/autogluon | python | def _fit_with_samples(self, X, y, time_limit, start_samples=10000, max_samples=None, sample_growth_factor=2, sample_time_growth_factor=8):
'\n Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.\n\n X and y must already be preprocessed.\n\n Parameters\n ----------\n X : np.ndarray\n The training data features (preprocessed).\n y : Series\n The training data ground truth labels.\n time_limit : float, default = None\n Time limit in seconds to adhere to when fitting model.\n start_samples : int, default = 10000\n Number of samples to start with. This will be multiplied by sample_growth_factor after each model fit to determine the next number of samples.\n For example, if start_samples=10000, sample_growth_factor=2, then the number of samples per model fit would be [10000, 20000, 40000, 80000, ...]\n max_samples : int, default = None\n The maximum number of samples to use.\n If None or greater than the number of rows in X, then it is set equal to the number of rows in X.\n sample_growth_factor : float, default = 2\n The rate of growth in sample size between each model fit. If 2, then the sample size doubles after each fit.\n sample_time_growth_factor : float, default = 8\n The multiplier to the expected fit time of the next model. If `sample_time_growth_factor=8` and a model took 10 seconds to train, the next model fit will be expected to take 80 seconds.\n If an expected time is greater than the remaining time in `time_limit`, the model will not be trained and the method will return early.\n '
time_start = time.time()
num_rows_samples = []
if (max_samples is None):
num_rows_max = len(X)
else:
num_rows_max = min(len(X), max_samples)
num_rows_cur = start_samples
while True:
num_rows_cur = min(num_rows_cur, num_rows_max)
num_rows_samples.append(num_rows_cur)
if (num_rows_cur == num_rows_max):
break
num_rows_cur *= sample_growth_factor
num_rows_cur = math.ceil(num_rows_cur)
if ((num_rows_cur * 1.5) >= num_rows_max):
num_rows_cur = num_rows_max
def sample_func(chunk, frac):
n = max(math.ceil((len(chunk) * frac)), 1)
return chunk.sample(n=n, replace=False, random_state=0)
if (self.problem_type != REGRESSION):
y_df = y.to_frame(name='label').reset_index(drop=True)
else:
y_df = None
time_start_sample_loop = time.time()
time_limit_left = (time_limit - (time_start_sample_loop - time_start))
model_type = self._get_model_type()
idx = None
for (i, samples) in enumerate(num_rows_samples):
if (samples != num_rows_max):
if (self.problem_type == REGRESSION):
idx = np.random.choice(num_rows_max, size=samples, replace=False)
else:
idx = y_df.groupby('label', group_keys=False).apply(sample_func, frac=(samples / num_rows_max)).index
X_samp = X[idx, :]
y_samp = y.iloc[idx]
else:
X_samp = X
y_samp = y
idx = None
self.model = model_type(**self._get_model_params()).fit(X_samp, y_samp)
time_limit_left_prior = time_limit_left
time_fit_end_sample = time.time()
time_limit_left = (time_limit - (time_fit_end_sample - time_start))
time_fit_sample = (time_limit_left_prior - time_limit_left)
time_required_for_next = (time_fit_sample * sample_time_growth_factor)
logger.log(15, f' {round(time_fit_sample, 2)}s = Train Time (Using {samples}/{num_rows_max} rows) ({round(time_limit_left, 2)}s remaining time)')
if ((time_required_for_next > time_limit_left) and (i != (len(num_rows_samples) - 1))):
logger.log(20, f' Not enough time to train KNN model on all training rows. Fit {samples}/{num_rows_max} rows. (Training KNN model on {num_rows_samples[(i + 1)]} rows is expected to take {round(time_required_for_next, 2)}s)')
break
if (idx is not None):
idx = set(idx)
self._X_unused_index = [i for i in range(num_rows_max) if (i not in idx)]
return self.model |
def naked_twins(values):
"Eliminate values using the naked twins strategy.\n\n The naked twins strategy says that if you have two or more unallocated boxes\n in a unit and there are only two digits that can go in those two boxes, then\n those two digits can be eliminated from the possible assignments of all other\n boxes in the same unit.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict\n The values dictionary with the naked twins eliminated from peers\n\n Notes\n -----\n Your solution can either process all pairs of naked twins from the input once,\n or it can continue processing pairs of naked twins until there are no such\n pairs remaining -- the project assistant test suite will accept either\n convention. However, it will not accept code that does not process all pairs\n of naked twins from the original input. (For example, if you start processing\n pairs of twins and eliminate another pair of twins before the second pair\n is processed then your code will fail the PA test suite.)\n\n The first convention is preferred for consistency with the other strategies,\n and because it is simpler (since the reduce_puzzle function already calls this\n strategy repeatedly).\n\n See Also\n --------\n Pseudocode for this algorithm on github:\n https://github.com/udacity/artificial-intelligence/blob/master/Projects/1_Sudoku/pseudocode.md\n "
"\n out = values.copy()\n len_2_boxes = [box for box in values if len(values[box]) == 2]\n for boxA in len_2_boxes:\n boxAPeers = peers[boxA]\n for boxB in boxAPeers:\n if values[boxA] == values[boxB]:\n intersect = [val for val in boxAPeers if val in peers[boxB]]\n for peer in intersect:\n out[peer] = out[peer].replace(values[boxA], '')\n return out\n "
out = values.copy()
for boxA in values:
boxAPeers = peers[boxA]
for boxB in boxAPeers:
if ((values[boxA] == values[boxB]) and (len(values[boxA]) == 2)):
intersect = [val for val in boxAPeers if (val in peers[boxB])]
for peer in intersect:
for digit in values[boxA]:
out[peer] = out[peer].replace(digit, '')
return out | -1,691,231,728,503,389,700 | Eliminate values using the naked twins strategy.
The naked twins strategy says that if you have two or more unallocated boxes
in a unit and there are only two digits that can go in those two boxes, then
those two digits can be eliminated from the possible assignments of all other
boxes in the same unit.
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict
The values dictionary with the naked twins eliminated from peers
Notes
-----
Your solution can either process all pairs of naked twins from the input once,
or it can continue processing pairs of naked twins until there are no such
pairs remaining -- the project assistant test suite will accept either
convention. However, it will not accept code that does not process all pairs
of naked twins from the original input. (For example, if you start processing
pairs of twins and eliminate another pair of twins before the second pair
is processed then your code will fail the PA test suite.)
The first convention is preferred for consistency with the other strategies,
and because it is simpler (since the reduce_puzzle function already calls this
strategy repeatedly).
See Also
--------
Pseudocode for this algorithm on github:
https://github.com/udacity/artificial-intelligence/blob/master/Projects/1_Sudoku/pseudocode.md | Projects/1_Sudoku/solution.py | naked_twins | justinlnx/artificial-intelligence | python | def naked_twins(values):
"Eliminate values using the naked twins strategy.\n\n The naked twins strategy says that if you have two or more unallocated boxes\n in a unit and there are only two digits that can go in those two boxes, then\n those two digits can be eliminated from the possible assignments of all other\n boxes in the same unit.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict\n The values dictionary with the naked twins eliminated from peers\n\n Notes\n -----\n Your solution can either process all pairs of naked twins from the input once,\n or it can continue processing pairs of naked twins until there are no such\n pairs remaining -- the project assistant test suite will accept either\n convention. However, it will not accept code that does not process all pairs\n of naked twins from the original input. (For example, if you start processing\n pairs of twins and eliminate another pair of twins before the second pair\n is processed then your code will fail the PA test suite.)\n\n The first convention is preferred for consistency with the other strategies,\n and because it is simpler (since the reduce_puzzle function already calls this\n strategy repeatedly).\n\n See Also\n --------\n Pseudocode for this algorithm on github:\n https://github.com/udacity/artificial-intelligence/blob/master/Projects/1_Sudoku/pseudocode.md\n "
"\n out = values.copy()\n len_2_boxes = [box for box in values if len(values[box]) == 2]\n for boxA in len_2_boxes:\n boxAPeers = peers[boxA]\n for boxB in boxAPeers:\n if values[boxA] == values[boxB]:\n intersect = [val for val in boxAPeers if val in peers[boxB]]\n for peer in intersect:\n out[peer] = out[peer].replace(values[boxA], )\n return out\n "
out = values.copy()
for boxA in values:
boxAPeers = peers[boxA]
for boxB in boxAPeers:
if ((values[boxA] == values[boxB]) and (len(values[boxA]) == 2)):
intersect = [val for val in boxAPeers if (val in peers[boxB])]
for peer in intersect:
for digit in values[boxA]:
out[peer] = out[peer].replace(digit, )
return out |
def eliminate(values):
"Apply the eliminate strategy to a Sudoku puzzle\n\n The eliminate strategy says that if a box has a value assigned, then none\n of the peers of that box can have the same value.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict\n The values dictionary with the assigned values eliminated from peers\n "
solved_values = [box for box in values.keys() if (len(values[box]) == 1)]
for box in solved_values:
digit = values[box]
for peer in peers[box]:
values[peer] = values[peer].replace(digit, '')
return values | 1,745,120,404,089,232,000 | Apply the eliminate strategy to a Sudoku puzzle
The eliminate strategy says that if a box has a value assigned, then none
of the peers of that box can have the same value.
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict
The values dictionary with the assigned values eliminated from peers | Projects/1_Sudoku/solution.py | eliminate | justinlnx/artificial-intelligence | python | def eliminate(values):
"Apply the eliminate strategy to a Sudoku puzzle\n\n The eliminate strategy says that if a box has a value assigned, then none\n of the peers of that box can have the same value.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict\n The values dictionary with the assigned values eliminated from peers\n "
solved_values = [box for box in values.keys() if (len(values[box]) == 1)]
for box in solved_values:
digit = values[box]
for peer in peers[box]:
values[peer] = values[peer].replace(digit, )
return values |
def only_choice(values):
"Apply the only choice strategy to a Sudoku puzzle\n\n The only choice strategy says that if only one box in a unit allows a certain\n digit, then that box must be assigned that digit.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict\n The values dictionary with all single-valued boxes assigned\n\n Notes\n -----\n You should be able to complete this function by copying your code from the classroom\n "
for unit in unitlist:
for digit in '123456789':
dplaces = [box for box in unit if (digit in values[box])]
if (len(dplaces) == 1):
values[dplaces[0]] = digit
return values | -4,383,931,250,168,897,500 | Apply the only choice strategy to a Sudoku puzzle
The only choice strategy says that if only one box in a unit allows a certain
digit, then that box must be assigned that digit.
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict
The values dictionary with all single-valued boxes assigned
Notes
-----
You should be able to complete this function by copying your code from the classroom | Projects/1_Sudoku/solution.py | only_choice | justinlnx/artificial-intelligence | python | def only_choice(values):
"Apply the only choice strategy to a Sudoku puzzle\n\n The only choice strategy says that if only one box in a unit allows a certain\n digit, then that box must be assigned that digit.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict\n The values dictionary with all single-valued boxes assigned\n\n Notes\n -----\n You should be able to complete this function by copying your code from the classroom\n "
for unit in unitlist:
for digit in '123456789':
dplaces = [box for box in unit if (digit in values[box])]
if (len(dplaces) == 1):
values[dplaces[0]] = digit
return values |
def reduce_puzzle(values):
"Reduce a Sudoku puzzle by repeatedly applying all constraint strategies\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\n The values dictionary after continued application of the constraint strategies\n no longer produces any changes, or False if the puzzle is unsolvable \n "
solved_values = [box for box in values.keys() if (len(values[box]) == 1)]
stalled = False
while (not stalled):
solved_values_before = len([box for box in values.keys() if (len(values[box]) == 1)])
values = eliminate(values)
values = only_choice(values)
values = naked_twins(values)
solved_values_after = len([box for box in values.keys() if (len(values[box]) == 1)])
stalled = (solved_values_before == solved_values_after)
if len([box for box in values.keys() if (len(values[box]) == 0)]):
return False
return values | -3,851,804,040,853,470,000 | Reduce a Sudoku puzzle by repeatedly applying all constraint strategies
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict or False
The values dictionary after continued application of the constraint strategies
no longer produces any changes, or False if the puzzle is unsolvable | Projects/1_Sudoku/solution.py | reduce_puzzle | justinlnx/artificial-intelligence | python | def reduce_puzzle(values):
"Reduce a Sudoku puzzle by repeatedly applying all constraint strategies\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\n The values dictionary after continued application of the constraint strategies\n no longer produces any changes, or False if the puzzle is unsolvable \n "
solved_values = [box for box in values.keys() if (len(values[box]) == 1)]
stalled = False
while (not stalled):
solved_values_before = len([box for box in values.keys() if (len(values[box]) == 1)])
values = eliminate(values)
values = only_choice(values)
values = naked_twins(values)
solved_values_after = len([box for box in values.keys() if (len(values[box]) == 1)])
stalled = (solved_values_before == solved_values_after)
if len([box for box in values.keys() if (len(values[box]) == 0)]):
return False
return values |
def search(values):
"Apply depth first search to solve Sudoku puzzles in order to solve puzzles\n that cannot be solved by repeated reduction alone.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\n The values dictionary with all boxes assigned or False\n\n Notes\n -----\n You should be able to complete this function by copying your code from the classroom\n and extending it to call the naked twins strategy.\n "
'Using depth-first search and propagation, try all possible values.'
values = reduce_puzzle(values)
if (values is False):
return False
if all(((len(values[s]) == 1) for s in boxes)):
return values
(n, s) = min(((len(values[s]), s) for s in boxes if (len(values[s]) > 1)))
for value in values[s]:
new_sudoku = values.copy()
new_sudoku[s] = value
attempt = search(new_sudoku)
if attempt:
return attempt | -5,391,375,916,073,540,000 | Apply depth first search to solve Sudoku puzzles in order to solve puzzles
that cannot be solved by repeated reduction alone.
Parameters
----------
values(dict)
a dictionary of the form {'box_name': '123456789', ...}
Returns
-------
dict or False
The values dictionary with all boxes assigned or False
Notes
-----
You should be able to complete this function by copying your code from the classroom
and extending it to call the naked twins strategy. | Projects/1_Sudoku/solution.py | search | justinlnx/artificial-intelligence | python | def search(values):
"Apply depth first search to solve Sudoku puzzles in order to solve puzzles\n that cannot be solved by repeated reduction alone.\n\n Parameters\n ----------\n values(dict)\n a dictionary of the form {'box_name': '123456789', ...}\n\n Returns\n -------\n dict or False\n The values dictionary with all boxes assigned or False\n\n Notes\n -----\n You should be able to complete this function by copying your code from the classroom\n and extending it to call the naked twins strategy.\n "
'Using depth-first search and propagation, try all possible values.'
values = reduce_puzzle(values)
if (values is False):
return False
if all(((len(values[s]) == 1) for s in boxes)):
return values
(n, s) = min(((len(values[s]), s) for s in boxes if (len(values[s]) > 1)))
for value in values[s]:
new_sudoku = values.copy()
new_sudoku[s] = value
attempt = search(new_sudoku)
if attempt:
return attempt |
def solve(grid):
"Find the solution to a Sudoku puzzle using search and constraint propagation\n\n Parameters\n ----------\n grid(string)\n a string representing a sudoku grid.\n \n Ex. '2.............62....1....7...6..8...3...9...7...6..4...4....8....52.............3'\n\n Returns\n -------\n dict or False\n The dictionary representation of the final sudoku grid or False if no solution exists.\n "
values = grid2values(grid)
values = search(values)
return values | 7,617,055,493,705,177,000 | Find the solution to a Sudoku puzzle using search and constraint propagation
Parameters
----------
grid(string)
a string representing a sudoku grid.
Ex. '2.............62....1....7...6..8...3...9...7...6..4...4....8....52.............3'
Returns
-------
dict or False
The dictionary representation of the final sudoku grid or False if no solution exists. | Projects/1_Sudoku/solution.py | solve | justinlnx/artificial-intelligence | python | def solve(grid):
"Find the solution to a Sudoku puzzle using search and constraint propagation\n\n Parameters\n ----------\n grid(string)\n a string representing a sudoku grid.\n \n Ex. '2.............62....1....7...6..8...3...9...7...6..4...4....8....52.............3'\n\n Returns\n -------\n dict or False\n The dictionary representation of the final sudoku grid or False if no solution exists.\n "
values = grid2values(grid)
values = search(values)
return values |
def basic_argument_parser(distributed=True, requires_config_file=True, requires_output_dir=True):
' Basic cli tool parser for Detectron2Go binaries '
parser = argparse.ArgumentParser(description='PyTorch Object Detection Training')
parser.add_argument('--runner', type=str, default='d2go.runner.GeneralizedRCNNRunner', help='Full class name, i.e. (package.)module.class')
parser.add_argument('--config-file', help='path to config file', default='', required=requires_config_file, metavar='FILE')
parser.add_argument('--output-dir', help='When given, this will override the OUTPUT_DIR in the config-file', required=requires_output_dir, default=None, type=str)
parser.add_argument('opts', help='Modify config options using the command-line', default=None, nargs=argparse.REMAINDER)
if distributed:
parser.add_argument('--num-processes', type=int, default=1, help='number of gpus per machine')
parser.add_argument('--num-machines', type=int, default=1)
parser.add_argument('--machine-rank', type=int, default=0, help='the rank of this machine (unique per machine)')
parser.add_argument('--dist-url', default='file:///tmp/d2go_dist_file_{}'.format(time.time()))
parser.add_argument('--dist-backend', type=str, default='NCCL')
if (not requires_config_file):
parser.add_argument('--datasets', type=str, nargs='+', required=True, help='cfg.DATASETS.TEST')
parser.add_argument('--min_size', type=int, required=True, help='cfg.INPUT.MIN_SIZE_TEST')
parser.add_argument('--max_size', type=int, required=True, help='cfg.INPUT.MAX_SIZE_TEST')
return parser
return parser | -3,745,655,481,647,895,000 | Basic cli tool parser for Detectron2Go binaries | d2go/setup.py | basic_argument_parser | Dinesh101041/d2go | python | def basic_argument_parser(distributed=True, requires_config_file=True, requires_output_dir=True):
' '
parser = argparse.ArgumentParser(description='PyTorch Object Detection Training')
parser.add_argument('--runner', type=str, default='d2go.runner.GeneralizedRCNNRunner', help='Full class name, i.e. (package.)module.class')
parser.add_argument('--config-file', help='path to config file', default=, required=requires_config_file, metavar='FILE')
parser.add_argument('--output-dir', help='When given, this will override the OUTPUT_DIR in the config-file', required=requires_output_dir, default=None, type=str)
parser.add_argument('opts', help='Modify config options using the command-line', default=None, nargs=argparse.REMAINDER)
if distributed:
parser.add_argument('--num-processes', type=int, default=1, help='number of gpus per machine')
parser.add_argument('--num-machines', type=int, default=1)
parser.add_argument('--machine-rank', type=int, default=0, help='the rank of this machine (unique per machine)')
parser.add_argument('--dist-url', default='file:///tmp/d2go_dist_file_{}'.format(time.time()))
parser.add_argument('--dist-backend', type=str, default='NCCL')
if (not requires_config_file):
parser.add_argument('--datasets', type=str, nargs='+', required=True, help='cfg.DATASETS.TEST')
parser.add_argument('--min_size', type=int, required=True, help='cfg.INPUT.MIN_SIZE_TEST')
parser.add_argument('--max_size', type=int, required=True, help='cfg.INPUT.MAX_SIZE_TEST')
return parser
return parser |
def create_cfg_from_cli_args(args, default_cfg):
"\n Instead of loading from defaults.py, this binary only includes necessary\n configs building from scratch, and overrides them from args. There're two\n levels of config:\n _C: the config system used by this binary, which is a sub-set of training\n config, override by configurable_cfg. It can also be override by\n args.opts for convinience.\n configurable_cfg: common configs that user should explicitly specify\n in the args.\n "
_C = CN()
_C.INPUT = default_cfg.INPUT
_C.DATASETS = default_cfg.DATASETS
_C.DATALOADER = default_cfg.DATALOADER
_C.TEST = default_cfg.TEST
if hasattr(default_cfg, 'D2GO_DATA'):
_C.D2GO_DATA = default_cfg.D2GO_DATA
if hasattr(default_cfg, 'TENSORBOARD'):
_C.TENSORBOARD = default_cfg.TENSORBOARD
_C.MODEL = CN()
_C.MODEL.META_ARCHITECTURE = default_cfg.MODEL.META_ARCHITECTURE
_C.MODEL.MASK_ON = default_cfg.MODEL.MASK_ON
_C.MODEL.KEYPOINT_ON = default_cfg.MODEL.KEYPOINT_ON
_C.MODEL.LOAD_PROPOSALS = default_cfg.MODEL.LOAD_PROPOSALS
assert (_C.MODEL.LOAD_PROPOSALS is False), "caffe2 model doesn't support"
_C.OUTPUT_DIR = args.output_dir
configurable_cfg = ['DATASETS.TEST', args.datasets, 'INPUT.MIN_SIZE_TEST', args.min_size, 'INPUT.MAX_SIZE_TEST', args.max_size]
cfg = _C.clone()
cfg.merge_from_list(configurable_cfg)
cfg.merge_from_list(args.opts)
return cfg | 1,567,503,064,963,738,400 | Instead of loading from defaults.py, this binary only includes necessary
configs building from scratch, and overrides them from args. There're two
levels of config:
_C: the config system used by this binary, which is a sub-set of training
config, override by configurable_cfg. It can also be override by
args.opts for convinience.
configurable_cfg: common configs that user should explicitly specify
in the args. | d2go/setup.py | create_cfg_from_cli_args | Dinesh101041/d2go | python | def create_cfg_from_cli_args(args, default_cfg):
"\n Instead of loading from defaults.py, this binary only includes necessary\n configs building from scratch, and overrides them from args. There're two\n levels of config:\n _C: the config system used by this binary, which is a sub-set of training\n config, override by configurable_cfg. It can also be override by\n args.opts for convinience.\n configurable_cfg: common configs that user should explicitly specify\n in the args.\n "
_C = CN()
_C.INPUT = default_cfg.INPUT
_C.DATASETS = default_cfg.DATASETS
_C.DATALOADER = default_cfg.DATALOADER
_C.TEST = default_cfg.TEST
if hasattr(default_cfg, 'D2GO_DATA'):
_C.D2GO_DATA = default_cfg.D2GO_DATA
if hasattr(default_cfg, 'TENSORBOARD'):
_C.TENSORBOARD = default_cfg.TENSORBOARD
_C.MODEL = CN()
_C.MODEL.META_ARCHITECTURE = default_cfg.MODEL.META_ARCHITECTURE
_C.MODEL.MASK_ON = default_cfg.MODEL.MASK_ON
_C.MODEL.KEYPOINT_ON = default_cfg.MODEL.KEYPOINT_ON
_C.MODEL.LOAD_PROPOSALS = default_cfg.MODEL.LOAD_PROPOSALS
assert (_C.MODEL.LOAD_PROPOSALS is False), "caffe2 model doesn't support"
_C.OUTPUT_DIR = args.output_dir
configurable_cfg = ['DATASETS.TEST', args.datasets, 'INPUT.MIN_SIZE_TEST', args.min_size, 'INPUT.MAX_SIZE_TEST', args.max_size]
cfg = _C.clone()
cfg.merge_from_list(configurable_cfg)
cfg.merge_from_list(args.opts)
return cfg |
def prepare_for_launch(args):
'\n Load config, figure out working directory, create runner.\n - when args.config_file is empty, returned cfg will be the default one\n - returned output_dir will always be non empty, args.output_dir has higher\n priority than cfg.OUTPUT_DIR.\n '
print(args)
runner = create_runner(args.runner)
cfg = runner.get_default_cfg()
if args.config_file:
with PathManager.open(reroute_config_path(args.config_file), 'r') as f:
print('Loaded config file {}:\n{}'.format(args.config_file, f.read()))
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
else:
cfg = create_cfg_from_cli_args(args, default_cfg=cfg)
cfg.freeze()
assert (args.output_dir or args.config_file)
output_dir = (args.output_dir or cfg.OUTPUT_DIR)
return (cfg, output_dir, runner) | 8,141,107,573,497,229,000 | Load config, figure out working directory, create runner.
- when args.config_file is empty, returned cfg will be the default one
- returned output_dir will always be non empty, args.output_dir has higher
priority than cfg.OUTPUT_DIR. | d2go/setup.py | prepare_for_launch | Dinesh101041/d2go | python | def prepare_for_launch(args):
'\n Load config, figure out working directory, create runner.\n - when args.config_file is empty, returned cfg will be the default one\n - returned output_dir will always be non empty, args.output_dir has higher\n priority than cfg.OUTPUT_DIR.\n '
print(args)
runner = create_runner(args.runner)
cfg = runner.get_default_cfg()
if args.config_file:
with PathManager.open(reroute_config_path(args.config_file), 'r') as f:
print('Loaded config file {}:\n{}'.format(args.config_file, f.read()))
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
else:
cfg = create_cfg_from_cli_args(args, default_cfg=cfg)
cfg.freeze()
assert (args.output_dir or args.config_file)
output_dir = (args.output_dir or cfg.OUTPUT_DIR)
return (cfg, output_dir, runner) |
def setup_after_launch(cfg, output_dir, runner):
'\n Set things up after entering DDP, including\n - creating working directory\n - setting up logger\n - logging environment\n - initializing runner\n '
create_dir_on_global_main_process(output_dir)
comm.synchronize()
setup_loggers(output_dir)
cfg.freeze()
if (cfg.OUTPUT_DIR != output_dir):
with temp_defrost(cfg):
logger.warning('Override cfg.OUTPUT_DIR ({}) to be the same as output_dir {}'.format(cfg.OUTPUT_DIR, output_dir))
cfg.OUTPUT_DIR = output_dir
logger.info('Initializing runner ...')
runner = initialize_runner(runner, cfg)
log_info(cfg, runner)
dump_cfg(cfg, os.path.join(output_dir, 'config.yaml'))
auto_scale_world_size(cfg, new_world_size=comm.get_world_size()) | -8,754,067,670,595,316,000 | Set things up after entering DDP, including
- creating working directory
- setting up logger
- logging environment
- initializing runner | d2go/setup.py | setup_after_launch | Dinesh101041/d2go | python | def setup_after_launch(cfg, output_dir, runner):
'\n Set things up after entering DDP, including\n - creating working directory\n - setting up logger\n - logging environment\n - initializing runner\n '
create_dir_on_global_main_process(output_dir)
comm.synchronize()
setup_loggers(output_dir)
cfg.freeze()
if (cfg.OUTPUT_DIR != output_dir):
with temp_defrost(cfg):
logger.warning('Override cfg.OUTPUT_DIR ({}) to be the same as output_dir {}'.format(cfg.OUTPUT_DIR, output_dir))
cfg.OUTPUT_DIR = output_dir
logger.info('Initializing runner ...')
runner = initialize_runner(runner, cfg)
log_info(cfg, runner)
dump_cfg(cfg, os.path.join(output_dir, 'config.yaml'))
auto_scale_world_size(cfg, new_world_size=comm.get_world_size()) |
def __init__(self, main_window, palette):
"\n Creates a new window for user to input\n which regions to add to scene.\n\n Arguments:\n ----------\n\n main_window: reference to the App's main window\n palette: main_window's palette, used to style widgets\n "
super().__init__()
self.setWindowTitle('Add brain regions')
self.ui()
self.main_window = main_window
self.setStyleSheet(update_css(style, palette)) | 2,832,295,261,314,470,000 | Creates a new window for user to input
which regions to add to scene.
Arguments:
----------
main_window: reference to the App's main window
palette: main_window's palette, used to style widgets | brainrender_gui/widgets/add_regions.py | __init__ | brainglobe/bg-brainrender-gui | python | def __init__(self, main_window, palette):
"\n Creates a new window for user to input\n which regions to add to scene.\n\n Arguments:\n ----------\n\n main_window: reference to the App's main window\n palette: main_window's palette, used to style widgets\n "
super().__init__()
self.setWindowTitle('Add brain regions')
self.ui()
self.main_window = main_window
self.setStyleSheet(update_css(style, palette)) |
def ui(self):
"\n Define UI's elements\n "
self.setGeometry(self.left, self.top, self.width, self.height)
layout = QVBoxLayout()
label = QLabel(self)
label.setObjectName('PopupLabel')
label.setText(self.label_msg)
self.textbox = QLineEdit(self)
alpha_label = QLabel(self)
alpha_label.setObjectName('PopupLabel')
alpha_label.setText('Alpha')
self.alpha_textbox = QLineEdit(self)
self.alpha_textbox.setText(str(1.0))
color_label = QLabel(self)
color_label.setObjectName('PopupLabel')
color_label.setText('Color')
self.color_textbox = QLineEdit(self)
self.color_textbox.setText('atlas')
self.button = QPushButton('Add regions', self)
self.button.clicked.connect(self.on_click)
self.button.setObjectName('RegionsButton')
layout.addWidget(label)
layout.addWidget(self.textbox)
layout.addWidget(alpha_label)
layout.addWidget(self.alpha_textbox)
layout.addWidget(color_label)
layout.addWidget(self.color_textbox)
layout.addWidget(self.button)
self.setLayout(layout)
self.show() | -7,489,549,448,365,388,000 | Define UI's elements | brainrender_gui/widgets/add_regions.py | ui | brainglobe/bg-brainrender-gui | python | def ui(self):
"\n \n "
self.setGeometry(self.left, self.top, self.width, self.height)
layout = QVBoxLayout()
label = QLabel(self)
label.setObjectName('PopupLabel')
label.setText(self.label_msg)
self.textbox = QLineEdit(self)
alpha_label = QLabel(self)
alpha_label.setObjectName('PopupLabel')
alpha_label.setText('Alpha')
self.alpha_textbox = QLineEdit(self)
self.alpha_textbox.setText(str(1.0))
color_label = QLabel(self)
color_label.setObjectName('PopupLabel')
color_label.setText('Color')
self.color_textbox = QLineEdit(self)
self.color_textbox.setText('atlas')
self.button = QPushButton('Add regions', self)
self.button.clicked.connect(self.on_click)
self.button.setObjectName('RegionsButton')
layout.addWidget(label)
layout.addWidget(self.textbox)
layout.addWidget(alpha_label)
layout.addWidget(self.alpha_textbox)
layout.addWidget(color_label)
layout.addWidget(self.color_textbox)
layout.addWidget(self.button)
self.setLayout(layout)
self.show() |
def on_click(self):
"\n On click or 'Enter' get the regions\n from the input and call the add_regions\n method of the main window\n "
regions = self.textbox.text().split(' ')
self.main_window.add_regions(regions, self.alpha_textbox.text(), self.color_textbox.text())
self.close() | -1,581,329,918,703,527,000 | On click or 'Enter' get the regions
from the input and call the add_regions
method of the main window | brainrender_gui/widgets/add_regions.py | on_click | brainglobe/bg-brainrender-gui | python | def on_click(self):
"\n On click or 'Enter' get the regions\n from the input and call the add_regions\n method of the main window\n "
regions = self.textbox.text().split(' ')
self.main_window.add_regions(regions, self.alpha_textbox.text(), self.color_textbox.text())
self.close() |
def update_user_data():
'Update user_data to enable or disable Telemetry.\n\n If employment data has been changed Telemetry might be switched on\n automatically. The opt-in decision is taken for the new employee. Non employees\n will have an option to enable data collection.\n '
is_employee_changed = user_data.set_user_data()
if (not is_employee_changed):
return
if user_data.is_employee:
logger.warning('Enabled collecting MozPhab usage data.\nSee https://moz-conduit.readthedocs.io/en/latest/mozphab-data-collection.html')
config.telemetry_enabled = True
else:
opt_in = (prompt('Would you like to allow MozPhab to collect usage data?', ['Yes', 'No']) == 'Yes')
if opt_in:
config.telemetry_enabled = True
else:
logger.info('MozPhab usage data collection disabled.\nSee https://moz-conduit.readthedocs.io/en/latest/mozphab-data-collection.html')
config.telemetry_enabled = False
config.write() | 4,639,693,185,802,704,000 | Update user_data to enable or disable Telemetry.
If employment data has been changed Telemetry might be switched on
automatically. The opt-in decision is taken for the new employee. Non employees
will have an option to enable data collection. | mozphab/telemetry.py | update_user_data | cgsheeh/review | python | def update_user_data():
'Update user_data to enable or disable Telemetry.\n\n If employment data has been changed Telemetry might be switched on\n automatically. The opt-in decision is taken for the new employee. Non employees\n will have an option to enable data collection.\n '
is_employee_changed = user_data.set_user_data()
if (not is_employee_changed):
return
if user_data.is_employee:
logger.warning('Enabled collecting MozPhab usage data.\nSee https://moz-conduit.readthedocs.io/en/latest/mozphab-data-collection.html')
config.telemetry_enabled = True
else:
opt_in = (prompt('Would you like to allow MozPhab to collect usage data?', ['Yes', 'No']) == 'Yes')
if opt_in:
config.telemetry_enabled = True
else:
logger.info('MozPhab usage data collection disabled.\nSee https://moz-conduit.readthedocs.io/en/latest/mozphab-data-collection.html')
config.telemetry_enabled = False
config.write() |
def __init__(self):
'Initiate Glean, load pings and metrics.'
import glean
logging.getLogger('glean').setLevel(logging.DEBUG)
logger.debug('Initializing Glean...')
glean.Glean.initialize(application_id='MozPhab', application_version=MOZPHAB_VERSION, upload_enabled=True, configuration=glean.Configuration(), data_dir=(Path(environment.MOZBUILD_PATH) / 'telemetry-data'))
self._pings = glean.load_pings((environment.MOZPHAB_MAIN_DIR / 'pings.yaml'))
self._metrics = glean.load_metrics((environment.MOZPHAB_MAIN_DIR / 'metrics.yaml')) | -7,231,570,573,987,132,000 | Initiate Glean, load pings and metrics. | mozphab/telemetry.py | __init__ | cgsheeh/review | python | def __init__(self):
import glean
logging.getLogger('glean').setLevel(logging.DEBUG)
logger.debug('Initializing Glean...')
glean.Glean.initialize(application_id='MozPhab', application_version=MOZPHAB_VERSION, upload_enabled=True, configuration=glean.Configuration(), data_dir=(Path(environment.MOZBUILD_PATH) / 'telemetry-data'))
self._pings = glean.load_pings((environment.MOZPHAB_MAIN_DIR / 'pings.yaml'))
self._metrics = glean.load_metrics((environment.MOZPHAB_MAIN_DIR / 'metrics.yaml')) |
def _set_os(self):
'Collect human readable information about the OS version.\n\n For Linux it is setting a distribution name and version.\n '
(system, node, release, version, machine, processor) = platform.uname()
if (system == 'Linux'):
(distribution_name, distribution_number, _) = distro.linux_distribution(full_distribution_name=False)
distribution_version = ' '.join([distribution_name, distribution_number])
elif (system == 'Windows'):
(_release, distribution_version, _csd, _ptype) = platform.win32_ver()
elif (system == 'Darwin'):
(distribution_version, _versioninfo, _machine) = platform.mac_ver()
else:
distribution_version = release
self.environment.distribution_version.set(distribution_version) | -6,868,257,696,406,971,000 | Collect human readable information about the OS version.
For Linux it is setting a distribution name and version. | mozphab/telemetry.py | _set_os | cgsheeh/review | python | def _set_os(self):
'Collect human readable information about the OS version.\n\n For Linux it is setting a distribution name and version.\n '
(system, node, release, version, machine, processor) = platform.uname()
if (system == 'Linux'):
(distribution_name, distribution_number, _) = distro.linux_distribution(full_distribution_name=False)
distribution_version = ' '.join([distribution_name, distribution_number])
elif (system == 'Windows'):
(_release, distribution_version, _csd, _ptype) = platform.win32_ver()
elif (system == 'Darwin'):
(distribution_version, _versioninfo, _machine) = platform.mac_ver()
else:
distribution_version = release
self.environment.distribution_version.set(distribution_version) |
def set_metrics(self, args):
'Sets metrics common to all commands.'
self.usage.command.set(args.command)
self._set_os()
self._set_python()
self.usage.override_switch.set((getattr(args, 'force_vcs', False) or getattr(args, 'force', False)))
self.usage.command_time.start()
self.user.installation.set(user_data.installation_id)
self.user.id.set(user_data.user_code) | -1,575,089,079,134,722,300 | Sets metrics common to all commands. | mozphab/telemetry.py | set_metrics | cgsheeh/review | python | def set_metrics(self, args):
self.usage.command.set(args.command)
self._set_os()
self._set_python()
self.usage.override_switch.set((getattr(args, 'force_vcs', False) or getattr(args, 'force', False)))
self.usage.command_time.start()
self.user.installation.set(user_data.installation_id)
self.user.id.set(user_data.user_code) |
def binary_image_to_lut_indices(x):
'\n Convert a binary image to an index image that can be used with a lookup table\n to perform morphological operations. Non-zero elements in the image are interpreted\n as 1, zero elements as 0\n\n :param x: a 2D NumPy array.\n :return: a 2D NumPy array, same shape as x\n '
if (x.ndim != 2):
raise ValueError('x should have 2 dimensions, not {}'.format(x.ndim))
if (x.dtype != np.bool):
x = (x != 0)
x = np.pad(x, [(1, 1), (1, 1)], mode='constant')
lut_indices = (((((((((x[:(- 2), :(- 2)] * NEIGH_MASK_NORTH_WEST) + (x[:(- 2), 1:(- 1)] * NEIGH_MASK_NORTH)) + (x[:(- 2), 2:] * NEIGH_MASK_NORTH_EAST)) + (x[1:(- 1), :(- 2)] * NEIGH_MASK_WEST)) + (x[1:(- 1), 1:(- 1)] * NEIGH_MASK_CENTRE)) + (x[1:(- 1), 2:] * NEIGH_MASK_EAST)) + (x[2:, :(- 2)] * NEIGH_MASK_SOUTH_WEST)) + (x[2:, 1:(- 1)] * NEIGH_MASK_SOUTH)) + (x[2:, 2:] * NEIGH_MASK_SOUTH_EAST))
return lut_indices.astype(np.int32) | -7,441,921,039,338,985,000 | Convert a binary image to an index image that can be used with a lookup table
to perform morphological operations. Non-zero elements in the image are interpreted
as 1, zero elements as 0
:param x: a 2D NumPy array.
:return: a 2D NumPy array, same shape as x | Benchmarking/bsds500/bsds/thin.py | binary_image_to_lut_indices | CipiOrhei/eecvf | python | def binary_image_to_lut_indices(x):
'\n Convert a binary image to an index image that can be used with a lookup table\n to perform morphological operations. Non-zero elements in the image are interpreted\n as 1, zero elements as 0\n\n :param x: a 2D NumPy array.\n :return: a 2D NumPy array, same shape as x\n '
if (x.ndim != 2):
raise ValueError('x should have 2 dimensions, not {}'.format(x.ndim))
if (x.dtype != np.bool):
x = (x != 0)
x = np.pad(x, [(1, 1), (1, 1)], mode='constant')
lut_indices = (((((((((x[:(- 2), :(- 2)] * NEIGH_MASK_NORTH_WEST) + (x[:(- 2), 1:(- 1)] * NEIGH_MASK_NORTH)) + (x[:(- 2), 2:] * NEIGH_MASK_NORTH_EAST)) + (x[1:(- 1), :(- 2)] * NEIGH_MASK_WEST)) + (x[1:(- 1), 1:(- 1)] * NEIGH_MASK_CENTRE)) + (x[1:(- 1), 2:] * NEIGH_MASK_EAST)) + (x[2:, :(- 2)] * NEIGH_MASK_SOUTH_WEST)) + (x[2:, 1:(- 1)] * NEIGH_MASK_SOUTH)) + (x[2:, 2:] * NEIGH_MASK_SOUTH_EAST))
return lut_indices.astype(np.int32) |
def apply_lut(x, lut):
'\n Perform a morphological operation on the binary image x using the supplied lookup table\n :param x:\n :param lut:\n :return:\n '
if (lut.ndim != 1):
raise ValueError('lut should have 1 dimension, not {}'.format(lut.ndim))
if (lut.shape[0] != 512):
raise ValueError('lut should have 512 entries, not {}'.format(lut.shape[0]))
lut_indices = binary_image_to_lut_indices(x)
return lut[lut_indices] | -4,490,145,918,969,152,000 | Perform a morphological operation on the binary image x using the supplied lookup table
:param x:
:param lut:
:return: | Benchmarking/bsds500/bsds/thin.py | apply_lut | CipiOrhei/eecvf | python | def apply_lut(x, lut):
'\n Perform a morphological operation on the binary image x using the supplied lookup table\n :param x:\n :param lut:\n :return:\n '
if (lut.ndim != 1):
raise ValueError('lut should have 1 dimension, not {}'.format(lut.ndim))
if (lut.shape[0] != 512):
raise ValueError('lut should have 512 entries, not {}'.format(lut.shape[0]))
lut_indices = binary_image_to_lut_indices(x)
return lut[lut_indices] |
def identity_lut():
'\n Create identity lookup tablef\n :return:\n '
lut = np.zeros((512,), dtype=bool)
inds = np.arange(512)
lut[((inds & NEIGH_MASK_CENTRE) != 0)] = True
return lut | -3,448,551,723,326,318,600 | Create identity lookup tablef
:return: | Benchmarking/bsds500/bsds/thin.py | identity_lut | CipiOrhei/eecvf | python | def identity_lut():
'\n Create identity lookup tablef\n :return:\n '
lut = np.zeros((512,), dtype=bool)
inds = np.arange(512)
lut[((inds & NEIGH_MASK_CENTRE) != 0)] = True
return lut |
def _lut_mutate_mask(lut):
'\n Get a mask that shows which neighbourhood shapes result in changes to the image\n :param lut: lookup table\n :return: mask indicating which lookup indices result in changes\n '
return (lut != identity_lut()) | -1,491,527,051,737,313,000 | Get a mask that shows which neighbourhood shapes result in changes to the image
:param lut: lookup table
:return: mask indicating which lookup indices result in changes | Benchmarking/bsds500/bsds/thin.py | _lut_mutate_mask | CipiOrhei/eecvf | python | def _lut_mutate_mask(lut):
'\n Get a mask that shows which neighbourhood shapes result in changes to the image\n :param lut: lookup table\n :return: mask indicating which lookup indices result in changes\n '
return (lut != identity_lut()) |
def lut_masks_zero(neigh):
'\n Create a LUT index mask for which the specified neighbour is 0\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) == 0) | 7,111,937,062,312,660,000 | Create a LUT index mask for which the specified neighbour is 0
:param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | lut_masks_zero | CipiOrhei/eecvf | python | def lut_masks_zero(neigh):
'\n Create a LUT index mask for which the specified neighbour is 0\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) == 0) |
def lut_masks_one(neigh):
'\n Create a LUT index mask for which the specified neighbour is 1\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) != 0) | 6,568,589,080,645,123,000 | Create a LUT index mask for which the specified neighbour is 1
:param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | lut_masks_one | CipiOrhei/eecvf | python | def lut_masks_one(neigh):
'\n Create a LUT index mask for which the specified neighbour is 1\n :param neigh: neighbour index; counter-clockwise from 1 staring at the eastern neighbour\n :return: a LUT index mask\n '
if (neigh > 8):
neigh -= 8
return ((_LUT_INDS & MASKS[neigh]) != 0) |
def _thin_cond_g1():
'\n Thinning morphological operation; condition G1\n :return: a LUT index mask\n '
b = np.zeros(512, dtype=int)
for i in range(1, 5):
b += (lut_masks_zero(((2 * i) - 1)) & (lut_masks_one((2 * i)) | lut_masks_one(((2 * i) + 1))))
return (b == 1) | 7,932,152,981,081,950,000 | Thinning morphological operation; condition G1
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g1 | CipiOrhei/eecvf | python | def _thin_cond_g1():
'\n Thinning morphological operation; condition G1\n :return: a LUT index mask\n '
b = np.zeros(512, dtype=int)
for i in range(1, 5):
b += (lut_masks_zero(((2 * i) - 1)) & (lut_masks_one((2 * i)) | lut_masks_one(((2 * i) + 1))))
return (b == 1) |
def _thin_cond_g2():
'\n Thinning morphological operation; condition G2\n :return: a LUT index mask\n '
n1 = np.zeros(512, dtype=int)
n2 = np.zeros(512, dtype=int)
for k in range(1, 5):
n1 += (lut_masks_one(((2 * k) - 1)) | lut_masks_one((2 * k)))
n2 += (lut_masks_one((2 * k)) | lut_masks_one(((2 * k) + 1)))
m = np.minimum(n1, n2)
return ((m >= 2) & (m <= 3)) | 5,711,260,385,655,939,000 | Thinning morphological operation; condition G2
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g2 | CipiOrhei/eecvf | python | def _thin_cond_g2():
'\n Thinning morphological operation; condition G2\n :return: a LUT index mask\n '
n1 = np.zeros(512, dtype=int)
n2 = np.zeros(512, dtype=int)
for k in range(1, 5):
n1 += (lut_masks_one(((2 * k) - 1)) | lut_masks_one((2 * k)))
n2 += (lut_masks_one((2 * k)) | lut_masks_one(((2 * k) + 1)))
m = np.minimum(n1, n2)
return ((m >= 2) & (m <= 3)) |
def _thin_cond_g3():
'\n Thinning morphological operation; condition G3\n :return: a LUT index mask\n '
return ((((lut_masks_one(2) | lut_masks_one(3)) | lut_masks_zero(8)) & lut_masks_one(1)) == 0) | -1,797,199,284,587,221,000 | Thinning morphological operation; condition G3
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g3 | CipiOrhei/eecvf | python | def _thin_cond_g3():
'\n Thinning morphological operation; condition G3\n :return: a LUT index mask\n '
return ((((lut_masks_one(2) | lut_masks_one(3)) | lut_masks_zero(8)) & lut_masks_one(1)) == 0) |
def _thin_cond_g3_prime():
"\n Thinning morphological operation; condition G3'\n :return: a LUT index mask\n "
return ((((lut_masks_one(6) | lut_masks_one(7)) | lut_masks_zero(4)) & lut_masks_one(5)) == 0) | 7,209,364,479,417,253,000 | Thinning morphological operation; condition G3'
:return: a LUT index mask | Benchmarking/bsds500/bsds/thin.py | _thin_cond_g3_prime | CipiOrhei/eecvf | python | def _thin_cond_g3_prime():
"\n Thinning morphological operation; condition G3'\n :return: a LUT index mask\n "
return ((((lut_masks_one(6) | lut_masks_one(7)) | lut_masks_zero(4)) & lut_masks_one(5)) == 0) |
def _thin_iter_1_lut():
'\n Thinning morphological operation; lookup table for iteration 1\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3())
lut[cond] = False
return lut | 5,085,434,141,869,963,000 | Thinning morphological operation; lookup table for iteration 1
:return: lookup table | Benchmarking/bsds500/bsds/thin.py | _thin_iter_1_lut | CipiOrhei/eecvf | python | def _thin_iter_1_lut():
'\n Thinning morphological operation; lookup table for iteration 1\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3())
lut[cond] = False
return lut |
def _thin_iter_2_lut():
'\n Thinning morphological operation; lookup table for iteration 2\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3_prime())
lut[cond] = False
return lut | -103,154,475,881,035,140 | Thinning morphological operation; lookup table for iteration 2
:return: lookup table | Benchmarking/bsds500/bsds/thin.py | _thin_iter_2_lut | CipiOrhei/eecvf | python | def _thin_iter_2_lut():
'\n Thinning morphological operation; lookup table for iteration 2\n :return: lookup table\n '
lut = identity_lut()
cond = ((_thin_cond_g1() & _thin_cond_g2()) & _thin_cond_g3_prime())
lut[cond] = False
return lut |
def binary_thin(x, max_iter=None):
'\n Binary thinning morphological operation\n\n :param x: a binary image, or an image that is to be converted to a binary image\n :param max_iter: maximum number of iterations; default is `None` that results in an infinite\n number of iterations (note that `binary_thin` will automatically terminate when no more changes occur)\n :return:\n '
thin1 = _thin_iter_1_lut()
thin2 = _thin_iter_2_lut()
thin1_mut = _lut_mutate_mask(thin1)
thin2_mut = _lut_mutate_mask(thin2)
iter_count = 0
while ((max_iter is None) or (iter_count < max_iter)):
lut_indices = binary_image_to_lut_indices(x)
x_mut = thin1_mut[lut_indices]
if (x_mut.sum() == 0):
break
x = thin1[lut_indices]
lut_indices = binary_image_to_lut_indices(x)
x_mut = thin2_mut[lut_indices]
if (x_mut.sum() == 0):
break
x = thin2[lut_indices]
iter_count += 1
return x | 3,673,415,387,885,628,400 | Binary thinning morphological operation
:param x: a binary image, or an image that is to be converted to a binary image
:param max_iter: maximum number of iterations; default is `None` that results in an infinite
number of iterations (note that `binary_thin` will automatically terminate when no more changes occur)
:return: | Benchmarking/bsds500/bsds/thin.py | binary_thin | CipiOrhei/eecvf | python | def binary_thin(x, max_iter=None):
'\n Binary thinning morphological operation\n\n :param x: a binary image, or an image that is to be converted to a binary image\n :param max_iter: maximum number of iterations; default is `None` that results in an infinite\n number of iterations (note that `binary_thin` will automatically terminate when no more changes occur)\n :return:\n '
thin1 = _thin_iter_1_lut()
thin2 = _thin_iter_2_lut()
thin1_mut = _lut_mutate_mask(thin1)
thin2_mut = _lut_mutate_mask(thin2)
iter_count = 0
while ((max_iter is None) or (iter_count < max_iter)):
lut_indices = binary_image_to_lut_indices(x)
x_mut = thin1_mut[lut_indices]
if (x_mut.sum() == 0):
break
x = thin1[lut_indices]
lut_indices = binary_image_to_lut_indices(x)
x_mut = thin2_mut[lut_indices]
if (x_mut.sum() == 0):
break
x = thin2[lut_indices]
iter_count += 1
return x |
def play(self):
"\n # 1. Create a deck of 52 cards\n # 2. Shuffle the deck\n # 3. Ask the Player for their bet\n # 4. Make sure that the Player's bet does not exceed their available chips\n # 5. Deal two cards to the Dealer and two cards to the Player\n # 6. Show only one of the Dealer's cards, the other remains hidden\n # 7. Show both of the Player's cards\n # 8. Ask the Player if they wish to Hit, and take another card\n # 9. If the Player's hand doesn't Bust (go over 21), ask if they'd like to Hit again.\n # 10. If a Player Stands, play the Dealer's hand.\n # The dealer will always Hit until the Dealer's value meets or exceeds 17\n # 11. Determine the winner and adjust the Player's chips accordingly\n # 12. Ask the Player if they'd like to play again\n "
print('--NEW GAME---')
self.playing = True
self.deck.shuffle()
dealer_hand = Hand()
player_hand = Hand()
player_hand.add_card(self.deck.deal_card())
dealer_hand.add_card(self.deck.deal_card())
player_hand.add_card(self.deck.deal_card())
dealer_hand.add_card(self.deck.deal_card())
self.take_bet()
self.show_some(player_hand, dealer_hand)
while self.playing:
self.hit_or_stand(player_hand)
self.show_some(player_hand, dealer_hand)
if (player_hand.value > 21):
self.player_busts(player_hand, dealer_hand)
break
if (player_hand.value <= 21):
while (dealer_hand.value < 17):
self.hit(dealer_hand)
self.show_all_cards(player_hand, dealer_hand)
if (dealer_hand.value > 21):
self.dealer_busts(player_hand, dealer_hand)
elif (player_hand.value > dealer_hand.value):
self.player_wins(player_hand, dealer_hand)
elif (player_hand.value < dealer_hand.value):
self.dealer_wins(player_hand, dealer_hand)
else:
self.push(player_hand, dealer_hand) | 7,772,900,167,371,930,000 | # 1. Create a deck of 52 cards
# 2. Shuffle the deck
# 3. Ask the Player for their bet
# 4. Make sure that the Player's bet does not exceed their available chips
# 5. Deal two cards to the Dealer and two cards to the Player
# 6. Show only one of the Dealer's cards, the other remains hidden
# 7. Show both of the Player's cards
# 8. Ask the Player if they wish to Hit, and take another card
# 9. If the Player's hand doesn't Bust (go over 21), ask if they'd like to Hit again.
# 10. If a Player Stands, play the Dealer's hand.
# The dealer will always Hit until the Dealer's value meets or exceeds 17
# 11. Determine the winner and adjust the Player's chips accordingly
# 12. Ask the Player if they'd like to play again | BlackJack.py | play | tse4a/Python-Challenge | python | def play(self):
"\n # 1. Create a deck of 52 cards\n # 2. Shuffle the deck\n # 3. Ask the Player for their bet\n # 4. Make sure that the Player's bet does not exceed their available chips\n # 5. Deal two cards to the Dealer and two cards to the Player\n # 6. Show only one of the Dealer's cards, the other remains hidden\n # 7. Show both of the Player's cards\n # 8. Ask the Player if they wish to Hit, and take another card\n # 9. If the Player's hand doesn't Bust (go over 21), ask if they'd like to Hit again.\n # 10. If a Player Stands, play the Dealer's hand.\n # The dealer will always Hit until the Dealer's value meets or exceeds 17\n # 11. Determine the winner and adjust the Player's chips accordingly\n # 12. Ask the Player if they'd like to play again\n "
print('--NEW GAME---')
self.playing = True
self.deck.shuffle()
dealer_hand = Hand()
player_hand = Hand()
player_hand.add_card(self.deck.deal_card())
dealer_hand.add_card(self.deck.deal_card())
player_hand.add_card(self.deck.deal_card())
dealer_hand.add_card(self.deck.deal_card())
self.take_bet()
self.show_some(player_hand, dealer_hand)
while self.playing:
self.hit_or_stand(player_hand)
self.show_some(player_hand, dealer_hand)
if (player_hand.value > 21):
self.player_busts(player_hand, dealer_hand)
break
if (player_hand.value <= 21):
while (dealer_hand.value < 17):
self.hit(dealer_hand)
self.show_all_cards(player_hand, dealer_hand)
if (dealer_hand.value > 21):
self.dealer_busts(player_hand, dealer_hand)
elif (player_hand.value > dealer_hand.value):
self.player_wins(player_hand, dealer_hand)
elif (player_hand.value < dealer_hand.value):
self.dealer_wins(player_hand, dealer_hand)
else:
self.push(player_hand, dealer_hand) |
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, account_name: Optional[pulumi.Input[str]]=None, active_directories: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]]]=None, location: Optional[pulumi.Input[str]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, __props__=None, __name__=None, __opts__=None):
"\n NetApp account resource\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] account_name: The name of the NetApp account\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]] active_directories: Active Directories\n :param pulumi.Input[str] location: Resource location\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags\n "
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = _utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['account_name'] = account_name
__props__['active_directories'] = active_directories
__props__['location'] = location
if ((resource_group_name is None) and (not opts.urn)):
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['tags'] = tags
__props__['name'] = None
__props__['provisioning_state'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_='azure-nextgen:netapp/v20200901:Account'), pulumi.Alias(type_='azure-native:netapp:Account'), pulumi.Alias(type_='azure-nextgen:netapp:Account'), pulumi.Alias(type_='azure-native:netapp/latest:Account'), pulumi.Alias(type_='azure-nextgen:netapp/latest:Account'), pulumi.Alias(type_='azure-native:netapp/v20170815:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20170815:Account'), pulumi.Alias(type_='azure-native:netapp/v20190501:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190501:Account'), pulumi.Alias(type_='azure-native:netapp/v20190601:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190601:Account'), pulumi.Alias(type_='azure-native:netapp/v20190701:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190701:Account'), pulumi.Alias(type_='azure-native:netapp/v20190801:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190801:Account'), pulumi.Alias(type_='azure-native:netapp/v20191001:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20191001:Account'), pulumi.Alias(type_='azure-native:netapp/v20191101:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20191101:Account'), pulumi.Alias(type_='azure-native:netapp/v20200201:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200201:Account'), pulumi.Alias(type_='azure-native:netapp/v20200301:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200301:Account'), pulumi.Alias(type_='azure-native:netapp/v20200501:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200501:Account'), pulumi.Alias(type_='azure-native:netapp/v20200601:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200601:Account'), pulumi.Alias(type_='azure-native:netapp/v20200701:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200701:Account'), pulumi.Alias(type_='azure-native:netapp/v20200801:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200801:Account'), pulumi.Alias(type_='azure-native:netapp/v20201101:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20201101:Account')])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(Account, __self__).__init__('azure-native:netapp/v20200901:Account', resource_name, __props__, opts) | -3,839,363,611,189,158,000 | NetApp account resource
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] account_name: The name of the NetApp account
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]] active_directories: Active Directories
:param pulumi.Input[str] location: Resource location
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | __init__ | pulumi-bot/pulumi-azure-native | python | def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, account_name: Optional[pulumi.Input[str]]=None, active_directories: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]]]=None, location: Optional[pulumi.Input[str]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, __props__=None, __name__=None, __opts__=None):
"\n NetApp account resource\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] account_name: The name of the NetApp account\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ActiveDirectoryArgs']]]] active_directories: Active Directories\n :param pulumi.Input[str] location: Resource location\n :param pulumi.Input[str] resource_group_name: The name of the resource group.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags\n "
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = _utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['account_name'] = account_name
__props__['active_directories'] = active_directories
__props__['location'] = location
if ((resource_group_name is None) and (not opts.urn)):
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['tags'] = tags
__props__['name'] = None
__props__['provisioning_state'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_='azure-nextgen:netapp/v20200901:Account'), pulumi.Alias(type_='azure-native:netapp:Account'), pulumi.Alias(type_='azure-nextgen:netapp:Account'), pulumi.Alias(type_='azure-native:netapp/latest:Account'), pulumi.Alias(type_='azure-nextgen:netapp/latest:Account'), pulumi.Alias(type_='azure-native:netapp/v20170815:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20170815:Account'), pulumi.Alias(type_='azure-native:netapp/v20190501:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190501:Account'), pulumi.Alias(type_='azure-native:netapp/v20190601:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190601:Account'), pulumi.Alias(type_='azure-native:netapp/v20190701:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190701:Account'), pulumi.Alias(type_='azure-native:netapp/v20190801:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20190801:Account'), pulumi.Alias(type_='azure-native:netapp/v20191001:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20191001:Account'), pulumi.Alias(type_='azure-native:netapp/v20191101:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20191101:Account'), pulumi.Alias(type_='azure-native:netapp/v20200201:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200201:Account'), pulumi.Alias(type_='azure-native:netapp/v20200301:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200301:Account'), pulumi.Alias(type_='azure-native:netapp/v20200501:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200501:Account'), pulumi.Alias(type_='azure-native:netapp/v20200601:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200601:Account'), pulumi.Alias(type_='azure-native:netapp/v20200701:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200701:Account'), pulumi.Alias(type_='azure-native:netapp/v20200801:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20200801:Account'), pulumi.Alias(type_='azure-native:netapp/v20201101:Account'), pulumi.Alias(type_='azure-nextgen:netapp/v20201101:Account')])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(Account, __self__).__init__('azure-native:netapp/v20200901:Account', resource_name, __props__, opts) |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'Account':
"\n Get an existing Account resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['active_directories'] = None
__props__['location'] = None
__props__['name'] = None
__props__['provisioning_state'] = None
__props__['tags'] = None
__props__['type'] = None
return Account(resource_name, opts=opts, __props__=__props__) | 329,630,109,003,327,500 | Get an existing Account resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | get | pulumi-bot/pulumi-azure-native | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'Account':
"\n Get an existing Account resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['active_directories'] = None
__props__['location'] = None
__props__['name'] = None
__props__['provisioning_state'] = None
__props__['tags'] = None
__props__['type'] = None
return Account(resource_name, opts=opts, __props__=__props__) |
@property
@pulumi.getter(name='activeDirectories')
def active_directories(self) -> pulumi.Output[Optional[Sequence['outputs.ActiveDirectoryResponse']]]:
'\n Active Directories\n '
return pulumi.get(self, 'active_directories') | 6,275,772,879,752,033,000 | Active Directories | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | active_directories | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='activeDirectories')
def active_directories(self) -> pulumi.Output[Optional[Sequence['outputs.ActiveDirectoryResponse']]]:
'\n \n '
return pulumi.get(self, 'active_directories') |
@property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
'\n Resource location\n '
return pulumi.get(self, 'location') | 2,974,713,878,710,662,000 | Resource location | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | location | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'location') |
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n Resource name\n '
return pulumi.get(self, 'name') | 387,709,723,693,576,260 | Resource name | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | name | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name') |
@property
@pulumi.getter(name='provisioningState')
def provisioning_state(self) -> pulumi.Output[str]:
'\n Azure lifecycle management\n '
return pulumi.get(self, 'provisioning_state') | 5,814,604,552,307,744,000 | Azure lifecycle management | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | provisioning_state | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='provisioningState')
def provisioning_state(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'provisioning_state') |
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n Resource tags\n '
return pulumi.get(self, 'tags') | -1,239,552,863,427,208,400 | Resource tags | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | tags | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'tags') |
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n Resource type\n '
return pulumi.get(self, 'type') | 8,967,421,614,257,702,000 | Resource type | sdk/python/pulumi_azure_native/netapp/v20200901/account.py | type | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'type') |
def parse_input(input, inflv, starttime, endtime):
'Read simulations data from input file.\n\n Arguments:\n input -- prefix of file containing neutrino fluxes\n inflv -- neutrino flavor to consider\n starttime -- start time set by user via command line option (or None)\n endtime -- end time set by user via command line option (or None)\n '
f = h5py.File(input, 'r')
for (t, r) in f['sim_data']['shock_radius']:
if (r > 1):
tbounce = (t * 1000)
break
starttime = get_starttime(starttime, ((1000 * f['sim_data']['shock_radius'][0][0]) - tbounce))
endtime = get_endtime(endtime, ((1000 * f['sim_data']['shock_radius'][(- 1)][0]) - tbounce))
global flux
flux = {}
path = {'e': 'nue_data', 'eb': 'nuae_data', 'x': 'nux_data', 'xb': 'nux_data'}[inflv]
for (i, (t, lum)) in enumerate(f[path]['lum']):
t = ((1000 * t) - tbounce)
if ((t < (starttime - 30)) or (t > (endtime + 30))):
continue
lum *= (1e+51 * 624.151)
mean_e = f[path]['avg_energy'][i][1]
mean_e_sq = (f[path]['rms_energy'][i][1] ** 2)
flux[t] = (mean_e, mean_e_sq, lum)
f.close()
return (starttime, endtime, sorted(flux.keys())) | 6,570,633,104,090,349,000 | Read simulations data from input file.
Arguments:
input -- prefix of file containing neutrino fluxes
inflv -- neutrino flavor to consider
starttime -- start time set by user via command line option (or None)
endtime -- end time set by user via command line option (or None) | sntools/formats/warren2020.py | parse_input | arfon/sntools | python | def parse_input(input, inflv, starttime, endtime):
'Read simulations data from input file.\n\n Arguments:\n input -- prefix of file containing neutrino fluxes\n inflv -- neutrino flavor to consider\n starttime -- start time set by user via command line option (or None)\n endtime -- end time set by user via command line option (or None)\n '
f = h5py.File(input, 'r')
for (t, r) in f['sim_data']['shock_radius']:
if (r > 1):
tbounce = (t * 1000)
break
starttime = get_starttime(starttime, ((1000 * f['sim_data']['shock_radius'][0][0]) - tbounce))
endtime = get_endtime(endtime, ((1000 * f['sim_data']['shock_radius'][(- 1)][0]) - tbounce))
global flux
flux = {}
path = {'e': 'nue_data', 'eb': 'nuae_data', 'x': 'nux_data', 'xb': 'nux_data'}[inflv]
for (i, (t, lum)) in enumerate(f[path]['lum']):
t = ((1000 * t) - tbounce)
if ((t < (starttime - 30)) or (t > (endtime + 30))):
continue
lum *= (1e+51 * 624.151)
mean_e = f[path]['avg_energy'][i][1]
mean_e_sq = (f[path]['rms_energy'][i][1] ** 2)
flux[t] = (mean_e, mean_e_sq, lum)
f.close()
return (starttime, endtime, sorted(flux.keys())) |
def testEzsignformfieldResponseCompound(self):
'Test EzsignformfieldResponseCompound'
pass | -4,861,070,669,607,094,000 | Test EzsignformfieldResponseCompound | test/test_ezsignformfield_response_compound.py | testEzsignformfieldResponseCompound | eZmaxinc/eZmax-SDK-python | python | def testEzsignformfieldResponseCompound(self):
pass |
@patch('regulations.apps.get_app_template_dirs')
def test_precompute_custom_templates(self, get_app_template_dirs):
'Verify that custom templates are found'
get_app_template_dirs.return_value = [self.tmpdir]
open(os.path.join(self.tmpdir, '123-45-a.html'), 'w').close()
open(os.path.join(self.tmpdir, 'other.html'), 'w').close()
RegulationsConfig.precompute_custom_templates()
self.assertEqual(RegulationsConfig.custom_tpls['123-45-a'], 'regulations/custom_nodes/123-45-a.html')
self.assertEqual(RegulationsConfig.custom_tpls['other'], 'regulations/custom_nodes/other.html')
self.assertFalse(('another' in RegulationsConfig.custom_tpls)) | -4,249,644,129,594,510,300 | Verify that custom templates are found | regulations/tests/apps_tests.py | test_precompute_custom_templates | CMSgov/cmcs-eregulations | python | @patch('regulations.apps.get_app_template_dirs')
def test_precompute_custom_templates(self, get_app_template_dirs):
get_app_template_dirs.return_value = [self.tmpdir]
open(os.path.join(self.tmpdir, '123-45-a.html'), 'w').close()
open(os.path.join(self.tmpdir, 'other.html'), 'w').close()
RegulationsConfig.precompute_custom_templates()
self.assertEqual(RegulationsConfig.custom_tpls['123-45-a'], 'regulations/custom_nodes/123-45-a.html')
self.assertEqual(RegulationsConfig.custom_tpls['other'], 'regulations/custom_nodes/other.html')
self.assertFalse(('another' in RegulationsConfig.custom_tpls)) |
def uvc_return_mapping(x_sol, data, tol=1e-08, maximum_iterations=1000):
" Implements the time integration of the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton tolerance.\n :param int maximum_iterations: maximum iterations in local Newton procedure, raises RuntimeError if exceeded.\n :return dict: History of: stress ('stress'), strain ('strain'), the total error ('error') calculated by the\n updated Voce-Chaboche model, number of iterations for convergence at each increment ('num_its').\n "
if (len(x_sol) < 8):
raise RuntimeError('No backstresses or using original V-C params.')
n_param_per_back = 2
n_basic_param = 6
E = (x_sol[0] * 1.0)
sy_0 = (x_sol[1] * 1.0)
Q = (x_sol[2] * 1.0)
b = (x_sol[3] * 1.0)
D = (x_sol[4] * 1.0)
a = (x_sol[5] * 1.0)
n_backstresses = int(((len(x_sol) - n_basic_param) / n_param_per_back))
c_k = []
gamma_k = []
for i in range(0, n_backstresses):
c_k.append(x_sol[(n_basic_param + (n_param_per_back * i))])
gamma_k.append(x_sol[((n_basic_param + 1) + (n_param_per_back * i))])
alpha_components = np.zeros(n_backstresses, dtype=object)
strain = 0.0
stress = 0.0
ep_eq = 0.0
error = 0.0
sum_abs_de = 0.0
stress_sim = 0.0
stress_test = 0.0
area_test = 0.0
stress_track = []
strain_track = []
strain_inc_track = []
iteration_track = []
loading = np.diff(data['e_true'])
for (increment_number, strain_inc) in enumerate(loading):
strain += strain_inc
alpha = np.sum(alpha_components)
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
trial_stress = (stress + (E * strain_inc))
relative_stress = (trial_stress - alpha)
flow_dir = np.sign(relative_stress)
yield_condition = (np.abs(relative_stress) - yield_stress)
if (yield_condition > tol):
is_converged = False
else:
is_converged = True
stress_sim_1 = (stress_sim * 1.0)
stress_test_1 = (stress_test * 1.0)
ep_eq_init = ep_eq
alpha_init = alpha
consist_param = 0.0
number_of_iterations = 0
while ((is_converged is False) and (number_of_iterations < maximum_iterations)):
number_of_iterations += 1
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
iso_modulus = (((Q * b) * np.exp(((- b) * ep_eq))) - ((D * a) * np.exp(((- a) * ep_eq))))
alpha = 0.0
kin_modulus = 0.0
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha += (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
kin_modulus += ((c_k[i] * e_k) - (((flow_dir * gamma_k[i]) * e_k) * alpha_components[i]))
delta_alpha = (alpha - alpha_init)
numerator = (np.abs(relative_stress) - (((consist_param * E) + yield_stress) + (flow_dir * delta_alpha)))
denominator = (- ((E + iso_modulus) + kin_modulus))
consist_param = (consist_param - (numerator / denominator))
ep_eq = (ep_eq_init + consist_param)
if (np.abs(numerator) < tol):
is_converged = True
stress = (trial_stress - ((E * flow_dir) * consist_param))
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha_components[i] = (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
stress_track.append(stress)
strain_track.append(strain)
strain_inc_track.append(strain_inc)
iteration_track.append(number_of_iterations)
stress_sim = (stress * 1.0)
stress_test = data['Sigma_true'].iloc[(increment_number + 1)]
sum_abs_de += np.abs(strain_inc)
area_test += ((np.abs(strain_inc) * ((stress_test ** 2) + (stress_test_1 ** 2))) / 2.0)
error += ((np.abs(strain_inc) * (((stress_sim - stress_test) ** 2) + ((stress_sim_1 - stress_test_1) ** 2))) / 2.0)
if (number_of_iterations >= maximum_iterations):
print('Increment number = ', increment_number)
print('Parameters = ', x_sol)
print('Numerator = ', numerator)
raise RuntimeError((('Return mapping did not converge in ' + str(maximum_iterations)) + ' iterations.'))
area = (area_test / sum_abs_de)
error = (error / sum_abs_de)
return {'stress': stress_track, 'strain': strain_track, 'error': error, 'num_its': iteration_track, 'area': area} | -8,363,361,874,546,954,000 | Implements the time integration of the updated Voce-Chaboche material model.
:param np.array x_sol: Updated Voce-Chaboche model parameters.
:param pd.DataFrame data: stress-strain data.
:param float tol: Local Newton tolerance.
:param int maximum_iterations: maximum iterations in local Newton procedure, raises RuntimeError if exceeded.
:return dict: History of: stress ('stress'), strain ('strain'), the total error ('error') calculated by the
updated Voce-Chaboche model, number of iterations for convergence at each increment ('num_its'). | RESSPyLab/uvc_model.py | uvc_return_mapping | AlbanoCastroSousa/RESSPyLab | python | def uvc_return_mapping(x_sol, data, tol=1e-08, maximum_iterations=1000):
" Implements the time integration of the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton tolerance.\n :param int maximum_iterations: maximum iterations in local Newton procedure, raises RuntimeError if exceeded.\n :return dict: History of: stress ('stress'), strain ('strain'), the total error ('error') calculated by the\n updated Voce-Chaboche model, number of iterations for convergence at each increment ('num_its').\n "
if (len(x_sol) < 8):
raise RuntimeError('No backstresses or using original V-C params.')
n_param_per_back = 2
n_basic_param = 6
E = (x_sol[0] * 1.0)
sy_0 = (x_sol[1] * 1.0)
Q = (x_sol[2] * 1.0)
b = (x_sol[3] * 1.0)
D = (x_sol[4] * 1.0)
a = (x_sol[5] * 1.0)
n_backstresses = int(((len(x_sol) - n_basic_param) / n_param_per_back))
c_k = []
gamma_k = []
for i in range(0, n_backstresses):
c_k.append(x_sol[(n_basic_param + (n_param_per_back * i))])
gamma_k.append(x_sol[((n_basic_param + 1) + (n_param_per_back * i))])
alpha_components = np.zeros(n_backstresses, dtype=object)
strain = 0.0
stress = 0.0
ep_eq = 0.0
error = 0.0
sum_abs_de = 0.0
stress_sim = 0.0
stress_test = 0.0
area_test = 0.0
stress_track = []
strain_track = []
strain_inc_track = []
iteration_track = []
loading = np.diff(data['e_true'])
for (increment_number, strain_inc) in enumerate(loading):
strain += strain_inc
alpha = np.sum(alpha_components)
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
trial_stress = (stress + (E * strain_inc))
relative_stress = (trial_stress - alpha)
flow_dir = np.sign(relative_stress)
yield_condition = (np.abs(relative_stress) - yield_stress)
if (yield_condition > tol):
is_converged = False
else:
is_converged = True
stress_sim_1 = (stress_sim * 1.0)
stress_test_1 = (stress_test * 1.0)
ep_eq_init = ep_eq
alpha_init = alpha
consist_param = 0.0
number_of_iterations = 0
while ((is_converged is False) and (number_of_iterations < maximum_iterations)):
number_of_iterations += 1
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
iso_modulus = (((Q * b) * np.exp(((- b) * ep_eq))) - ((D * a) * np.exp(((- a) * ep_eq))))
alpha = 0.0
kin_modulus = 0.0
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha += (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
kin_modulus += ((c_k[i] * e_k) - (((flow_dir * gamma_k[i]) * e_k) * alpha_components[i]))
delta_alpha = (alpha - alpha_init)
numerator = (np.abs(relative_stress) - (((consist_param * E) + yield_stress) + (flow_dir * delta_alpha)))
denominator = (- ((E + iso_modulus) + kin_modulus))
consist_param = (consist_param - (numerator / denominator))
ep_eq = (ep_eq_init + consist_param)
if (np.abs(numerator) < tol):
is_converged = True
stress = (trial_stress - ((E * flow_dir) * consist_param))
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha_components[i] = (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
stress_track.append(stress)
strain_track.append(strain)
strain_inc_track.append(strain_inc)
iteration_track.append(number_of_iterations)
stress_sim = (stress * 1.0)
stress_test = data['Sigma_true'].iloc[(increment_number + 1)]
sum_abs_de += np.abs(strain_inc)
area_test += ((np.abs(strain_inc) * ((stress_test ** 2) + (stress_test_1 ** 2))) / 2.0)
error += ((np.abs(strain_inc) * (((stress_sim - stress_test) ** 2) + ((stress_sim_1 - stress_test_1) ** 2))) / 2.0)
if (number_of_iterations >= maximum_iterations):
print('Increment number = ', increment_number)
print('Parameters = ', x_sol)
print('Numerator = ', numerator)
raise RuntimeError((('Return mapping did not converge in ' + str(maximum_iterations)) + ' iterations.'))
area = (area_test / sum_abs_de)
error = (error / sum_abs_de)
return {'stress': stress_track, 'strain': strain_track, 'error': error, 'num_its': iteration_track, 'area': area} |
def sim_curve_uvc(x_sol, test_clean):
' Returns the stress-strain approximation of the updated Voce-Chaboche material model to a given strain input.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return DataFrame: Voce-Chaboche approximation\n\n The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true".\n '
model_output = uvc_return_mapping(x_sol, test_clean)
strain = np.append([0.0], model_output['strain'])
stress = np.append([0.0], model_output['stress'])
sim_curve = pd.DataFrame(np.array([strain, stress]).transpose(), columns=['e_true', 'Sigma_true'])
return sim_curve | 3,410,126,839,265,906,700 | Returns the stress-strain approximation of the updated Voce-Chaboche material model to a given strain input.
:param np.array x_sol: Voce-Chaboche model parameters
:param DataFrame test_clean: stress-strain data
:return DataFrame: Voce-Chaboche approximation
The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true". | RESSPyLab/uvc_model.py | sim_curve_uvc | AlbanoCastroSousa/RESSPyLab | python | def sim_curve_uvc(x_sol, test_clean):
' Returns the stress-strain approximation of the updated Voce-Chaboche material model to a given strain input.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return DataFrame: Voce-Chaboche approximation\n\n The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true".\n '
model_output = uvc_return_mapping(x_sol, test_clean)
strain = np.append([0.0], model_output['strain'])
stress = np.append([0.0], model_output['stress'])
sim_curve = pd.DataFrame(np.array([strain, stress]).transpose(), columns=['e_true', 'Sigma_true'])
return sim_curve |
def error_single_test_uvc(x_sol, test_clean):
' Returns the relative error between a test and its approximation using the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return float: relative error\n\n The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true".\n '
model_output = uvc_return_mapping(x_sol, test_clean)
return model_output['error'] | -6,505,289,781,695,587,000 | Returns the relative error between a test and its approximation using the updated Voce-Chaboche material model.
:param np.array x_sol: Voce-Chaboche model parameters
:param DataFrame test_clean: stress-strain data
:return float: relative error
The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true". | RESSPyLab/uvc_model.py | error_single_test_uvc | AlbanoCastroSousa/RESSPyLab | python | def error_single_test_uvc(x_sol, test_clean):
' Returns the relative error between a test and its approximation using the updated Voce-Chaboche material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return float: relative error\n\n The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true".\n '
model_output = uvc_return_mapping(x_sol, test_clean)
return model_output['error'] |
def normalized_error_single_test_uvc(x_sol, test_clean):
' Returns the error and the total area of a test and its approximation using the updated Voce-Chaboche\n material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return list: (float) total error, (float) total area\n\n The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true".\n '
model_output = uvc_return_mapping(x_sol, test_clean)
return [model_output['error'], model_output['area']] | 1,769,212,009,327,486,500 | Returns the error and the total area of a test and its approximation using the updated Voce-Chaboche
material model.
:param np.array x_sol: Voce-Chaboche model parameters
:param DataFrame test_clean: stress-strain data
:return list: (float) total error, (float) total area
The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true". | RESSPyLab/uvc_model.py | normalized_error_single_test_uvc | AlbanoCastroSousa/RESSPyLab | python | def normalized_error_single_test_uvc(x_sol, test_clean):
' Returns the error and the total area of a test and its approximation using the updated Voce-Chaboche\n material model.\n\n :param np.array x_sol: Voce-Chaboche model parameters\n :param DataFrame test_clean: stress-strain data\n :return list: (float) total error, (float) total area\n\n The strain column in the DataFrame is labeled "e_true" and the stress column is labeled "Sigma_true".\n '
model_output = uvc_return_mapping(x_sol, test_clean)
return [model_output['error'], model_output['area']] |
def calc_phi_total(x, data):
' Returns the sum of the normalized relative error of the updated Voce-Chaboche material model given x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Normalized error value expressed as a percent (raw value * 100).\n\n The normalized error is defined in de Sousa and Lignos (2017).\n '
error_total = 0.0
area_total = 0.0
for d in data:
(error, area) = normalized_error_single_test_uvc(x, d)
error_total += error
area_total += area
return (np.sqrt((error_total / area_total)) * 100.0) | -7,501,822,167,166,433,000 | Returns the sum of the normalized relative error of the updated Voce-Chaboche material model given x.
:param np.array x: Updated Voce-Chaboche material model parameters.
:param list data: (pd.DataFrame) Stress-strain history for each test considered.
:return float: Normalized error value expressed as a percent (raw value * 100).
The normalized error is defined in de Sousa and Lignos (2017). | RESSPyLab/uvc_model.py | calc_phi_total | AlbanoCastroSousa/RESSPyLab | python | def calc_phi_total(x, data):
' Returns the sum of the normalized relative error of the updated Voce-Chaboche material model given x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Normalized error value expressed as a percent (raw value * 100).\n\n The normalized error is defined in de Sousa and Lignos (2017).\n '
error_total = 0.0
area_total = 0.0
for d in data:
(error, area) = normalized_error_single_test_uvc(x, d)
error_total += error
area_total += area
return (np.sqrt((error_total / area_total)) * 100.0) |
def test_total_area(x, data):
' Returns the total squared area underneath all the tests.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Total squared area.\n '
area_total = 0.0
for d in data:
(_, area) = normalized_error_single_test_uvc(x, d)
area_total += area
return area_total | -5,041,924,756,357,932,000 | Returns the total squared area underneath all the tests.
:param np.array x: Updated Voce-Chaboche material model parameters.
:param list data: (pd.DataFrame) Stress-strain history for each test considered.
:return float: Total squared area. | RESSPyLab/uvc_model.py | test_total_area | AlbanoCastroSousa/RESSPyLab | python | def test_total_area(x, data):
' Returns the total squared area underneath all the tests.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Total squared area.\n '
area_total = 0.0
for d in data:
(_, area) = normalized_error_single_test_uvc(x, d)
area_total += area
return area_total |
def uvc_get_hessian(x, data):
' Returns the Hessian of the material model error function for a given set of test data evaluated at x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return np.array: Hessian matrix of the error function.\n '
def f(xi):
val = 0.0
for d in data:
val += error_single_test_uvc(xi, d)
return val
hess_fun = nda.Hessian(f)
return hess_fun(x) | -5,182,262,053,579,384,000 | Returns the Hessian of the material model error function for a given set of test data evaluated at x.
:param np.array x: Updated Voce-Chaboche material model parameters.
:param list data: (pd.DataFrame) Stress-strain history for each test considered.
:return np.array: Hessian matrix of the error function. | RESSPyLab/uvc_model.py | uvc_get_hessian | AlbanoCastroSousa/RESSPyLab | python | def uvc_get_hessian(x, data):
' Returns the Hessian of the material model error function for a given set of test data evaluated at x.\n\n :param np.array x: Updated Voce-Chaboche material model parameters.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return np.array: Hessian matrix of the error function.\n '
def f(xi):
val = 0.0
for d in data:
val += error_single_test_uvc(xi, d)
return val
hess_fun = nda.Hessian(f)
return hess_fun(x) |
def uvc_consistency_metric(x_base, x_sample, data):
' Returns the xi_2 consistency metric from de Sousa and Lignos 2019 using the updated Voce-Chaboche model.\n\n :param np.array x_base: Updated Voce-Chaboche material model parameters from the base case.\n :param np.array x_sample: Updated Voce-Chaboche material model parameters from the sample case.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Increase in quadratic approximation from the base to the sample case.\n '
x_diff = (x_sample - x_base)
hess_base = uvc_get_hessian(x_base, data)
numerator = np.dot(x_diff, hess_base.dot(x_diff))
denominator = test_total_area(x_base, data)
return np.sqrt((numerator / denominator)) | 7,123,153,927,627,399,000 | Returns the xi_2 consistency metric from de Sousa and Lignos 2019 using the updated Voce-Chaboche model.
:param np.array x_base: Updated Voce-Chaboche material model parameters from the base case.
:param np.array x_sample: Updated Voce-Chaboche material model parameters from the sample case.
:param list data: (pd.DataFrame) Stress-strain history for each test considered.
:return float: Increase in quadratic approximation from the base to the sample case. | RESSPyLab/uvc_model.py | uvc_consistency_metric | AlbanoCastroSousa/RESSPyLab | python | def uvc_consistency_metric(x_base, x_sample, data):
' Returns the xi_2 consistency metric from de Sousa and Lignos 2019 using the updated Voce-Chaboche model.\n\n :param np.array x_base: Updated Voce-Chaboche material model parameters from the base case.\n :param np.array x_sample: Updated Voce-Chaboche material model parameters from the sample case.\n :param list data: (pd.DataFrame) Stress-strain history for each test considered.\n :return float: Increase in quadratic approximation from the base to the sample case.\n '
x_diff = (x_sample - x_base)
hess_base = uvc_get_hessian(x_base, data)
numerator = np.dot(x_diff, hess_base.dot(x_diff))
denominator = test_total_area(x_base, data)
return np.sqrt((numerator / denominator)) |
def uvc_tangent_modulus(x_sol, data, tol=1e-08, maximum_iterations=1000):
' Returns the tangent modulus at each strain step.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton tolerance.\n :param int maximum_iterations: maximum iterations in local Newton procedure, raises RuntimeError if exceeded.\n :return np.ndarray: Tangent modulus array.\n '
if (len(x_sol) < 8):
raise RuntimeError('No backstresses or using original V-C params.')
n_param_per_back = 2
n_basic_param = 6
E = (x_sol[0] * 1.0)
sy_0 = (x_sol[1] * 1.0)
Q = (x_sol[2] * 1.0)
b = (x_sol[3] * 1.0)
D = (x_sol[4] * 1.0)
a = (x_sol[5] * 1.0)
n_backstresses = int(((len(x_sol) - n_basic_param) / n_param_per_back))
c_k = []
gamma_k = []
for i in range(0, n_backstresses):
c_k.append(x_sol[(n_basic_param + (n_param_per_back * i))])
gamma_k.append(x_sol[((n_basic_param + 1) + (n_param_per_back * i))])
alpha_components = np.zeros(n_backstresses, dtype=object)
strain = 0.0
stress = 0.0
ep_eq = 0.0
stress_track = []
strain_track = []
strain_inc_track = []
iteration_track = []
tangent_track = []
loading = np.diff(data['e_true'])
for (increment_number, strain_inc) in enumerate(loading):
strain += strain_inc
alpha = np.sum(alpha_components)
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
trial_stress = (stress + (E * strain_inc))
relative_stress = (trial_stress - alpha)
flow_dir = np.sign(relative_stress)
yield_condition = (np.abs(relative_stress) - yield_stress)
if (yield_condition > tol):
is_converged = False
else:
is_converged = True
ep_eq_init = ep_eq
alpha_init = alpha
consist_param = 0.0
number_of_iterations = 0
while ((is_converged is False) and (number_of_iterations < maximum_iterations)):
number_of_iterations += 1
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
iso_modulus = (((Q * b) * np.exp(((- b) * ep_eq))) - ((D * a) * np.exp(((- a) * ep_eq))))
alpha = 0.0
kin_modulus = 0.0
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha += (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
kin_modulus += ((c_k[i] * e_k) - (((flow_dir * gamma_k[i]) * e_k) * alpha_components[i]))
delta_alpha = (alpha - alpha_init)
numerator = (np.abs(relative_stress) - (((consist_param * E) + yield_stress) + (flow_dir * delta_alpha)))
denominator = (- ((E + iso_modulus) + kin_modulus))
consist_param = (consist_param - (numerator / denominator))
ep_eq = (ep_eq_init + consist_param)
if (np.abs(numerator) < tol):
is_converged = True
stress = (trial_stress - ((E * flow_dir) * consist_param))
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha_components[i] = (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
stress_track.append(stress)
strain_track.append(strain)
strain_inc_track.append(strain_inc)
iteration_track.append(number_of_iterations)
if (number_of_iterations > 0):
h_prime = 0.0
for i in range(0, n_backstresses):
h_prime += (c_k[i] - ((flow_dir * gamma_k[i]) * alpha_components[i]))
k_prime = (((Q * b) * np.exp(((- b) * ep_eq))) - ((D * a) * np.exp(((- a) * ep_eq))))
tangent_track.append(((E * (k_prime + h_prime)) / ((E + k_prime) + h_prime)))
else:
tangent_track.append(E)
return np.append([0.0], np.array(tangent_track)) | 5,687,772,783,232,525,000 | Returns the tangent modulus at each strain step.
:param np.array x_sol: Updated Voce-Chaboche model parameters.
:param pd.DataFrame data: stress-strain data.
:param float tol: Local Newton tolerance.
:param int maximum_iterations: maximum iterations in local Newton procedure, raises RuntimeError if exceeded.
:return np.ndarray: Tangent modulus array. | RESSPyLab/uvc_model.py | uvc_tangent_modulus | AlbanoCastroSousa/RESSPyLab | python | def uvc_tangent_modulus(x_sol, data, tol=1e-08, maximum_iterations=1000):
' Returns the tangent modulus at each strain step.\n\n :param np.array x_sol: Updated Voce-Chaboche model parameters.\n :param pd.DataFrame data: stress-strain data.\n :param float tol: Local Newton tolerance.\n :param int maximum_iterations: maximum iterations in local Newton procedure, raises RuntimeError if exceeded.\n :return np.ndarray: Tangent modulus array.\n '
if (len(x_sol) < 8):
raise RuntimeError('No backstresses or using original V-C params.')
n_param_per_back = 2
n_basic_param = 6
E = (x_sol[0] * 1.0)
sy_0 = (x_sol[1] * 1.0)
Q = (x_sol[2] * 1.0)
b = (x_sol[3] * 1.0)
D = (x_sol[4] * 1.0)
a = (x_sol[5] * 1.0)
n_backstresses = int(((len(x_sol) - n_basic_param) / n_param_per_back))
c_k = []
gamma_k = []
for i in range(0, n_backstresses):
c_k.append(x_sol[(n_basic_param + (n_param_per_back * i))])
gamma_k.append(x_sol[((n_basic_param + 1) + (n_param_per_back * i))])
alpha_components = np.zeros(n_backstresses, dtype=object)
strain = 0.0
stress = 0.0
ep_eq = 0.0
stress_track = []
strain_track = []
strain_inc_track = []
iteration_track = []
tangent_track = []
loading = np.diff(data['e_true'])
for (increment_number, strain_inc) in enumerate(loading):
strain += strain_inc
alpha = np.sum(alpha_components)
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
trial_stress = (stress + (E * strain_inc))
relative_stress = (trial_stress - alpha)
flow_dir = np.sign(relative_stress)
yield_condition = (np.abs(relative_stress) - yield_stress)
if (yield_condition > tol):
is_converged = False
else:
is_converged = True
ep_eq_init = ep_eq
alpha_init = alpha
consist_param = 0.0
number_of_iterations = 0
while ((is_converged is False) and (number_of_iterations < maximum_iterations)):
number_of_iterations += 1
yield_stress = ((sy_0 + (Q * (1.0 - np.exp(((- b) * ep_eq))))) - (D * (1.0 - np.exp(((- a) * ep_eq)))))
iso_modulus = (((Q * b) * np.exp(((- b) * ep_eq))) - ((D * a) * np.exp(((- a) * ep_eq))))
alpha = 0.0
kin_modulus = 0.0
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha += (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
kin_modulus += ((c_k[i] * e_k) - (((flow_dir * gamma_k[i]) * e_k) * alpha_components[i]))
delta_alpha = (alpha - alpha_init)
numerator = (np.abs(relative_stress) - (((consist_param * E) + yield_stress) + (flow_dir * delta_alpha)))
denominator = (- ((E + iso_modulus) + kin_modulus))
consist_param = (consist_param - (numerator / denominator))
ep_eq = (ep_eq_init + consist_param)
if (np.abs(numerator) < tol):
is_converged = True
stress = (trial_stress - ((E * flow_dir) * consist_param))
for i in range(0, n_backstresses):
e_k = np.exp(((- gamma_k[i]) * (ep_eq - ep_eq_init)))
alpha_components[i] = (((flow_dir * c_k[i]) / gamma_k[i]) + ((alpha_components[i] - ((flow_dir * c_k[i]) / gamma_k[i])) * e_k))
stress_track.append(stress)
strain_track.append(strain)
strain_inc_track.append(strain_inc)
iteration_track.append(number_of_iterations)
if (number_of_iterations > 0):
h_prime = 0.0
for i in range(0, n_backstresses):
h_prime += (c_k[i] - ((flow_dir * gamma_k[i]) * alpha_components[i]))
k_prime = (((Q * b) * np.exp(((- b) * ep_eq))) - ((D * a) * np.exp(((- a) * ep_eq))))
tangent_track.append(((E * (k_prime + h_prime)) / ((E + k_prime) + h_prime)))
else:
tangent_track.append(E)
return np.append([0.0], np.array(tangent_track)) |
def get_wiki_references(url, outfile=None):
'get_wiki_references.\n Extracts references from predefined sections of wiki page\n Uses `urlscan`, `refextract`, `doi`, `wikipedia`, and `re` (for ArXiv URLs)\n\n :param url: URL of wiki article to scrape\n :param outfile: File to write extracted references to\n '
def _check(l):
return (((not l['doi']) or (l['doi'] == l['refs'][(- 1)]['doi'])) and ((not l['arxiv']) or (l['arxiv'] == l['refs'][(- 1)]['arxiv'])))
page = wiki.page(get_wiki_page_id(url))
sections = get_wiki_sections(page.content)
lines = sum([get_wiki_lines(s, predicate=any) for s in sections.values()], [])
links = sum([wikiparse.parse(s).external_links for s in sections.values()], [])
summary = sum([[{'raw': l, 'links': urlscan.parse_text_urls(l), 'refs': refextract.extract_references_from_string(l), 'doi': doi.find_doi_in_text(l), 'arxiv': (m.group(1) if ((m := arxiv_url_regex.matches(l)) is not None) else None)} for l in get_wiki_lines(s, predicate=any)] for s in sections.values()])
failed = [ld for ld in summary if (not _check(ld))]
if any(failed):
logger.warning('Consistency check failed for the following lines: {}'.format(failed))
return _serialize(summary, outfile) | 1,990,428,418,421,912,600 | get_wiki_references.
Extracts references from predefined sections of wiki page
Uses `urlscan`, `refextract`, `doi`, `wikipedia`, and `re` (for ArXiv URLs)
:param url: URL of wiki article to scrape
:param outfile: File to write extracted references to | scraper/apis/wikipedia.py | get_wiki_references | antimike/citation-scraper | python | def get_wiki_references(url, outfile=None):
'get_wiki_references.\n Extracts references from predefined sections of wiki page\n Uses `urlscan`, `refextract`, `doi`, `wikipedia`, and `re` (for ArXiv URLs)\n\n :param url: URL of wiki article to scrape\n :param outfile: File to write extracted references to\n '
def _check(l):
return (((not l['doi']) or (l['doi'] == l['refs'][(- 1)]['doi'])) and ((not l['arxiv']) or (l['arxiv'] == l['refs'][(- 1)]['arxiv'])))
page = wiki.page(get_wiki_page_id(url))
sections = get_wiki_sections(page.content)
lines = sum([get_wiki_lines(s, predicate=any) for s in sections.values()], [])
links = sum([wikiparse.parse(s).external_links for s in sections.values()], [])
summary = sum([[{'raw': l, 'links': urlscan.parse_text_urls(l), 'refs': refextract.extract_references_from_string(l), 'doi': doi.find_doi_in_text(l), 'arxiv': (m.group(1) if ((m := arxiv_url_regex.matches(l)) is not None) else None)} for l in get_wiki_lines(s, predicate=any)] for s in sections.values()])
failed = [ld for ld in summary if (not _check(ld))]
if any(failed):
logger.warning('Consistency check failed for the following lines: {}'.format(failed))
return _serialize(summary, outfile) |
def asm_and_link_one_file(asm_path: str, work_dir: str) -> str:
'Assemble and link file at asm_path in work_dir.\n\n Returns the path to the resulting ELF\n\n '
otbn_as = os.path.join(UTIL_DIR, 'otbn-as')
otbn_ld = os.path.join(UTIL_DIR, 'otbn-ld')
obj_path = os.path.join(work_dir, 'tst.o')
elf_path = os.path.join(work_dir, 'tst')
subprocess.run([otbn_as, '-o', obj_path, asm_path], check=True)
subprocess.run([otbn_ld, '-o', elf_path, obj_path], check=True)
return elf_path | -372,252,728,031,894,140 | Assemble and link file at asm_path in work_dir.
Returns the path to the resulting ELF | hw/ip/otbn/dv/otbnsim/test/testutil.py | asm_and_link_one_file | OneToughMonkey/opentitan | python | def asm_and_link_one_file(asm_path: str, work_dir: str) -> str:
'Assemble and link file at asm_path in work_dir.\n\n Returns the path to the resulting ELF\n\n '
otbn_as = os.path.join(UTIL_DIR, 'otbn-as')
otbn_ld = os.path.join(UTIL_DIR, 'otbn-ld')
obj_path = os.path.join(work_dir, 'tst.o')
elf_path = os.path.join(work_dir, 'tst')
subprocess.run([otbn_as, '-o', obj_path, asm_path], check=True)
subprocess.run([otbn_ld, '-o', elf_path, obj_path], check=True)
return elf_path |
def find_two_smallest(L: List[float]) -> Tuple[(int, int)]:
' (see above) '
smallest = min(L)
min1 = L.index(smallest)
L.remove(smallest)
next_smallest = min(L)
min2 = L.index(next_smallest)
L.insert(min1, smallest)
if (min1 <= min2):
min2 += 1
return (min1, min2) | -1,861,280,632,368,825,900 | (see above) | chapter12/examples/example02.py | find_two_smallest | YordanIH/Intro_to_CS_w_Python | python | def find_two_smallest(L: List[float]) -> Tuple[(int, int)]:
' '
smallest = min(L)
min1 = L.index(smallest)
L.remove(smallest)
next_smallest = min(L)
min2 = L.index(next_smallest)
L.insert(min1, smallest)
if (min1 <= min2):
min2 += 1
return (min1, min2) |
def paddedInt(i):
"\n return a string that contains `i`, left-padded with 0's up to PAD_LEN digits\n "
i_str = str(i)
pad = (PAD_LEN - len(i_str))
return ((pad * '0') + i_str) | -4,372,382,450,324,855,300 | return a string that contains `i`, left-padded with 0's up to PAD_LEN digits | credstash.py | paddedInt | traveloka/credstash | python | def paddedInt(i):
"\n \n "
i_str = str(i)
pad = (PAD_LEN - len(i_str))
return ((pad * '0') + i_str) |
def getHighestVersion(name, region='us-east-1', table='credential-store'):
'\n Return the highest version of `name` in the table\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.query(Limit=1, ScanIndexForward=False, ConsistentRead=True, KeyConditionExpression=boto3.dynamodb.conditions.Key('name').eq(name), ProjectionExpression='version')
if (response['Count'] == 0):
return 0
return response['Items'][0]['version'] | 6,380,276,000,185,197,000 | Return the highest version of `name` in the table | credstash.py | getHighestVersion | traveloka/credstash | python | def getHighestVersion(name, region='us-east-1', table='credential-store'):
'\n \n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.query(Limit=1, ScanIndexForward=False, ConsistentRead=True, KeyConditionExpression=boto3.dynamodb.conditions.Key('name').eq(name), ProjectionExpression='version')
if (response['Count'] == 0):
return 0
return response['Items'][0]['version'] |
def listSecrets(region='us-east-1', table='credential-store'):
'\n do a full-table scan of the credential-store,\n and return the names and versions of every credential\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.scan(ProjectionExpression='#N, version', ExpressionAttributeNames={'#N': 'name'})
return response['Items'] | -3,835,120,575,174,796,300 | do a full-table scan of the credential-store,
and return the names and versions of every credential | credstash.py | listSecrets | traveloka/credstash | python | def listSecrets(region='us-east-1', table='credential-store'):
'\n do a full-table scan of the credential-store,\n and return the names and versions of every credential\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
response = secrets.scan(ProjectionExpression='#N, version', ExpressionAttributeNames={'#N': 'name'})
return response['Items'] |
def putSecret(name, secret, version, kms_key='alias/credstash', region='us-east-1', table='credential-store', context=None):
'\n put a secret called `name` into the secret-store,\n protected by the key kms_key\n '
if (not context):
context = {}
kms = boto3.client('kms', region_name=region)
try:
kms_response = kms.generate_data_key(KeyId=kms_key, EncryptionContext=context, NumberOfBytes=64)
except:
raise KmsError(('Could not generate key using KMS key %s' % kms_key))
data_key = kms_response['Plaintext'][:32]
hmac_key = kms_response['Plaintext'][32:]
wrapped_key = kms_response['CiphertextBlob']
enc_ctr = Counter.new(128)
encryptor = AES.new(data_key, AES.MODE_CTR, counter=enc_ctr)
c_text = encryptor.encrypt(secret)
hmac = HMAC(hmac_key, msg=c_text, digestmod=SHA256)
b64hmac = hmac.hexdigest()
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
data = {}
data['name'] = name
data['version'] = (version if (version != '') else paddedInt(1))
data['key'] = b64encode(wrapped_key).decode('utf-8')
data['contents'] = b64encode(c_text).decode('utf-8')
data['hmac'] = b64hmac
return secrets.put_item(Item=data, ConditionExpression=Attr('name').not_exists()) | -7,699,812,481,823,265,000 | put a secret called `name` into the secret-store,
protected by the key kms_key | credstash.py | putSecret | traveloka/credstash | python | def putSecret(name, secret, version, kms_key='alias/credstash', region='us-east-1', table='credential-store', context=None):
'\n put a secret called `name` into the secret-store,\n protected by the key kms_key\n '
if (not context):
context = {}
kms = boto3.client('kms', region_name=region)
try:
kms_response = kms.generate_data_key(KeyId=kms_key, EncryptionContext=context, NumberOfBytes=64)
except:
raise KmsError(('Could not generate key using KMS key %s' % kms_key))
data_key = kms_response['Plaintext'][:32]
hmac_key = kms_response['Plaintext'][32:]
wrapped_key = kms_response['CiphertextBlob']
enc_ctr = Counter.new(128)
encryptor = AES.new(data_key, AES.MODE_CTR, counter=enc_ctr)
c_text = encryptor.encrypt(secret)
hmac = HMAC(hmac_key, msg=c_text, digestmod=SHA256)
b64hmac = hmac.hexdigest()
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
data = {}
data['name'] = name
data['version'] = (version if (version != ) else paddedInt(1))
data['key'] = b64encode(wrapped_key).decode('utf-8')
data['contents'] = b64encode(c_text).decode('utf-8')
data['hmac'] = b64hmac
return secrets.put_item(Item=data, ConditionExpression=Attr('name').not_exists()) |
def getAllSecrets(version='', region='us-east-1', table='credential-store', context=None):
'\n fetch and decrypt all secrets\n '
output = {}
secrets = listSecrets(region, table)
for credential in set([x['name'] for x in secrets]):
try:
output[credential] = getSecret(credential, version, region, table, context)
except:
pass
return output | 7,797,601,393,189,596,000 | fetch and decrypt all secrets | credstash.py | getAllSecrets | traveloka/credstash | python | def getAllSecrets(version=, region='us-east-1', table='credential-store', context=None):
'\n \n '
output = {}
secrets = listSecrets(region, table)
for credential in set([x['name'] for x in secrets]):
try:
output[credential] = getSecret(credential, version, region, table, context)
except:
pass
return output |
def getSecret(name, version='', region='us-east-1', table='credential-store', context=None):
'\n fetch and decrypt the secret called `name`\n '
if (not context):
context = {}
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
if (version == ''):
response = secrets.query(Limit=1, ScanIndexForward=False, ConsistentRead=True, KeyConditionExpression=boto3.dynamodb.conditions.Key('name').eq(name))
if (response['Count'] == 0):
raise ItemNotFound(("Item {'name': '%s'} couldn't be found." % name))
material = response['Items'][0]
else:
response = secrets.get_item(Key={'name': name, 'version': version})
if ('Item' not in response):
raise ItemNotFound(("Item {'name': '%s', 'version': '%s'} couldn't be found." % (name, version)))
material = response['Item']
kms = boto3.client('kms', region_name=region)
try:
kms_response = kms.decrypt(CiphertextBlob=b64decode(material['key']), EncryptionContext=context)
except botocore.exceptions.ClientError as e:
if (e.response['Error']['Code'] == 'InvalidCiphertextException'):
if (context is None):
msg = 'Could not decrypt hmac key with KMS. The credential may require that an encryption context be provided to decrypt it.'
else:
msg = 'Could not decrypt hmac key with KMS. The encryption context provided may not match the one used when the credential was stored.'
else:
msg = ('Decryption error %s' % e)
raise KmsError(msg)
except Exception as e:
raise KmsError(('Decryption error %s' % e))
key = kms_response['Plaintext'][:32]
hmac_key = kms_response['Plaintext'][32:]
hmac = HMAC(hmac_key, msg=b64decode(material['contents']), digestmod=SHA256)
if (hmac.hexdigest() != material['hmac']):
raise IntegrityError(('Computed HMAC on %s does not match stored HMAC' % name))
dec_ctr = Counter.new(128)
decryptor = AES.new(key, AES.MODE_CTR, counter=dec_ctr)
plaintext = decryptor.decrypt(b64decode(material['contents'])).decode('utf-8')
return plaintext | 622,606,273,363,065,900 | fetch and decrypt the secret called `name` | credstash.py | getSecret | traveloka/credstash | python | def getSecret(name, version=, region='us-east-1', table='credential-store', context=None):
'\n \n '
if (not context):
context = {}
dynamodb = boto3.resource('dynamodb', region_name=region)
secrets = dynamodb.Table(table)
if (version == ):
response = secrets.query(Limit=1, ScanIndexForward=False, ConsistentRead=True, KeyConditionExpression=boto3.dynamodb.conditions.Key('name').eq(name))
if (response['Count'] == 0):
raise ItemNotFound(("Item {'name': '%s'} couldn't be found." % name))
material = response['Items'][0]
else:
response = secrets.get_item(Key={'name': name, 'version': version})
if ('Item' not in response):
raise ItemNotFound(("Item {'name': '%s', 'version': '%s'} couldn't be found." % (name, version)))
material = response['Item']
kms = boto3.client('kms', region_name=region)
try:
kms_response = kms.decrypt(CiphertextBlob=b64decode(material['key']), EncryptionContext=context)
except botocore.exceptions.ClientError as e:
if (e.response['Error']['Code'] == 'InvalidCiphertextException'):
if (context is None):
msg = 'Could not decrypt hmac key with KMS. The credential may require that an encryption context be provided to decrypt it.'
else:
msg = 'Could not decrypt hmac key with KMS. The encryption context provided may not match the one used when the credential was stored.'
else:
msg = ('Decryption error %s' % e)
raise KmsError(msg)
except Exception as e:
raise KmsError(('Decryption error %s' % e))
key = kms_response['Plaintext'][:32]
hmac_key = kms_response['Plaintext'][32:]
hmac = HMAC(hmac_key, msg=b64decode(material['contents']), digestmod=SHA256)
if (hmac.hexdigest() != material['hmac']):
raise IntegrityError(('Computed HMAC on %s does not match stored HMAC' % name))
dec_ctr = Counter.new(128)
decryptor = AES.new(key, AES.MODE_CTR, counter=dec_ctr)
plaintext = decryptor.decrypt(b64decode(material['contents'])).decode('utf-8')
return plaintext |
def createDdbTable(region='us-east-1', table='credential-store'):
'\n create the secret store table in DDB in the specified region\n '
dynamodb = boto3.resource('dynamodb', region_name=region)
if (table in (t.name for t in dynamodb.tables.all())):
print('Credential Store table already exists')
return
print('Creating table...')
response = dynamodb.create_table(TableName=table, KeySchema=[{'AttributeName': 'name', 'KeyType': 'HASH'}, {'AttributeName': 'version', 'KeyType': 'RANGE'}], AttributeDefinitions=[{'AttributeName': 'name', 'AttributeType': 'S'}, {'AttributeName': 'version', 'AttributeType': 'S'}], ProvisionedThroughput={'ReadCapacityUnits': 1, 'WriteCapacityUnits': 1})
print('Waiting for table to be created...')
client = boto3.client('dynamodb', region_name=region)
client.get_waiter('table_exists').wait(TableName=table)
print('Table has been created. Go read the README about how to create your KMS key') | 5,070,826,915,824,553,000 | create the secret store table in DDB in the specified region | credstash.py | createDdbTable | traveloka/credstash | python | def createDdbTable(region='us-east-1', table='credential-store'):
'\n \n '
dynamodb = boto3.resource('dynamodb', region_name=region)
if (table in (t.name for t in dynamodb.tables.all())):
print('Credential Store table already exists')
return
print('Creating table...')
response = dynamodb.create_table(TableName=table, KeySchema=[{'AttributeName': 'name', 'KeyType': 'HASH'}, {'AttributeName': 'version', 'KeyType': 'RANGE'}], AttributeDefinitions=[{'AttributeName': 'name', 'AttributeType': 'S'}, {'AttributeName': 'version', 'AttributeType': 'S'}], ProvisionedThroughput={'ReadCapacityUnits': 1, 'WriteCapacityUnits': 1})
print('Waiting for table to be created...')
client = boto3.client('dynamodb', region_name=region)
client.get_waiter('table_exists').wait(TableName=table)
print('Table has been created. Go read the README about how to create your KMS key') |
def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
'Stack InvertedResidual blocks to build a layer for MobileNetV2.\n\n Args:\n out_channels (int): out_channels of block.\n num_blocks (int): number of blocks.\n stride (int): stride of the first block. Default: 1\n expand_ratio (int): Expand the number of channels of the\n hidden layer in InvertedResidual by this ratio. Default: 6.\n '
layers = []
for i in range(num_blocks):
if (i >= 1):
stride = 1
layers.append(InvertedResidual(self.in_channels, out_channels, stride, expand_ratio=expand_ratio, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg, with_cp=self.with_cp))
self.in_channels = out_channels
return nn.Sequential(*layers) | 6,643,845,954,223,003,000 | Stack InvertedResidual blocks to build a layer for MobileNetV2.
Args:
out_channels (int): out_channels of block.
num_blocks (int): number of blocks.
stride (int): stride of the first block. Default: 1
expand_ratio (int): Expand the number of channels of the
hidden layer in InvertedResidual by this ratio. Default: 6. | mmcls/models/backbones/mobilenet_v2.py | make_layer | ChaseMonsterAway/mmclassification | python | def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
'Stack InvertedResidual blocks to build a layer for MobileNetV2.\n\n Args:\n out_channels (int): out_channels of block.\n num_blocks (int): number of blocks.\n stride (int): stride of the first block. Default: 1\n expand_ratio (int): Expand the number of channels of the\n hidden layer in InvertedResidual by this ratio. Default: 6.\n '
layers = []
for i in range(num_blocks):
if (i >= 1):
stride = 1
layers.append(InvertedResidual(self.in_channels, out_channels, stride, expand_ratio=expand_ratio, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg, with_cp=self.with_cp))
self.in_channels = out_channels
return nn.Sequential(*layers) |
def __init__(self, code):
"Initialize a PDBFile object with a pdb file of interest\n\n Parameters\n ----------\n code : the pdb code if interest\n Any valid PDB code can be passed into PDBFile.\n\n Examples\n --------\n >>> pdb_file = PDBFile('1rcy') \n \n "
self.code = code.lower() | 835,532,312,311,867,000 | Initialize a PDBFile object with a pdb file of interest
Parameters
----------
code : the pdb code if interest
Any valid PDB code can be passed into PDBFile.
Examples
--------
>>> pdb_file = PDBFile('1rcy') | scalene-triangle/libs/PDB_filegetter.py | __init__ | dsw7/BridgingInteractions | python | def __init__(self, code):
"Initialize a PDBFile object with a pdb file of interest\n\n Parameters\n ----------\n code : the pdb code if interest\n Any valid PDB code can be passed into PDBFile.\n\n Examples\n --------\n >>> pdb_file = PDBFile('1rcy') \n \n "
self.code = code.lower() |
def fetch_from_PDB(self):
"\n Connects to PDB FTP server, downloads a .gz file of interest,\n decompresses the .gz file into .ent and then dumps a copy of\n the pdb{code}.ent file into cwd.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n \n >>> inst = PDBFile('1rcy')\n >>> path_to_file = inst.fetch_from_PDB()\n >>> print(path_to_file)\n \n "
subdir = self.code[1:3]
infile = 'pdb{}.ent.gz'.format(self.code)
decompressed = infile.strip('.gz')
fullpath = ROOT.format(subdir, infile)
try:
urlcleanup()
urlretrieve(fullpath, infile)
except Exception:
return 'URLError'
else:
with gzip.open(infile, 'rb') as gz:
with open(decompressed, 'wb') as out:
out.writelines(gz)
remove(infile)
return path.join(getcwd(), decompressed) | 5,381,435,870,021,593,000 | Connects to PDB FTP server, downloads a .gz file of interest,
decompresses the .gz file into .ent and then dumps a copy of
the pdb{code}.ent file into cwd.
Parameters
----------
None
Examples
--------
>>> inst = PDBFile('1rcy')
>>> path_to_file = inst.fetch_from_PDB()
>>> print(path_to_file) | scalene-triangle/libs/PDB_filegetter.py | fetch_from_PDB | dsw7/BridgingInteractions | python | def fetch_from_PDB(self):
"\n Connects to PDB FTP server, downloads a .gz file of interest,\n decompresses the .gz file into .ent and then dumps a copy of\n the pdb{code}.ent file into cwd.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n \n >>> inst = PDBFile('1rcy')\n >>> path_to_file = inst.fetch_from_PDB()\n >>> print(path_to_file)\n \n "
subdir = self.code[1:3]
infile = 'pdb{}.ent.gz'.format(self.code)
decompressed = infile.strip('.gz')
fullpath = ROOT.format(subdir, infile)
try:
urlcleanup()
urlretrieve(fullpath, infile)
except Exception:
return 'URLError'
else:
with gzip.open(infile, 'rb') as gz:
with open(decompressed, 'wb') as out:
out.writelines(gz)
remove(infile)
return path.join(getcwd(), decompressed) |
def clear(self):
"\n Deletes file from current working directory after the file has\n been processed by some algorithm.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n >>> inst = PDBFile('1rcy')\n >>> path_to_file = inst.fetch_from_PDB()\n >>> print(path_to_file) # process the file using some algorithm\n >>> inst.clear()\n \n "
filename = 'pdb{}.ent'.format(self.code)
try:
remove(path.join(getcwd(), filename))
except FileNotFoundError:
print('Cannot delete file. Does not exist.') | 8,477,879,807,243,158,000 | Deletes file from current working directory after the file has
been processed by some algorithm.
Parameters
----------
None
Examples
--------
>>> inst = PDBFile('1rcy')
>>> path_to_file = inst.fetch_from_PDB()
>>> print(path_to_file) # process the file using some algorithm
>>> inst.clear() | scalene-triangle/libs/PDB_filegetter.py | clear | dsw7/BridgingInteractions | python | def clear(self):
"\n Deletes file from current working directory after the file has\n been processed by some algorithm.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n >>> inst = PDBFile('1rcy')\n >>> path_to_file = inst.fetch_from_PDB()\n >>> print(path_to_file) # process the file using some algorithm\n >>> inst.clear()\n \n "
filename = 'pdb{}.ent'.format(self.code)
try:
remove(path.join(getcwd(), filename))
except FileNotFoundError:
print('Cannot delete file. Does not exist.') |
def gen_captcha_text_image(self, img_name):
'\n 返回一个验证码的array形式和对应的字符串标签\n :return:tuple (str, numpy.array)\n '
label = img_name.split('_')[0]
img_file = os.path.join(self.img_path, img_name)
captcha_image = Image.open(img_file)
captcha_array = np.array(captcha_image)
return (label, captcha_array) | 7,944,805,907,609,061,000 | 返回一个验证码的array形式和对应的字符串标签
:return:tuple (str, numpy.array) | train_model.py | gen_captcha_text_image | shineyjg/cnn_captcha | python | def gen_captcha_text_image(self, img_name):
'\n 返回一个验证码的array形式和对应的字符串标签\n :return:tuple (str, numpy.array)\n '
label = img_name.split('_')[0]
img_file = os.path.join(self.img_path, img_name)
captcha_image = Image.open(img_file)
captcha_array = np.array(captcha_image)
return (label, captcha_array) |
@staticmethod
def convert2gray(img):
'\n 图片转为灰度图，如果是3通道图则计算，单通道图则直接返回\n :param img:\n :return:\n '
if (len(img.shape) > 2):
(r, g, b) = (img[:, :, 0], img[:, :, 1], img[:, :, 2])
gray = (((0.2989 * r) + (0.587 * g)) + (0.114 * b))
return gray
else:
return img | 611,634,753,502,825,900 | 图片转为灰度图，如果是3通道图则计算，单通道图则直接返回
:param img:
:return: | train_model.py | convert2gray | shineyjg/cnn_captcha | python | @staticmethod
def convert2gray(img):
'\n 图片转为灰度图，如果是3通道图则计算，单通道图则直接返回\n :param img:\n :return:\n '
if (len(img.shape) > 2):
(r, g, b) = (img[:, :, 0], img[:, :, 1], img[:, :, 2])
gray = (((0.2989 * r) + (0.587 * g)) + (0.114 * b))
return gray
else:
return img |
def text2vec(self, text):
'\n 转标签为oneHot编码\n :param text: str\n :return: numpy.array\n '
text_len = len(text)
if (text_len > self.max_captcha):
raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
vector = np.zeros((self.max_captcha * self.char_set_len))
for (i, ch) in enumerate(text):
idx = ((i * self.char_set_len) + self.char_set.index(ch))
vector[idx] = 1
return vector | -1,980,550,115,108,716,800 | 转标签为oneHot编码
:param text: str
:return: numpy.array | train_model.py | text2vec | shineyjg/cnn_captcha | python | def text2vec(self, text):
'\n 转标签为oneHot编码\n :param text: str\n :return: numpy.array\n '
text_len = len(text)
if (text_len > self.max_captcha):
raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
vector = np.zeros((self.max_captcha * self.char_set_len))
for (i, ch) in enumerate(text):
idx = ((i * self.char_set_len) + self.char_set.index(ch))
vector[idx] = 1
return vector |
def get_converter(from_unit, to_unit):
'Like Unit._get_converter, except returns None if no scaling is needed,\n i.e., if the inferred scale is unity.'
try:
scale = from_unit._to(to_unit)
except UnitsError:
return from_unit._apply_equivalencies(from_unit, to_unit, get_current_unit_registry().equivalencies)
except AttributeError:
raise UnitTypeError("Unit '{0}' cannot be converted to '{1}'".format(from_unit, to_unit))
if (scale == 1.0):
return None
else:
return (lambda val: (scale * val)) | 6,356,987,915,934,134,000 | Like Unit._get_converter, except returns None if no scaling is needed,
i.e., if the inferred scale is unity. | astropy/units/quantity_helper/helpers.py | get_converter | PriyankaH21/astropy | python | def get_converter(from_unit, to_unit):
'Like Unit._get_converter, except returns None if no scaling is needed,\n i.e., if the inferred scale is unity.'
try:
scale = from_unit._to(to_unit)
except UnitsError:
return from_unit._apply_equivalencies(from_unit, to_unit, get_current_unit_registry().equivalencies)
except AttributeError:
raise UnitTypeError("Unit '{0}' cannot be converted to '{1}'".format(from_unit, to_unit))
if (scale == 1.0):
return None
else:
return (lambda val: (scale * val)) |
def _raw_fetch(url, logger):
'\n Fetch remote data and return the text output.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Raw text data, None otherwise\n '
ret_data = None
try:
req = requests.get(url)
if (req.status_code == requests.codes.ok):
ret_data = req.text
except requests.exceptions.ConnectionError as error:
logger.warning(error.request)
return ret_data | -894,493,403,224,933,800 | Fetch remote data and return the text output.
:param url: The URL to fetch the data from
:param logger: A logger instance to use.
:return: Raw text data, None otherwise | atkinson/dlrn/http_data.py | _raw_fetch | jpichon/atkinson | python | def _raw_fetch(url, logger):
'\n Fetch remote data and return the text output.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Raw text data, None otherwise\n '
ret_data = None
try:
req = requests.get(url)
if (req.status_code == requests.codes.ok):
ret_data = req.text
except requests.exceptions.ConnectionError as error:
logger.warning(error.request)
return ret_data |
def _fetch_yaml(url, logger):
'\n Fetch remote data and process the text as yaml.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Parsed yaml data in the form of a dictionary\n '
ret_data = None
raw_data = _raw_fetch(url, logger)
if (raw_data is not None):
ret_data = yaml.parse(raw_data)
return ret_data | -3,088,369,978,945,365,500 | Fetch remote data and process the text as yaml.
:param url: The URL to fetch the data from
:param logger: A logger instance to use.
:return: Parsed yaml data in the form of a dictionary | atkinson/dlrn/http_data.py | _fetch_yaml | jpichon/atkinson | python | def _fetch_yaml(url, logger):
'\n Fetch remote data and process the text as yaml.\n\n :param url: The URL to fetch the data from\n :param logger: A logger instance to use.\n :return: Parsed yaml data in the form of a dictionary\n '
ret_data = None
raw_data = _raw_fetch(url, logger)
if (raw_data is not None):
ret_data = yaml.parse(raw_data)
return ret_data |
def dlrn_http_factory(host, config_file=None, link_name=None, logger=getLogger()):
'\n Create a DlrnData instance based on a host.\n\n :param host: A host name string to build instances\n :param config_file: A dlrn config file(s) to use in addition to\n the default.\n :param link_name: A dlrn symlink to use. This overrides the config files\n link parameter.\n :param logger: An atkinson logger to use. Default is the base logger.\n :return: A DlrnData instance\n '
manager = None
files = ['dlrn.yml']
if (config_file is not None):
if isinstance(config_file, list):
files.extend(config_file)
else:
files.append(config_file)
local_path = os.path.realpath(os.path.dirname(__file__))
manager = ConfigManager(filenames=files, paths=local_path)
if (manager is None):
return None
config = manager.config
if (host not in config):
return None
link = config[host]['link']
if (link_name is not None):
link = link_name
return DlrnHttpData(config[host]['url'], config[host]['release'], link_name=link, logger=logger) | -4,437,842,762,096,356,400 | Create a DlrnData instance based on a host.
:param host: A host name string to build instances
:param config_file: A dlrn config file(s) to use in addition to
the default.
:param link_name: A dlrn symlink to use. This overrides the config files
link parameter.
:param logger: An atkinson logger to use. Default is the base logger.
:return: A DlrnData instance | atkinson/dlrn/http_data.py | dlrn_http_factory | jpichon/atkinson | python | def dlrn_http_factory(host, config_file=None, link_name=None, logger=getLogger()):
'\n Create a DlrnData instance based on a host.\n\n :param host: A host name string to build instances\n :param config_file: A dlrn config file(s) to use in addition to\n the default.\n :param link_name: A dlrn symlink to use. This overrides the config files\n link parameter.\n :param logger: An atkinson logger to use. Default is the base logger.\n :return: A DlrnData instance\n '
manager = None
files = ['dlrn.yml']
if (config_file is not None):
if isinstance(config_file, list):
files.extend(config_file)
else:
files.append(config_file)
local_path = os.path.realpath(os.path.dirname(__file__))
manager = ConfigManager(filenames=files, paths=local_path)
if (manager is None):
return None
config = manager.config
if (host not in config):
return None
link = config[host]['link']
if (link_name is not None):
link = link_name
return DlrnHttpData(config[host]['url'], config[host]['release'], link_name=link, logger=logger) |
def __init__(self, url, release, link_name='current', logger=getLogger()):
'\n Class constructor\n\n :param url: The URL to the host to obtain data.\n :param releases: The release name to use for lookup.\n :param link_name: The name of the dlrn symlink to fetch data from.\n :param logger: An atkinson logger to use. Default is the base logger.\n '
self.url = os.path.join(url, release)
self.release = release
self._logger = logger
self._link_name = link_name
self._commit_data = {}
self._fetch_commit() | -1,853,492,324,126,466,600 | Class constructor
:param url: The URL to the host to obtain data.
:param releases: The release name to use for lookup.
:param link_name: The name of the dlrn symlink to fetch data from.
:param logger: An atkinson logger to use. Default is the base logger. | atkinson/dlrn/http_data.py | __init__ | jpichon/atkinson | python | def __init__(self, url, release, link_name='current', logger=getLogger()):
'\n Class constructor\n\n :param url: The URL to the host to obtain data.\n :param releases: The release name to use for lookup.\n :param link_name: The name of the dlrn symlink to fetch data from.\n :param logger: An atkinson logger to use. Default is the base logger.\n '
self.url = os.path.join(url, release)
self.release = release
self._logger = logger
self._link_name = link_name
self._commit_data = {}
self._fetch_commit() |
def _fetch_commit(self):
'\n Fetch the commit data from dlrn\n '
full_url = os.path.join(self.url, self._link_name, 'commit.yaml')
data = _fetch_yaml(full_url, self._logger)
if ((data is not None) and ('commits' in data)):
pkg = data['commits'][0]
if (pkg['status'] == 'SUCCESS'):
self._commit_data = {'name': pkg['project_name'], 'dist_hash': pkg['distro_hash'], 'commit_hash': pkg['commit_hash'], 'extended_hash': pkg.get('extended_hash')}
else:
msg = '{0} has a status of error'.format(str(pkg))
self._logger.warning(msg) | 6,997,459,630,592,828,000 | Fetch the commit data from dlrn | atkinson/dlrn/http_data.py | _fetch_commit | jpichon/atkinson | python | def _fetch_commit(self):
'\n \n '
full_url = os.path.join(self.url, self._link_name, 'commit.yaml')
data = _fetch_yaml(full_url, self._logger)
if ((data is not None) and ('commits' in data)):
pkg = data['commits'][0]
if (pkg['status'] == 'SUCCESS'):
self._commit_data = {'name': pkg['project_name'], 'dist_hash': pkg['distro_hash'], 'commit_hash': pkg['commit_hash'], 'extended_hash': pkg.get('extended_hash')}
else:
msg = '{0} has a status of error'.format(str(pkg))
self._logger.warning(msg) |
def _build_url(self):
'\n Generate a url given a commit hash and distgit hash to match the format\n base/AB/CD/ABCD123_XYZ987 where ABCD123 is the commit hash and XYZ987\n is a portion of the distgit hash.\n\n :return: A string with the full URL.\n '
first = self._commit_data['commit_hash'][0:2]
second = self._commit_data['commit_hash'][2:4]
third = self._commit_data['commit_hash']
for key in ['dist_hash', 'extended_hash']:
if (self._commit_data.get(key, 'None') != 'None'):
third += ('_' + self._commit_data[key][0:8])
return os.path.join(self.url, first, second, third) | -3,125,452,940,105,935,000 | Generate a url given a commit hash and distgit hash to match the format
base/AB/CD/ABCD123_XYZ987 where ABCD123 is the commit hash and XYZ987
is a portion of the distgit hash.
:return: A string with the full URL. | atkinson/dlrn/http_data.py | _build_url | jpichon/atkinson | python | def _build_url(self):
'\n Generate a url given a commit hash and distgit hash to match the format\n base/AB/CD/ABCD123_XYZ987 where ABCD123 is the commit hash and XYZ987\n is a portion of the distgit hash.\n\n :return: A string with the full URL.\n '
first = self._commit_data['commit_hash'][0:2]
second = self._commit_data['commit_hash'][2:4]
third = self._commit_data['commit_hash']
for key in ['dist_hash', 'extended_hash']:
if (self._commit_data.get(key, 'None') != 'None'):
third += ('_' + self._commit_data[key][0:8])
return os.path.join(self.url, first, second, third) |
@property
def commit(self):
'\n Get the dlrn commit information\n\n :return: A dictionary of name, dist-git hash, commit hash and\n extended hash.\n An empty dictionary is returned otherwise.\n '
return self._commit_data | -1,729,170,792,126,949,000 | Get the dlrn commit information
:return: A dictionary of name, dist-git hash, commit hash and
extended hash.
An empty dictionary is returned otherwise. | atkinson/dlrn/http_data.py | commit | jpichon/atkinson | python | @property
def commit(self):
'\n Get the dlrn commit information\n\n :return: A dictionary of name, dist-git hash, commit hash and\n extended hash.\n An empty dictionary is returned otherwise.\n '
return self._commit_data |
@property
def versions(self):
'\n Get the version data for the versions.csv file and return the\n data in a dictionary\n\n :return: A dictionary of packages with commit and dist-git hashes\n '
ret_dict = {}
full_url = os.path.join(self._build_url(), 'versions.csv')
data = _raw_fetch(full_url, self._logger)
if (data is not None):
data = data.replace(' ', '_')
split_data = data.split()
reader = csv.DictReader(split_data)
for row in reader:
ret_dict[row['Project']] = {'source': row['Source_Sha'], 'state': row['Status'], 'distgit': row['Dist_Sha'], 'nvr': row['Pkg_NVR']}
else:
msg = 'Could not fetch {0}'.format(full_url)
self._logger.error(msg)
return ret_dict | -7,811,259,190,884,229,000 | Get the version data for the versions.csv file and return the
data in a dictionary
:return: A dictionary of packages with commit and dist-git hashes | atkinson/dlrn/http_data.py | versions | jpichon/atkinson | python | @property
def versions(self):
'\n Get the version data for the versions.csv file and return the\n data in a dictionary\n\n :return: A dictionary of packages with commit and dist-git hashes\n '
ret_dict = {}
full_url = os.path.join(self._build_url(), 'versions.csv')
data = _raw_fetch(full_url, self._logger)
if (data is not None):
data = data.replace(' ', '_')
split_data = data.split()
reader = csv.DictReader(split_data)
for row in reader:
ret_dict[row['Project']] = {'source': row['Source_Sha'], 'state': row['Status'], 'distgit': row['Dist_Sha'], 'nvr': row['Pkg_NVR']}
else:
msg = 'Could not fetch {0}'.format(full_url)
self._logger.error(msg)
return ret_dict |
def compute_train_val_test(X, y, model, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Compute the training-validation-test scores for the given model on the given dataset.\n\n The training and test scores are simply computed by splitting the dataset into the training and test sets. The validation\n score is performed applying the cross validation on the training set.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model to evaluate.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. datasets indexed by days).\n (This affects the computing of the scores).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n\n Returns\n ----------\n train_score: float\n val_score: float\n test_score: float\n\n Notes\n ----------\n - If `regr` is True, the returned scores are errors, computed using the MSE formula (i.e. Mean Squared Error).\n Otherwise, the returned scores are accuracy measures.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are obtained simply by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
if regr:
scoring = 'neg_mean_squared_error'
else:
scoring = 'accuracy'
if (not time_series):
(X_train_80, X_test, y_train_80, y_test) = train_test_split(X, y, test_size=test_size, random_state=random_state)
else:
train_len = int((X.shape[0] * (1 - test_size)))
X_train_80 = X[:train_len]
y_train_80 = y[:train_len]
X_test = X[train_len:]
y_test = y[train_len:]
if scale:
scaler = MinMaxScaler()
scaler.fit(X_train_80)
X_train_80 = scaler.transform(X_train_80)
X_test = scaler.transform(X_test)
if (not time_series):
cv = n_folds
else:
cv = TimeSeriesSplit(n_splits=n_folds)
scores = cross_val_score(model, X_train_80, y_train_80, cv=cv, scoring=scoring)
val_score = scores.mean()
if regr:
val_score = (- val_score)
model.fit(X_train_80, y_train_80)
train_score = 0
test_score = 0
if regr:
train_score = mean_squared_error(y_true=y_train_80, y_pred=model.predict(X_train_80))
test_score = mean_squared_error(y_true=y_test, y_pred=model.predict(X_test))
else:
train_score = accuracy_score(y_true=y_train_80, y_pred=model.predict(X_train_80))
test_score = accuracy_score(y_true=y_test, y_pred=model.predict(X_test))
return (train_score, val_score, test_score) | -5,066,045,042,697,431,000 | Compute the training-validation-test scores for the given model on the given dataset.
The training and test scores are simply computed by splitting the dataset into the training and test sets. The validation
score is performed applying the cross validation on the training set.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory features of the dataset.
y: np.array
Mono dimensional np.array, containing the response feature of the dataset.
model: sklearn.base.BaseEstimator
Model to evaluate.
scale: bool
Indicates whether to scale or not the features in `X`.
(The scaling is performed using the sklearn MinMaxScaler).
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set.
time_series: bool
Indicates if the given dataset is a time series dataset (i.e. datasets indexed by days).
(This affects the computing of the scores).
random_state: int
Used in the training-test splitting of the dataset.
n_folds: int
Indicates how many folds are made in order to compute the k-fold cross validation.
(It's used only if `time_series` is False).
regr: bool
Indicates if it's either a regression or a classification problem.
Returns
----------
train_score: float
val_score: float
test_score: float
Notes
----------
- If `regr` is True, the returned scores are errors, computed using the MSE formula (i.e. Mean Squared Error).
Otherwise, the returned scores are accuracy measures.
- If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross
validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.
Otherwise, if `time_series` is True, the training-test sets are obtained simply by splitting the dataset into two
contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit. | model_selection.py | compute_train_val_test | EnricoPittini/model-selection | python | def compute_train_val_test(X, y, model, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Compute the training-validation-test scores for the given model on the given dataset.\n\n The training and test scores are simply computed by splitting the dataset into the training and test sets. The validation\n score is performed applying the cross validation on the training set.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model to evaluate.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. datasets indexed by days).\n (This affects the computing of the scores).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n\n Returns\n ----------\n train_score: float\n val_score: float\n test_score: float\n\n Notes\n ----------\n - If `regr` is True, the returned scores are errors, computed using the MSE formula (i.e. Mean Squared Error).\n Otherwise, the returned scores are accuracy measures.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are obtained simply by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
if regr:
scoring = 'neg_mean_squared_error'
else:
scoring = 'accuracy'
if (not time_series):
(X_train_80, X_test, y_train_80, y_test) = train_test_split(X, y, test_size=test_size, random_state=random_state)
else:
train_len = int((X.shape[0] * (1 - test_size)))
X_train_80 = X[:train_len]
y_train_80 = y[:train_len]
X_test = X[train_len:]
y_test = y[train_len:]
if scale:
scaler = MinMaxScaler()
scaler.fit(X_train_80)
X_train_80 = scaler.transform(X_train_80)
X_test = scaler.transform(X_test)
if (not time_series):
cv = n_folds
else:
cv = TimeSeriesSplit(n_splits=n_folds)
scores = cross_val_score(model, X_train_80, y_train_80, cv=cv, scoring=scoring)
val_score = scores.mean()
if regr:
val_score = (- val_score)
model.fit(X_train_80, y_train_80)
train_score = 0
test_score = 0
if regr:
train_score = mean_squared_error(y_true=y_train_80, y_pred=model.predict(X_train_80))
test_score = mean_squared_error(y_true=y_test, y_pred=model.predict(X_test))
else:
train_score = accuracy_score(y_true=y_train_80, y_pred=model.predict(X_train_80))
test_score = accuracy_score(y_true=y_test, y_pred=model.predict(X_test))
return (train_score, val_score, test_score) |
def compute_bias_variance_error(X, y, model, scale=False, N_TESTS=20, sample_size=0.67):
'\n Compute the bias^2-variance-error scores for the given model on the given dataset.\n\n These measures are computed in an approximate way, using `N_TESTS` random samples of size `sample_size` from the\n dataset.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model to evaluate.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n N_TESTS: int\n Number of samples that are made in order to compute the measures.\n sample_size: float\n Decimal number between 0 and 1, which indicates the proportion of the sample.\n\n Returns\n ----------\n bias: float\n variance: float\n error: float\n '
if scale:
scaler = MinMaxScaler()
scaler.fit(X)
X = scaler.transform(X)
vector_ypred = []
for i in range(N_TESTS):
(Xs, ys) = resample(X, y, n_samples=int((sample_size * len(y))))
model.fit(Xs, ys)
vector_ypred.append(list(model.predict(X)))
vector_ypred = np.array(vector_ypred)
vector_bias = ((y - np.mean(vector_ypred, axis=0)) ** 2)
vector_variance = np.var(vector_ypred, axis=0)
vector_error = (np.sum(((vector_ypred - y) ** 2), axis=0) / N_TESTS)
bias = np.mean(vector_bias)
variance = np.mean(vector_variance)
error = np.mean(vector_error)
return (bias, variance, error) | 1,135,176,463,303,326,600 | Compute the bias^2-variance-error scores for the given model on the given dataset.
These measures are computed in an approximate way, using `N_TESTS` random samples of size `sample_size` from the
dataset.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory features of the dataset.
y: np.array
Mono dimensional np.array, containing the response feature of the dataset.
model: sklearn.base.BaseEstimator
Model to evaluate.
scale: bool
Indicates whether to scale or not the features in `X`.
(The scaling is performed using the sklearn MinMaxScaler).
N_TESTS: int
Number of samples that are made in order to compute the measures.
sample_size: float
Decimal number between 0 and 1, which indicates the proportion of the sample.
Returns
----------
bias: float
variance: float
error: float | model_selection.py | compute_bias_variance_error | EnricoPittini/model-selection | python | def compute_bias_variance_error(X, y, model, scale=False, N_TESTS=20, sample_size=0.67):
'\n Compute the bias^2-variance-error scores for the given model on the given dataset.\n\n These measures are computed in an approximate way, using `N_TESTS` random samples of size `sample_size` from the\n dataset.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model to evaluate.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n N_TESTS: int\n Number of samples that are made in order to compute the measures.\n sample_size: float\n Decimal number between 0 and 1, which indicates the proportion of the sample.\n\n Returns\n ----------\n bias: float\n variance: float\n error: float\n '
if scale:
scaler = MinMaxScaler()
scaler.fit(X)
X = scaler.transform(X)
vector_ypred = []
for i in range(N_TESTS):
(Xs, ys) = resample(X, y, n_samples=int((sample_size * len(y))))
model.fit(Xs, ys)
vector_ypred.append(list(model.predict(X)))
vector_ypred = np.array(vector_ypred)
vector_bias = ((y - np.mean(vector_ypred, axis=0)) ** 2)
vector_variance = np.var(vector_ypred, axis=0)
vector_error = (np.sum(((vector_ypred - y) ** 2), axis=0) / N_TESTS)
bias = np.mean(vector_bias)
variance = np.mean(vector_variance)
error = np.mean(vector_error)
return (bias, variance, error) |
def plot_predictions(X, y, model, scale=False, test_size=0.2, plot_type=0, xvalues=None, xlabel='Index', title='Actual vs Predicted values', figsize=(6, 6)):
"\n Plot the predictions made by the given model on the given dataset, versus its actual values.\n\n The dataset is split into training-test sets: the former is used to train the `model`, on the latter the predictions are\n made.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model used to make the predictions.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n plot_type: int\n Indicates the type of the plot.\n - 0 -> In the same plot two different curves are drawn: the first has on the x axis `xvalues` and on the y axis\n the actual values (i.e. `y`); the second has on the x axis `xvalues` and on the y axis the computed\n predicted values.\n - 1 -> On the x axis the actual values are put, on the y axis the predicted ones.\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n (It's used only if `plot_type` is 0).\n xlabel: str\n Label of the x axis of the plot.\n (It's used only if `plot_type` is 0).\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n\n Notes\n ----------\n The splitting of the datasets into the training-test sets is simply made by dividing the dataset into two contiguous\n sequences.\n I.e. it is the same technique used usually when the dataset is a time series dataset. (This is done in order to simplify\n the visualization).\n For this reason, typically this function is applied on time series datasets.\n "
train_len = int((X.shape[0] * (1 - test_size)))
X_train_80 = X[:train_len]
y_train_80 = y[:train_len]
X_test = X[train_len:]
y_test = y[train_len:]
if scale:
scaler = MinMaxScaler()
scaler.fit(X_train_80)
X_train_80 = scaler.transform(X_train_80)
X_test = scaler.transform(X_test)
model.fit(X_train_80, y_train_80)
predictions = model.predict(X_test)
(fig, ax) = plt.subplots(figsize=figsize)
if (plot_type == 0):
if (xvalues is None):
xvalues = range(len(X))
ax.plot(xvalues, y, 'o:', label='actual values')
ax.plot(xvalues[train_len:], predictions, 'o:', label='predicted values')
ax.legend()
elif (plot_type == 1):
ax.plot(y[train_len:], predictions, 'o')
ax.plot([0, 1], [0, 1], 'r-', transform=ax.transAxes)
xlabel = 'Actual values'
ax.set_ylabel('Predicted values')
ax.set_xlabel(xlabel)
ax.set_title(title)
ax.grid()
return ax | 6,549,853,644,879,781,000 | Plot the predictions made by the given model on the given dataset, versus its actual values.
The dataset is split into training-test sets: the former is used to train the `model`, on the latter the predictions are
made.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory features of the dataset.
y: np.array
Mono dimensional np.array, containing the response feature of the dataset.
model: sklearn.base.BaseEstimator
Model used to make the predictions.
scale: bool
Indicates whether to scale or not the features in `X`.
(The scaling is performed using the sklearn MinMaxScaler).
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set.
plot_type: int
Indicates the type of the plot.
- 0 -> In the same plot two different curves are drawn: the first has on the x axis `xvalues` and on the y axis
the actual values (i.e. `y`); the second has on the x axis `xvalues` and on the y axis the computed
predicted values.
- 1 -> On the x axis the actual values are put, on the y axis the predicted ones.
xvalues: list (in general, iterable)
Values that have to be put in the x axis of the plot.
(It's used only if `plot_type` is 0).
xlabel: str
Label of the x axis of the plot.
(It's used only if `plot_type` is 0).
title: str
Title of the plot.
figsize: tuple
Two dimensions of the plot.
Returns
----------
matplotlib.axes.Axes
The matplotlib Axes where the plot has been made.
Notes
----------
The splitting of the datasets into the training-test sets is simply made by dividing the dataset into two contiguous
sequences.
I.e. it is the same technique used usually when the dataset is a time series dataset. (This is done in order to simplify
the visualization).
For this reason, typically this function is applied on time series datasets. | model_selection.py | plot_predictions | EnricoPittini/model-selection | python | def plot_predictions(X, y, model, scale=False, test_size=0.2, plot_type=0, xvalues=None, xlabel='Index', title='Actual vs Predicted values', figsize=(6, 6)):
"\n Plot the predictions made by the given model on the given dataset, versus its actual values.\n\n The dataset is split into training-test sets: the former is used to train the `model`, on the latter the predictions are\n made.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model used to make the predictions.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n plot_type: int\n Indicates the type of the plot.\n - 0 -> In the same plot two different curves are drawn: the first has on the x axis `xvalues` and on the y axis\n the actual values (i.e. `y`); the second has on the x axis `xvalues` and on the y axis the computed\n predicted values.\n - 1 -> On the x axis the actual values are put, on the y axis the predicted ones.\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n (It's used only if `plot_type` is 0).\n xlabel: str\n Label of the x axis of the plot.\n (It's used only if `plot_type` is 0).\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n\n Notes\n ----------\n The splitting of the datasets into the training-test sets is simply made by dividing the dataset into two contiguous\n sequences.\n I.e. it is the same technique used usually when the dataset is a time series dataset. (This is done in order to simplify\n the visualization).\n For this reason, typically this function is applied on time series datasets.\n "
train_len = int((X.shape[0] * (1 - test_size)))
X_train_80 = X[:train_len]
y_train_80 = y[:train_len]
X_test = X[train_len:]
y_test = y[train_len:]
if scale:
scaler = MinMaxScaler()
scaler.fit(X_train_80)
X_train_80 = scaler.transform(X_train_80)
X_test = scaler.transform(X_test)
model.fit(X_train_80, y_train_80)
predictions = model.predict(X_test)
(fig, ax) = plt.subplots(figsize=figsize)
if (plot_type == 0):
if (xvalues is None):
xvalues = range(len(X))
ax.plot(xvalues, y, 'o:', label='actual values')
ax.plot(xvalues[train_len:], predictions, 'o:', label='predicted values')
ax.legend()
elif (plot_type == 1):
ax.plot(y[train_len:], predictions, 'o')
ax.plot([0, 1], [0, 1], 'r-', transform=ax.transAxes)
xlabel = 'Actual values'
ax.set_ylabel('Predicted values')
ax.set_xlabel(xlabel)
ax.set_title(title)
ax.grid()
return ax |
def _plot_TrainVal_values(xvalues, train_val_scores, plot_train, xlabel, title, figsize=(6, 6), bar=False):
"\n Plot the given list of training-validation scores.\n\n This function is an auxiliary function for the model selection functions. It's meant to be private in the\n module.\n\n Parameters\n ----------\n xvalues: list (in general iterable)\n Values to put in the x axis of the plot.\n train_val_scores: np.array\n Two dimensional np.array, containing two columns: the first contains the trainining scores, the second the validation\n scores.\n Basically, it is a list of training-validation scores.\n plot_train: bool\n Indicates whether to plot also the training scores or to plot only the validation ones.\n xlabel: str\n Label of the x axis.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n bar: bool\n Indicates whether to plot the scores using bars or using points.\n If `bar` it's True, `xvalues` must contain string (i.e. labels).\n Returns\n ----------\n matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n "
(fig, ax) = plt.subplots(figsize=figsize)
if (not bar):
if plot_train:
ax.plot(xvalues, train_val_scores[:, 0], 'o:', label='Train')
ax.plot(xvalues, train_val_scores[:, 1], 'o:', label='Validation')
elif plot_train:
x = np.arange(len(xvalues))
width = 0.35
ax.bar((x - (width / 2)), train_val_scores[:, 0], width=width, label='Train')
ax.bar((x + (width / 2)), train_val_scores[:, 1], width=width, label='Validation')
ax.set_xticks(x)
ax.set_xticklabels(xvalues)
else:
ax.bar(xvalues, train_val_scores[:, 1], label='Validation')
ax.set_xlabel(xlabel)
ax.set_title(title)
ax.grid()
ax.legend()
return ax | -2,627,312,043,539,120,600 | Plot the given list of training-validation scores.
This function is an auxiliary function for the model selection functions. It's meant to be private in the
module.
Parameters
----------
xvalues: list (in general iterable)
Values to put in the x axis of the plot.
train_val_scores: np.array
Two dimensional np.array, containing two columns: the first contains the trainining scores, the second the validation
scores.
Basically, it is a list of training-validation scores.
plot_train: bool
Indicates whether to plot also the training scores or to plot only the validation ones.
xlabel: str
Label of the x axis.
title: str
Title of the plot.
figsize: tuple
Two dimensions of the plot.
bar: bool
Indicates whether to plot the scores using bars or using points.
If `bar` it's True, `xvalues` must contain string (i.e. labels).
Returns
----------
matplotlib.axes.Axes
The matplotlib Axes where the plot has been made. | model_selection.py | _plot_TrainVal_values | EnricoPittini/model-selection | python | def _plot_TrainVal_values(xvalues, train_val_scores, plot_train, xlabel, title, figsize=(6, 6), bar=False):
"\n Plot the given list of training-validation scores.\n\n This function is an auxiliary function for the model selection functions. It's meant to be private in the\n module.\n\n Parameters\n ----------\n xvalues: list (in general iterable)\n Values to put in the x axis of the plot.\n train_val_scores: np.array\n Two dimensional np.array, containing two columns: the first contains the trainining scores, the second the validation\n scores.\n Basically, it is a list of training-validation scores.\n plot_train: bool\n Indicates whether to plot also the training scores or to plot only the validation ones.\n xlabel: str\n Label of the x axis.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n bar: bool\n Indicates whether to plot the scores using bars or using points.\n If `bar` it's True, `xvalues` must contain string (i.e. labels).\n Returns\n ----------\n matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n "
(fig, ax) = plt.subplots(figsize=figsize)
if (not bar):
if plot_train:
ax.plot(xvalues, train_val_scores[:, 0], 'o:', label='Train')
ax.plot(xvalues, train_val_scores[:, 1], 'o:', label='Validation')
elif plot_train:
x = np.arange(len(xvalues))
width = 0.35
ax.bar((x - (width / 2)), train_val_scores[:, 0], width=width, label='Train')
ax.bar((x + (width / 2)), train_val_scores[:, 1], width=width, label='Validation')
ax.set_xticks(x)
ax.set_xticklabels(xvalues)
else:
ax.bar(xvalues, train_val_scores[:, 1], label='Validation')
ax.set_xlabel(xlabel)
ax.set_title(title)
ax.grid()
ax.legend()
return ax |
def hyperparameter_validation(X, y, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel=None, title='Hyperparameter validation', figsize=(6, 6)):
"\n Select the best value for the specified hyperparameter of the specified model on the given dataset.\n\n In other words, perform the tuning of the `hyperparameter` among the values in `hyperparameter_values`.\n\n This selection is made using the validation score (i.e. the best hyperparameter value is the one with the best validation\n score).\n The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the `hyperparameter_values` can be plotted, making a graphical visualization of the\n selection.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified `hyperparameter`.\n hyperparameter: str\n The name of the hyperparameter that has to be validated.\n hyperparameter_values: list\n List of values for `hyperparameter` that have to be taken into account in the selection.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. dataset indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values.\n plot_train: bool\n Indicates whether to plot also the training scores.\n (It's considered only if `plot` is True).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n xlabel: str\n Label of the x axis of the plot.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n train_val_scores: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of values in `hyperparameter_values` (i.e. number of values to be tested).\n best_index: int\n Index of `hyperparameter_values` that indicates which is the best hyperparameter value.\n test_score: float\n Test score associated with the best hyperparameter value.\n ax: matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n If `plot` is False, then it is None.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n hyperparameter value is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best hyperparameter value is the one associated\n with the maximum validation score.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
param_grid = {hyperparameter: hyperparameter_values}
(params, train_val_scores, best_index, test_score) = hyperparameters_validation(X, y, model, param_grid, scale=scale, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr)
ax = None
if plot:
if (not xvalues):
xvalues = hyperparameter_values
if (not xlabel):
xlabel = hyperparameter
ax = _plot_TrainVal_values(xvalues, train_val_scores, plot_train, xlabel, title, figsize)
return (train_val_scores, best_index, test_score, ax) | -3,417,247,821,585,341,400 | Select the best value for the specified hyperparameter of the specified model on the given dataset.
In other words, perform the tuning of the `hyperparameter` among the values in `hyperparameter_values`.
This selection is made using the validation score (i.e. the best hyperparameter value is the one with the best validation
score).
The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross
validation on the training set.
Additionally, the training and test scores are also computed.
Optionally, the validation scores of the `hyperparameter_values` can be plotted, making a graphical visualization of the
selection.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory features of the dataset.
y: np.array
Mono dimensional np.array, containing the response feature of the dataset.
model: sklearn.base.BaseEstimator
Model which has the specified `hyperparameter`.
hyperparameter: str
The name of the hyperparameter that has to be validated.
hyperparameter_values: list
List of values for `hyperparameter` that have to be taken into account in the selection.
scale: bool
Indicates whether to scale or not the features in `X`.
(The scaling is performed using the sklearn MinMaxScaler).
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set.
time_series: bool
Indicates if the given dataset is a time series dataset (i.e. dataset indexed by days).
(This affects the computing of the validation score).
random_state: int
Used in the training-test splitting of the dataset.
n_folds: int
Indicates how many folds are made in order to compute the k-fold cross validation.
(It's used only if `time_series` is False).
regr: bool
Indicates if it's either a regression or a classification problem.
plot: bool
Indicates whether to plot or not the validation score values.
plot_train: bool
Indicates whether to plot also the training scores.
(It's considered only if `plot` is True).
xvalues: list (in general, iterable)
Values that have to be put in the x axis of the plot.
xlabel: str
Label of the x axis of the plot.
title: str
Title of the plot.
figsize: tuple
Two dimensions of the plot.
Returns
----------
train_val_scores: np.array
Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation
scores.
It has as many rows as the number of values in `hyperparameter_values` (i.e. number of values to be tested).
best_index: int
Index of `hyperparameter_values` that indicates which is the best hyperparameter value.
test_score: float
Test score associated with the best hyperparameter value.
ax: matplotlib.axes.Axes
The matplotlib Axes where the plot has been made.
If `plot` is False, then it is None.
Notes
----------
- If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best
hyperparameter value is the one associated with the minimum validation score.
Otherwise, the validation scores are accuracies: this means that the best hyperparameter value is the one associated
with the maximum validation score.
- If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross
validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.
Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two
contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit. | model_selection.py | hyperparameter_validation | EnricoPittini/model-selection | python | def hyperparameter_validation(X, y, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel=None, title='Hyperparameter validation', figsize=(6, 6)):
"\n Select the best value for the specified hyperparameter of the specified model on the given dataset.\n\n In other words, perform the tuning of the `hyperparameter` among the values in `hyperparameter_values`.\n\n This selection is made using the validation score (i.e. the best hyperparameter value is the one with the best validation\n score).\n The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the `hyperparameter_values` can be plotted, making a graphical visualization of the\n selection.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified `hyperparameter`.\n hyperparameter: str\n The name of the hyperparameter that has to be validated.\n hyperparameter_values: list\n List of values for `hyperparameter` that have to be taken into account in the selection.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. dataset indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values.\n plot_train: bool\n Indicates whether to plot also the training scores.\n (It's considered only if `plot` is True).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n xlabel: str\n Label of the x axis of the plot.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n train_val_scores: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of values in `hyperparameter_values` (i.e. number of values to be tested).\n best_index: int\n Index of `hyperparameter_values` that indicates which is the best hyperparameter value.\n test_score: float\n Test score associated with the best hyperparameter value.\n ax: matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n If `plot` is False, then it is None.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n hyperparameter value is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best hyperparameter value is the one associated\n with the maximum validation score.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
param_grid = {hyperparameter: hyperparameter_values}
(params, train_val_scores, best_index, test_score) = hyperparameters_validation(X, y, model, param_grid, scale=scale, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr)
ax = None
if plot:
if (not xvalues):
xvalues = hyperparameter_values
if (not xlabel):
xlabel = hyperparameter
ax = _plot_TrainVal_values(xvalues, train_val_scores, plot_train, xlabel, title, figsize)
return (train_val_scores, best_index, test_score, ax) |
def hyperparameters_validation(X, y, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n In other words, perform the tuning of multiple hyperparameters.\n The parameter `param_grid` is a dictionary that indicates which are the specified hyperparameters and what are the\n associated values to test.\n\n All the possible combinations of values are tested, in an exhaustive way (i.e. grid search).\n\n This selection is made using the validation score (i.e. the best combination of hyperparameters values is the one with\n the best validation score).\n The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified hyperparameters.\n param_grid: dict\n Dictionary which has as keys the names of the specified hyperparameters and as values the associated list of\n values to test.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. dataframe indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n\n Returns\n ----------\n params: list\n List which enumerates all the possible combinations of hyperparameters values.\n It's a list of dictionaries: each dictionary represents a specific combination of hyperparameters values. (It's a\n dictionary which has as keys the hyperparameters names and as values the specific associated values of that combination).\n train_val_scores: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of possible combinations of the hyperparameters values.\n (It has as many rows as the elements of `params`).\n best_index: int\n Index of `params` that indicates which is the best combination of hyperparameters values.\n test_score: float\n Test score associated with the best combination of hyperparameters values.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n combination of hyperparameters values is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best combination of hyperparameters values is the\n one associated with the maximum validation score.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
if regr:
scoring = 'neg_mean_squared_error'
else:
scoring = 'accuracy'
if (not time_series):
(X_train_80, X_test, y_train_80, y_test) = train_test_split(X, y, test_size=test_size, random_state=random_state)
else:
train_len = int((X.shape[0] * (1 - test_size)))
X_train_80 = X[:train_len]
y_train_80 = y[:train_len]
X_test = X[train_len:]
y_test = y[train_len:]
if scale:
scaler = MinMaxScaler()
scaler.fit(X_train_80)
X_train_80 = scaler.transform(X_train_80)
X_test = scaler.transform(X_test)
if (not time_series):
cv = n_folds
else:
cv = TimeSeriesSplit(n_splits=n_folds)
grid_search = GridSearchCV(model, param_grid, scoring=scoring, cv=cv, return_train_score=True)
grid_search.fit(X_train_80, y_train_80)
params = grid_search.cv_results_['params']
train_scores = grid_search.cv_results_['mean_train_score']
val_scores = grid_search.cv_results_['mean_test_score']
best_index = grid_search.best_index_
best_model = grid_search.best_estimator_
if regr:
train_scores = (train_scores * (- 1))
val_scores = (val_scores * (- 1))
train_val_scores = np.concatenate((train_scores.reshape((- 1), 1), val_scores.reshape((- 1), 1)), axis=1)
best_model.fit(X_train_80, y_train_80)
test_score = 0
if regr:
test_score = mean_squared_error(y_true=y_test, y_pred=best_model.predict(X_test))
else:
test_score = accuracy_score(y_true=y_test, y_pred=best_model.predict(X_test))
return (params, train_val_scores, best_index, test_score) | -5,705,085,024,780,375,000 | Select the best combination of values for the specified hyperparameters of the specified model on the given dataset.
In other words, perform the tuning of multiple hyperparameters.
The parameter `param_grid` is a dictionary that indicates which are the specified hyperparameters and what are the
associated values to test.
All the possible combinations of values are tested, in an exhaustive way (i.e. grid search).
This selection is made using the validation score (i.e. the best combination of hyperparameters values is the one with
the best validation score).
The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross
validation on the training set.
Additionally, the training and test scores are also computed.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory features of the dataset.
y: np.array
Mono dimensional np.array, containing the response feature of the dataset.
model: sklearn.base.BaseEstimator
Model which has the specified hyperparameters.
param_grid: dict
Dictionary which has as keys the names of the specified hyperparameters and as values the associated list of
values to test.
scale: bool
Indicates whether to scale or not the features in `X`.
(The scaling is performed using the sklearn MinMaxScaler).
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set.
time_series: bool
Indicates if the given dataset is a time series dataset (i.e. dataframe indexed by days).
(This affects the computing of the validation score).
random_state: int
Used in the training-test splitting of the dataset.
n_folds: int
Indicates how many folds are made in order to compute the k-fold cross validation.
(It's used only if `time_series` is False).
regr: bool
Indicates if it's either a regression or a classification problem.
Returns
----------
params: list
List which enumerates all the possible combinations of hyperparameters values.
It's a list of dictionaries: each dictionary represents a specific combination of hyperparameters values. (It's a
dictionary which has as keys the hyperparameters names and as values the specific associated values of that combination).
train_val_scores: np.array
Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation
scores.
It has as many rows as the number of possible combinations of the hyperparameters values.
(It has as many rows as the elements of `params`).
best_index: int
Index of `params` that indicates which is the best combination of hyperparameters values.
test_score: float
Test score associated with the best combination of hyperparameters values.
Notes
----------
- If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best
combination of hyperparameters values is the one associated with the minimum validation score.
Otherwise, the validation scores are accuracies: this means that the best combination of hyperparameters values is the
one associated with the maximum validation score.
- If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross
validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.
Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two
contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit. | model_selection.py | hyperparameters_validation | EnricoPittini/model-selection | python | def hyperparameters_validation(X, y, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True):
"\n Select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n In other words, perform the tuning of multiple hyperparameters.\n The parameter `param_grid` is a dictionary that indicates which are the specified hyperparameters and what are the\n associated values to test.\n\n All the possible combinations of values are tested, in an exhaustive way (i.e. grid search).\n\n This selection is made using the validation score (i.e. the best combination of hyperparameters values is the one with\n the best validation score).\n The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified hyperparameters.\n param_grid: dict\n Dictionary which has as keys the names of the specified hyperparameters and as values the associated list of\n values to test.\n scale: bool\n Indicates whether to scale or not the features in `X`.\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. dataframe indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n\n Returns\n ----------\n params: list\n List which enumerates all the possible combinations of hyperparameters values.\n It's a list of dictionaries: each dictionary represents a specific combination of hyperparameters values. (It's a\n dictionary which has as keys the hyperparameters names and as values the specific associated values of that combination).\n train_val_scores: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of possible combinations of the hyperparameters values.\n (It has as many rows as the elements of `params`).\n best_index: int\n Index of `params` that indicates which is the best combination of hyperparameters values.\n test_score: float\n Test score associated with the best combination of hyperparameters values.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n combination of hyperparameters values is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best combination of hyperparameters values is the\n one associated with the maximum validation score.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
if regr:
scoring = 'neg_mean_squared_error'
else:
scoring = 'accuracy'
if (not time_series):
(X_train_80, X_test, y_train_80, y_test) = train_test_split(X, y, test_size=test_size, random_state=random_state)
else:
train_len = int((X.shape[0] * (1 - test_size)))
X_train_80 = X[:train_len]
y_train_80 = y[:train_len]
X_test = X[train_len:]
y_test = y[train_len:]
if scale:
scaler = MinMaxScaler()
scaler.fit(X_train_80)
X_train_80 = scaler.transform(X_train_80)
X_test = scaler.transform(X_test)
if (not time_series):
cv = n_folds
else:
cv = TimeSeriesSplit(n_splits=n_folds)
grid_search = GridSearchCV(model, param_grid, scoring=scoring, cv=cv, return_train_score=True)
grid_search.fit(X_train_80, y_train_80)
params = grid_search.cv_results_['params']
train_scores = grid_search.cv_results_['mean_train_score']
val_scores = grid_search.cv_results_['mean_test_score']
best_index = grid_search.best_index_
best_model = grid_search.best_estimator_
if regr:
train_scores = (train_scores * (- 1))
val_scores = (val_scores * (- 1))
train_val_scores = np.concatenate((train_scores.reshape((- 1), 1), val_scores.reshape((- 1), 1)), axis=1)
best_model.fit(X_train_80, y_train_80)
test_score = 0
if regr:
test_score = mean_squared_error(y_true=y_test, y_pred=best_model.predict(X_test))
else:
test_score = accuracy_score(y_true=y_test, y_pred=best_model.predict(X_test))
return (params, train_val_scores, best_index, test_score) |
def models_validation(X, y, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Models', title='Models validation', figsize=(6, 6)):
"\n Select the best model on the given dataset.\n\n The parameter `model_paramGrid_list` is the list of the models to test. It also contains, for each model, the grid of\n hyperparameters that have to be tested on that model (i.e. the grid which contains the values to test for each\n specified hyperparameter of the model).\n (That grid has the same structure as the `param_grid` parameter of the function `hyperparameters_validation`. See\n `hyperparameters_validation`).\n\n For each specified model, the best combination of hyperparameters values is selected in an exhaustive way (i.e. grid\n search).\n Actually, the function `hyperparameters_validation` is used.\n (See `hyperparameters_validation`).\n\n The selection of the best model is made using the validation score (i.e. the best model is the one with the best\n validation score).\n The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the different models can be plotted, making a graphical visualization of the\n selection.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model_paramGrid_list: list\n List that specifies the models and the relative grids of hyperparameters to be tested.\n It's a list of triples (i.e. tuples), where each triple represents a model:\n - the first element is a string, which is a mnemonic name of that model;\n - the second element is the sklearn model;\n - the third element is the grid of hyperparameters to test for that model. It's a dictionary, with the same\n structure of the parameter `param_grid` of the function `hyperparameters_validation`.\n scale_list: list or bool\n List of booleans, which has as many elements as the models to test (i.e. as the elements of the\n `model_paramGrid_list` list).\n This list indicates, for each different model, if the features in `X` have to be scaled or not.\n `scale_list` can be None or False: in this case the `X` features aren't scaled for any model. `scale_list` can be\n True: in this case the `X` features are scaled for all the models.\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. dataset indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values.\n plot_train: bool\n Indicates whether to plot also the training scores.\n (It's considered only if `plot` is True).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n xlabel: str\n Label of the x axis of the plot.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n models_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of models to test (i.e. number of elements in the `model_paramGrid_list` list).\n models_best_params: list\n List which indicates, for each model, the best combination of the hyperparameters values for that model.\n It has as many elements as the models to test (i.e. as the elements of the `model_paramGrid_list` list), and it\n contains dictionaries: each dictionary represents the best combination of the hyperparameters values for the\n associated model.\n best_index: int\n Index of `model_paramGrid_list` that indicates which is the best model.\n test_score: float\n Test score associated with the best model.\n ax: matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n If `plot` is False, then it is None.\n\n See also\n ----------\n hyperparameters_validation:\n select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n model is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best model is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
if (not scale_list):
scale_list = ([False] * len(model_paramGrid_list))
elif (scale_list is True):
scale_list = ([True] * len(model_paramGrid_list))
models_train_val_score = []
models_best_params = []
models_test_score = []
for (i, triple) in enumerate(model_paramGrid_list):
(model, param_grid) = triple[1:]
(params, train_val_scores, best_index, test_score) = hyperparameters_validation(X, y, model, param_grid, scale=scale_list[i], test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr)
models_train_val_score.append(tuple(train_val_scores[best_index]))
models_best_params.append(params[best_index])
models_test_score.append(test_score)
models_train_val_score = np.array(models_train_val_score)
if regr:
best_index = np.argmin(models_train_val_score, axis=0)[1]
else:
best_index = np.argmax(models_train_val_score, axis=0)[1]
test_score = models_test_score[best_index]
ax = None
if plot:
if (not xvalues):
xvalues = [model_paramGrid_list[i][0] for i in range(len(model_paramGrid_list))]
ax = _plot_TrainVal_values(xvalues, models_train_val_score, plot_train, xlabel, title, figsize, bar=True)
return (models_train_val_score, models_best_params, best_index, test_score, ax) | -7,523,235,934,046,416,000 | Select the best model on the given dataset.
The parameter `model_paramGrid_list` is the list of the models to test. It also contains, for each model, the grid of
hyperparameters that have to be tested on that model (i.e. the grid which contains the values to test for each
specified hyperparameter of the model).
(That grid has the same structure as the `param_grid` parameter of the function `hyperparameters_validation`. See
`hyperparameters_validation`).
For each specified model, the best combination of hyperparameters values is selected in an exhaustive way (i.e. grid
search).
Actually, the function `hyperparameters_validation` is used.
(See `hyperparameters_validation`).
The selection of the best model is made using the validation score (i.e. the best model is the one with the best
validation score).
The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross
validation on the training set.
Additionally, the training and test scores are also computed.
Optionally, the validation scores of the different models can be plotted, making a graphical visualization of the
selection.
Parameters
----------
X: np.array
Two-dimensional np.array, containing the explanatory features of the dataset.
y: np.array
Mono dimensional np.array, containing the response feature of the dataset.
model_paramGrid_list: list
List that specifies the models and the relative grids of hyperparameters to be tested.
It's a list of triples (i.e. tuples), where each triple represents a model:
- the first element is a string, which is a mnemonic name of that model;
- the second element is the sklearn model;
- the third element is the grid of hyperparameters to test for that model. It's a dictionary, with the same
structure of the parameter `param_grid` of the function `hyperparameters_validation`.
scale_list: list or bool
List of booleans, which has as many elements as the models to test (i.e. as the elements of the
`model_paramGrid_list` list).
This list indicates, for each different model, if the features in `X` have to be scaled or not.
`scale_list` can be None or False: in this case the `X` features aren't scaled for any model. `scale_list` can be
True: in this case the `X` features are scaled for all the models.
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set.
time_series: bool
Indicates if the given dataset is a time series dataset (i.e. dataset indexed by days).
(This affects the computing of the validation score).
random_state: int
Used in the training-test splitting of the dataset.
n_folds: int
Indicates how many folds are made in order to compute the k-fold cross validation.
(It's used only if `time_series` is False).
regr: bool
Indicates if it's either a regression or a classification problem.
plot: bool
Indicates whether to plot or not the validation score values.
plot_train: bool
Indicates whether to plot also the training scores.
(It's considered only if `plot` is True).
xvalues: list (in general, iterable)
Values that have to be put in the x axis of the plot.
xlabel: str
Label of the x axis of the plot.
title: str
Title of the plot.
figsize: tuple
Two dimensions of the plot.
Returns
----------
models_train_val_score: np.array
Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation
scores.
It has as many rows as the number of models to test (i.e. number of elements in the `model_paramGrid_list` list).
models_best_params: list
List which indicates, for each model, the best combination of the hyperparameters values for that model.
It has as many elements as the models to test (i.e. as the elements of the `model_paramGrid_list` list), and it
contains dictionaries: each dictionary represents the best combination of the hyperparameters values for the
associated model.
best_index: int
Index of `model_paramGrid_list` that indicates which is the best model.
test_score: float
Test score associated with the best model.
ax: matplotlib.axes.Axes
The matplotlib Axes where the plot has been made.
If `plot` is False, then it is None.
See also
----------
hyperparameters_validation:
select the best combination of values for the specified hyperparameters of the specified model on the given dataset.
Notes
----------
- If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best
model is the one associated with the minimum validation score.
Otherwise, the validation scores are accuracies: this means that the best model is the one associated with the
maximum validation score.
- If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross
validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.
Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two
contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit. | model_selection.py | models_validation | EnricoPittini/model-selection | python | def models_validation(X, y, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Models', title='Models validation', figsize=(6, 6)):
"\n Select the best model on the given dataset.\n\n The parameter `model_paramGrid_list` is the list of the models to test. It also contains, for each model, the grid of\n hyperparameters that have to be tested on that model (i.e. the grid which contains the values to test for each\n specified hyperparameter of the model).\n (That grid has the same structure as the `param_grid` parameter of the function `hyperparameters_validation`. See\n `hyperparameters_validation`).\n\n For each specified model, the best combination of hyperparameters values is selected in an exhaustive way (i.e. grid\n search).\n Actually, the function `hyperparameters_validation` is used.\n (See `hyperparameters_validation`).\n\n The selection of the best model is made using the validation score (i.e. the best model is the one with the best\n validation score).\n The validation score is computed by splitting the dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the different models can be plotted, making a graphical visualization of the\n selection.\n\n Parameters\n ----------\n X: np.array\n Two-dimensional np.array, containing the explanatory features of the dataset.\n y: np.array\n Mono dimensional np.array, containing the response feature of the dataset.\n model_paramGrid_list: list\n List that specifies the models and the relative grids of hyperparameters to be tested.\n It's a list of triples (i.e. tuples), where each triple represents a model:\n - the first element is a string, which is a mnemonic name of that model;\n - the second element is the sklearn model;\n - the third element is the grid of hyperparameters to test for that model. It's a dictionary, with the same\n structure of the parameter `param_grid` of the function `hyperparameters_validation`.\n scale_list: list or bool\n List of booleans, which has as many elements as the models to test (i.e. as the elements of the\n `model_paramGrid_list` list).\n This list indicates, for each different model, if the features in `X` have to be scaled or not.\n `scale_list` can be None or False: in this case the `X` features aren't scaled for any model. `scale_list` can be\n True: in this case the `X` features are scaled for all the models.\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set.\n time_series: bool\n Indicates if the given dataset is a time series dataset (i.e. dataset indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the dataset.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values.\n plot_train: bool\n Indicates whether to plot also the training scores.\n (It's considered only if `plot` is True).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n xlabel: str\n Label of the x axis of the plot.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n models_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of models to test (i.e. number of elements in the `model_paramGrid_list` list).\n models_best_params: list\n List which indicates, for each model, the best combination of the hyperparameters values for that model.\n It has as many elements as the models to test (i.e. as the elements of the `model_paramGrid_list` list), and it\n contains dictionaries: each dictionary represents the best combination of the hyperparameters values for the\n associated model.\n best_index: int\n Index of `model_paramGrid_list` that indicates which is the best model.\n test_score: float\n Test score associated with the best model.\n ax: matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n If `plot` is False, then it is None.\n\n See also\n ----------\n hyperparameters_validation:\n select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n model is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best model is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of the dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting the dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
if (not scale_list):
scale_list = ([False] * len(model_paramGrid_list))
elif (scale_list is True):
scale_list = ([True] * len(model_paramGrid_list))
models_train_val_score = []
models_best_params = []
models_test_score = []
for (i, triple) in enumerate(model_paramGrid_list):
(model, param_grid) = triple[1:]
(params, train_val_scores, best_index, test_score) = hyperparameters_validation(X, y, model, param_grid, scale=scale_list[i], test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr)
models_train_val_score.append(tuple(train_val_scores[best_index]))
models_best_params.append(params[best_index])
models_test_score.append(test_score)
models_train_val_score = np.array(models_train_val_score)
if regr:
best_index = np.argmin(models_train_val_score, axis=0)[1]
else:
best_index = np.argmax(models_train_val_score, axis=0)[1]
test_score = models_test_score[best_index]
ax = None
if plot:
if (not xvalues):
xvalues = [model_paramGrid_list[i][0] for i in range(len(model_paramGrid_list))]
ax = _plot_TrainVal_values(xvalues, models_train_val_score, plot_train, xlabel, title, figsize, bar=True)
return (models_train_val_score, models_best_params, best_index, test_score, ax) |
def datasets_hyperparameter_validation(dataset_list, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_verbose=(6, 6)):
"\n Select the best dataset and the best value for the specified hyperparameter of the specified model (i.e. select the best\n couple dataset-hyperparameter value).\n\n For each dataset in `dataset_list`, all the specified values `hyperparameter_values` are tested for the specified\n `hyperparameter` of `model`.\n In other words, on each dataset the tuning of `hyperparameter` is performed: in fact, on each dataset, the function\n `hyperparameter_validation` is applied. (See `hyperparameter_validation`).\n In the end, the best couple dataset-hyperparameter value is selected.\n\n Despite the fact that a couple dataset-hyperparameter value is selected, the main viewpoint is focused with respect to\n the datasets. It's a validation focused on the datasets.\n In fact, first of all, for each dataset the hyperparameter tuning is performed: in this way the best value is selected\n and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other words, on each\n dataset the function `hyperparameter_validation` is applied). Finally, after that, the best dataset is selected.\n It's a two-levels selection.\n\n This selection is made using the validation score (i.e. the best couple dataset-hyperparameter value is the one with the\n best validation score).\n The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset\n selection. This is the 'main' plot.\n Moreover, still optionally, the 'secondary' plots can be done: for each dataset, the validation scores of the\n `hyperparameter_values` are plotted, making a graphical visualization of the hyperparameter tuning on that dataset.\n (As the plot made by the `hyperparameter_validation` function).\n\n Parameters\n ----------\n dataset_list: list\n List of couples, where each couple is a dataset.\n - The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.\n - The second element is y, the mono dimensional np.array containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified `hyperparameter`.\n hyperparameter: str\n The name of the hyperparameter that has to be validated.\n hyperparameter_values: list\n List of values for `hyperparameter` that have to be taken into account in the selection.\n scale: bool\n Indicates whether to scale or not the features in 'X' (for all the datasets).\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).\n time_series: bool\n Indicates if the given datasets are time series dataset (i.e. datasets indexed by days).\n (This affects the computing of the validation scores).\n random_state: int\n Used in the training-test splitting of the datasets.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values of the datasets (i.e. this is the 'main' plot).\n plot_train: bool\n Indicates whether to plot also the training scores (both in the 'main' and 'secondary' plots).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the 'main' plot.\n xlabel: str\n Label of the x axis of the 'main' plot.\n title: str\n Title of the 'main' plot.\n figsize: tuple\n Two dimensions of the 'main' plot.\n verbose: bool\n If True, for each dataset are plotted the validation scores of the hyperparameter tuning (these are the 'secondary'\n plots).\n (See 'hyperparameter_validation').\n figsize_verbose: tuple\n Two dimensions of the 'secondary' plots.\n\n Returns\n ----------\n datasets_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.\n datasets_best_hyperparameter_value: list\n List which has as many elements as the number of the datasets (i.e. as the number of elements in `dataset_list`). For\n each dataset, it contains the best `hyperparameter` value on that dataset.\n best_index: int\n Index of `dataset_list` that indicates which is the best dataset.\n test_score: float\n Test score associated with the best couple dataset-hyperparameter value.\n axes: list\n List of the matplotlib Axes where the plots have been made.\n Firstly, the 'secondary' plots are put (if any). And, as last, the 'main' plot is put (if any).\n If no plot has been made, `axes` is an empty list.\n\n See also\n ----------\n hyperparameter_validation:\n select the best value for the specified hyperparameter of the specified model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n couple dataset-hyperparameter value is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
datasets_train_val_score = []
datasets_best_hyperparameter_value = []
datasets_test_score = []
axes = []
for (i, dataset) in enumerate(dataset_list):
(X, y) = dataset
(train_val_scores, best_index, test_score, ax) = hyperparameter_validation(X, y, model, hyperparameter, hyperparameter_values, scale=scale, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr, plot=verbose, plot_train=plot_train, xvalues=hyperparameter_values, xlabel=hyperparameter, title=(('Dataset ' + str(i)) + ' : hyperparameter validation'), figsize=figsize_verbose)
datasets_train_val_score.append(tuple(train_val_scores[best_index, :]))
datasets_best_hyperparameter_value.append(hyperparameter_values[best_index])
datasets_test_score.append(test_score)
if ax:
axes.append(ax)
datasets_train_val_score = np.array(datasets_train_val_score)
if regr:
best_index = np.argmin(datasets_train_val_score, axis=0)[1]
else:
best_index = np.argmax(datasets_train_val_score, axis=0)[1]
test_score = datasets_test_score[best_index]
if plot:
if (not xvalues):
xvalues = range(len(dataset_list))
ax = _plot_TrainVal_values(xvalues, datasets_train_val_score, plot_train, xlabel, title, figsize, bar=True)
axes.append(ax)
return (datasets_train_val_score, datasets_best_hyperparameter_value, best_index, test_score, axes) | -8,298,506,111,670,680,000 | Select the best dataset and the best value for the specified hyperparameter of the specified model (i.e. select the best
couple dataset-hyperparameter value).
For each dataset in `dataset_list`, all the specified values `hyperparameter_values` are tested for the specified
`hyperparameter` of `model`.
In other words, on each dataset the tuning of `hyperparameter` is performed: in fact, on each dataset, the function
`hyperparameter_validation` is applied. (See `hyperparameter_validation`).
In the end, the best couple dataset-hyperparameter value is selected.
Despite the fact that a couple dataset-hyperparameter value is selected, the main viewpoint is focused with respect to
the datasets. It's a validation focused on the datasets.
In fact, first of all, for each dataset the hyperparameter tuning is performed: in this way the best value is selected
and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other words, on each
dataset the function `hyperparameter_validation` is applied). Finally, after that, the best dataset is selected.
It's a two-levels selection.
This selection is made using the validation score (i.e. the best couple dataset-hyperparameter value is the one with the
best validation score).
The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross
validation on the training set.
Additionally, the training and test scores are also computed.
Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset
selection. This is the 'main' plot.
Moreover, still optionally, the 'secondary' plots can be done: for each dataset, the validation scores of the
`hyperparameter_values` are plotted, making a graphical visualization of the hyperparameter tuning on that dataset.
(As the plot made by the `hyperparameter_validation` function).
Parameters
----------
dataset_list: list
List of couples, where each couple is a dataset.
- The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.
- The second element is y, the mono dimensional np.array containing the response feature of the dataset.
model: sklearn.base.BaseEstimator
Model which has the specified `hyperparameter`.
hyperparameter: str
The name of the hyperparameter that has to be validated.
hyperparameter_values: list
List of values for `hyperparameter` that have to be taken into account in the selection.
scale: bool
Indicates whether to scale or not the features in 'X' (for all the datasets).
(The scaling is performed using the sklearn MinMaxScaler).
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).
time_series: bool
Indicates if the given datasets are time series dataset (i.e. datasets indexed by days).
(This affects the computing of the validation scores).
random_state: int
Used in the training-test splitting of the datasets.
n_folds: int
Indicates how many folds are made in order to compute the k-fold cross validation.
(It's used only if `time_series` is False).
regr: bool
Indicates if it's either a regression or a classification problem.
plot: bool
Indicates whether to plot or not the validation score values of the datasets (i.e. this is the 'main' plot).
plot_train: bool
Indicates whether to plot also the training scores (both in the 'main' and 'secondary' plots).
xvalues: list (in general, iterable)
Values that have to be put in the x axis of the 'main' plot.
xlabel: str
Label of the x axis of the 'main' plot.
title: str
Title of the 'main' plot.
figsize: tuple
Two dimensions of the 'main' plot.
verbose: bool
If True, for each dataset are plotted the validation scores of the hyperparameter tuning (these are the 'secondary'
plots).
(See 'hyperparameter_validation').
figsize_verbose: tuple
Two dimensions of the 'secondary' plots.
Returns
----------
datasets_train_val_score: np.array
Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation
scores.
It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.
datasets_best_hyperparameter_value: list
List which has as many elements as the number of the datasets (i.e. as the number of elements in `dataset_list`). For
each dataset, it contains the best `hyperparameter` value on that dataset.
best_index: int
Index of `dataset_list` that indicates which is the best dataset.
test_score: float
Test score associated with the best couple dataset-hyperparameter value.
axes: list
List of the matplotlib Axes where the plots have been made.
Firstly, the 'secondary' plots are put (if any). And, as last, the 'main' plot is put (if any).
If no plot has been made, `axes` is an empty list.
See also
----------
hyperparameter_validation:
select the best value for the specified hyperparameter of the specified model on the given dataset.
Notes
----------
- If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best
couple dataset-hyperparameter value is the one associated with the minimum validation score.
Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the
maximum validation score.
- If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross
validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.
Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two
contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit. | model_selection.py | datasets_hyperparameter_validation | EnricoPittini/model-selection | python | def datasets_hyperparameter_validation(dataset_list, model, hyperparameter, hyperparameter_values, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_verbose=(6, 6)):
"\n Select the best dataset and the best value for the specified hyperparameter of the specified model (i.e. select the best\n couple dataset-hyperparameter value).\n\n For each dataset in `dataset_list`, all the specified values `hyperparameter_values` are tested for the specified\n `hyperparameter` of `model`.\n In other words, on each dataset the tuning of `hyperparameter` is performed: in fact, on each dataset, the function\n `hyperparameter_validation` is applied. (See `hyperparameter_validation`).\n In the end, the best couple dataset-hyperparameter value is selected.\n\n Despite the fact that a couple dataset-hyperparameter value is selected, the main viewpoint is focused with respect to\n the datasets. It's a validation focused on the datasets.\n In fact, first of all, for each dataset the hyperparameter tuning is performed: in this way the best value is selected\n and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other words, on each\n dataset the function `hyperparameter_validation` is applied). Finally, after that, the best dataset is selected.\n It's a two-levels selection.\n\n This selection is made using the validation score (i.e. the best couple dataset-hyperparameter value is the one with the\n best validation score).\n The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset\n selection. This is the 'main' plot.\n Moreover, still optionally, the 'secondary' plots can be done: for each dataset, the validation scores of the\n `hyperparameter_values` are plotted, making a graphical visualization of the hyperparameter tuning on that dataset.\n (As the plot made by the `hyperparameter_validation` function).\n\n Parameters\n ----------\n dataset_list: list\n List of couples, where each couple is a dataset.\n - The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.\n - The second element is y, the mono dimensional np.array containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified `hyperparameter`.\n hyperparameter: str\n The name of the hyperparameter that has to be validated.\n hyperparameter_values: list\n List of values for `hyperparameter` that have to be taken into account in the selection.\n scale: bool\n Indicates whether to scale or not the features in 'X' (for all the datasets).\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).\n time_series: bool\n Indicates if the given datasets are time series dataset (i.e. datasets indexed by days).\n (This affects the computing of the validation scores).\n random_state: int\n Used in the training-test splitting of the datasets.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values of the datasets (i.e. this is the 'main' plot).\n plot_train: bool\n Indicates whether to plot also the training scores (both in the 'main' and 'secondary' plots).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the 'main' plot.\n xlabel: str\n Label of the x axis of the 'main' plot.\n title: str\n Title of the 'main' plot.\n figsize: tuple\n Two dimensions of the 'main' plot.\n verbose: bool\n If True, for each dataset are plotted the validation scores of the hyperparameter tuning (these are the 'secondary'\n plots).\n (See 'hyperparameter_validation').\n figsize_verbose: tuple\n Two dimensions of the 'secondary' plots.\n\n Returns\n ----------\n datasets_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.\n datasets_best_hyperparameter_value: list\n List which has as many elements as the number of the datasets (i.e. as the number of elements in `dataset_list`). For\n each dataset, it contains the best `hyperparameter` value on that dataset.\n best_index: int\n Index of `dataset_list` that indicates which is the best dataset.\n test_score: float\n Test score associated with the best couple dataset-hyperparameter value.\n axes: list\n List of the matplotlib Axes where the plots have been made.\n Firstly, the 'secondary' plots are put (if any). And, as last, the 'main' plot is put (if any).\n If no plot has been made, `axes` is an empty list.\n\n See also\n ----------\n hyperparameter_validation:\n select the best value for the specified hyperparameter of the specified model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n couple dataset-hyperparameter value is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
datasets_train_val_score = []
datasets_best_hyperparameter_value = []
datasets_test_score = []
axes = []
for (i, dataset) in enumerate(dataset_list):
(X, y) = dataset
(train_val_scores, best_index, test_score, ax) = hyperparameter_validation(X, y, model, hyperparameter, hyperparameter_values, scale=scale, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr, plot=verbose, plot_train=plot_train, xvalues=hyperparameter_values, xlabel=hyperparameter, title=(('Dataset ' + str(i)) + ' : hyperparameter validation'), figsize=figsize_verbose)
datasets_train_val_score.append(tuple(train_val_scores[best_index, :]))
datasets_best_hyperparameter_value.append(hyperparameter_values[best_index])
datasets_test_score.append(test_score)
if ax:
axes.append(ax)
datasets_train_val_score = np.array(datasets_train_val_score)
if regr:
best_index = np.argmin(datasets_train_val_score, axis=0)[1]
else:
best_index = np.argmax(datasets_train_val_score, axis=0)[1]
test_score = datasets_test_score[best_index]
if plot:
if (not xvalues):
xvalues = range(len(dataset_list))
ax = _plot_TrainVal_values(xvalues, datasets_train_val_score, plot_train, xlabel, title, figsize, bar=True)
axes.append(ax)
return (datasets_train_val_score, datasets_best_hyperparameter_value, best_index, test_score, axes) |
def datasets_hyperparameters_validation(dataset_list, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6)):
"\n Select the best dataset and the best combination of values for the specified hyperparameters of the specified model (i.e.\n select the best couple dataset-combination of hyperparameters values).\n\n For each dataset in `dataset_list`, all the possible combinations of the hyperparameters values for `model` (specified\n with `param_grid`) are tested.\n In other words, on each dataset the tuning of the specified hyperparameters is performed in an exhaustive way: in fact,\n on each dataset, the function `hyperparameters_validation` is applied. (See `hyperparameters_validation`).\n In the end, the best couple dataset-combination of hyperparameters values is selected.\n\n Despite the fact that a couple dataset-combination of hyperparameters values is selected, the main viewpoint is focused\n with respect to the datasets. It's a validation focused on the datasets.\n In fact, first of all, for each dataset the hyperparameters tuning is performed: in this way the best combination of\n values is selected and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other\n words, on each dataset the function `hyperparameters_validation` is applied). Finally, after that, the best dataset is\n selected. It's a two-levels selection.\n\n This selection is made using the validation score (i.e. the best couple dataset-combination of hyperparameters values, is\n the one with best validation score).\n The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset\n selection.\n\n Parameters\n ----------\n dataset_list: list\n List of couple, where each couple is a dataset.\n - The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.\n - The second element is y, the mono dimensional np.array containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified hyperparameters.\n param_grid: dict\n Dictionary which has as keys the names of the specified hyperparameters and as values the associated list of\n values to test.\n scale: bool\n Indicates whether to scale or not the features in 'X' (for all the datasets).\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).\n time_series: bool\n Indicates if the given datasets are time series datasets (i.e. datasets indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the datasets.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values of the datasets.\n plot_train: bool\n Indicates whether to plot also the training scores.\n (It's considered only if `plot` is True).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n xlabel: str\n Label of the x axis of the plot.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n datasets_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.\n datasets_best_params: list\n List which has as many elements as the number of the datasets (i.e. as the number of elements in `dataset_list`). For\n each dataset, it contains the best combination of hyperparameters values on that dataset.\n Each combination is represented as a dictionary, with keys the hyperparameters names and values the associated\n values.\n best_index: int\n Index of `dataset_list` that indicates which is the best dataset.\n test_score: float\n Test score associated with the best couple dataset-combination of hyperparameters values.\n ax: matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n\n See also\n ----------\n hyperparameters_validation:\n select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n couple dataset-combination of hyperparameters values is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
datasets_train_val_score = []
datasets_best_params = []
datasets_test_score = []
for (X, y) in dataset_list:
(params, train_val_scores, best_index, test_score) = hyperparameters_validation(X, y, model, param_grid, scale=scale, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr)
datasets_train_val_score.append(tuple(train_val_scores[best_index, :]))
datasets_best_params.append(params[best_index])
datasets_test_score.append(test_score)
datasets_train_val_score = np.array(datasets_train_val_score)
if regr:
best_index = np.argmin(datasets_train_val_score, axis=0)[1]
else:
best_index = np.argmax(datasets_train_val_score, axis=0)[1]
test_score = datasets_test_score[best_index]
ax = None
if plot:
if (not xvalues):
xvalues = range(len(dataset_list))
ax = _plot_TrainVal_values(xvalues, datasets_train_val_score, plot_train, xlabel, title, figsize, bar=True)
return (datasets_train_val_score, datasets_best_params, best_index, test_score, ax) | -182,712,746,719,899,140 | Select the best dataset and the best combination of values for the specified hyperparameters of the specified model (i.e.
select the best couple dataset-combination of hyperparameters values).
For each dataset in `dataset_list`, all the possible combinations of the hyperparameters values for `model` (specified
with `param_grid`) are tested.
In other words, on each dataset the tuning of the specified hyperparameters is performed in an exhaustive way: in fact,
on each dataset, the function `hyperparameters_validation` is applied. (See `hyperparameters_validation`).
In the end, the best couple dataset-combination of hyperparameters values is selected.
Despite the fact that a couple dataset-combination of hyperparameters values is selected, the main viewpoint is focused
with respect to the datasets. It's a validation focused on the datasets.
In fact, first of all, for each dataset the hyperparameters tuning is performed: in this way the best combination of
values is selected and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other
words, on each dataset the function `hyperparameters_validation` is applied). Finally, after that, the best dataset is
selected. It's a two-levels selection.
This selection is made using the validation score (i.e. the best couple dataset-combination of hyperparameters values, is
the one with best validation score).
The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross
validation on the training set.
Additionally, the training and test scores are also computed.
Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset
selection.
Parameters
----------
dataset_list: list
List of couple, where each couple is a dataset.
- The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.
- The second element is y, the mono dimensional np.array containing the response feature of the dataset.
model: sklearn.base.BaseEstimator
Model which has the specified hyperparameters.
param_grid: dict
Dictionary which has as keys the names of the specified hyperparameters and as values the associated list of
values to test.
scale: bool
Indicates whether to scale or not the features in 'X' (for all the datasets).
(The scaling is performed using the sklearn MinMaxScaler).
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).
time_series: bool
Indicates if the given datasets are time series datasets (i.e. datasets indexed by days).
(This affects the computing of the validation score).
random_state: int
Used in the training-test splitting of the datasets.
n_folds: int
Indicates how many folds are made in order to compute the k-fold cross validation.
(It's used only if `time_series` is False).
regr: bool
Indicates if it's either a regression or a classification problem.
plot: bool
Indicates whether to plot or not the validation score values of the datasets.
plot_train: bool
Indicates whether to plot also the training scores.
(It's considered only if `plot` is True).
xvalues: list (in general, iterable)
Values that have to be put in the x axis of the plot.
xlabel: str
Label of the x axis of the plot.
title: str
Title of the plot.
figsize: tuple
Two dimensions of the plot.
Returns
----------
datasets_train_val_score: np.array
Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation
scores.
It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.
datasets_best_params: list
List which has as many elements as the number of the datasets (i.e. as the number of elements in `dataset_list`). For
each dataset, it contains the best combination of hyperparameters values on that dataset.
Each combination is represented as a dictionary, with keys the hyperparameters names and values the associated
values.
best_index: int
Index of `dataset_list` that indicates which is the best dataset.
test_score: float
Test score associated with the best couple dataset-combination of hyperparameters values.
ax: matplotlib.axes.Axes
The matplotlib Axes where the plot has been made.
See also
----------
hyperparameters_validation:
select the best combination of values for the specified hyperparameters of the specified model on the given dataset.
Notes
----------
- If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best
couple dataset-combination of hyperparameters values is the one associated with the minimum validation score.
Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the
maximum validation score.
- If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross
validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.
Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two
contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit. | model_selection.py | datasets_hyperparameters_validation | EnricoPittini/model-selection | python | def datasets_hyperparameters_validation(dataset_list, model, param_grid, scale=False, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6)):
"\n Select the best dataset and the best combination of values for the specified hyperparameters of the specified model (i.e.\n select the best couple dataset-combination of hyperparameters values).\n\n For each dataset in `dataset_list`, all the possible combinations of the hyperparameters values for `model` (specified\n with `param_grid`) are tested.\n In other words, on each dataset the tuning of the specified hyperparameters is performed in an exhaustive way: in fact,\n on each dataset, the function `hyperparameters_validation` is applied. (See `hyperparameters_validation`).\n In the end, the best couple dataset-combination of hyperparameters values is selected.\n\n Despite the fact that a couple dataset-combination of hyperparameters values is selected, the main viewpoint is focused\n with respect to the datasets. It's a validation focused on the datasets.\n In fact, first of all, for each dataset the hyperparameters tuning is performed: in this way the best combination of\n values is selected and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other\n words, on each dataset the function `hyperparameters_validation` is applied). Finally, after that, the best dataset is\n selected. It's a two-levels selection.\n\n This selection is made using the validation score (i.e. the best couple dataset-combination of hyperparameters values, is\n the one with best validation score).\n The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset\n selection.\n\n Parameters\n ----------\n dataset_list: list\n List of couple, where each couple is a dataset.\n - The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.\n - The second element is y, the mono dimensional np.array containing the response feature of the dataset.\n model: sklearn.base.BaseEstimator\n Model which has the specified hyperparameters.\n param_grid: dict\n Dictionary which has as keys the names of the specified hyperparameters and as values the associated list of\n values to test.\n scale: bool\n Indicates whether to scale or not the features in 'X' (for all the datasets).\n (The scaling is performed using the sklearn MinMaxScaler).\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).\n time_series: bool\n Indicates if the given datasets are time series datasets (i.e. datasets indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the datasets.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values of the datasets.\n plot_train: bool\n Indicates whether to plot also the training scores.\n (It's considered only if `plot` is True).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the plot.\n xlabel: str\n Label of the x axis of the plot.\n title: str\n Title of the plot.\n figsize: tuple\n Two dimensions of the plot.\n\n Returns\n ----------\n datasets_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.\n datasets_best_params: list\n List which has as many elements as the number of the datasets (i.e. as the number of elements in `dataset_list`). For\n each dataset, it contains the best combination of hyperparameters values on that dataset.\n Each combination is represented as a dictionary, with keys the hyperparameters names and values the associated\n values.\n best_index: int\n Index of `dataset_list` that indicates which is the best dataset.\n test_score: float\n Test score associated with the best couple dataset-combination of hyperparameters values.\n ax: matplotlib.axes.Axes\n The matplotlib Axes where the plot has been made.\n\n See also\n ----------\n hyperparameters_validation:\n select the best combination of values for the specified hyperparameters of the specified model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n couple dataset-combination of hyperparameters values is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
datasets_train_val_score = []
datasets_best_params = []
datasets_test_score = []
for (X, y) in dataset_list:
(params, train_val_scores, best_index, test_score) = hyperparameters_validation(X, y, model, param_grid, scale=scale, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr)
datasets_train_val_score.append(tuple(train_val_scores[best_index, :]))
datasets_best_params.append(params[best_index])
datasets_test_score.append(test_score)
datasets_train_val_score = np.array(datasets_train_val_score)
if regr:
best_index = np.argmin(datasets_train_val_score, axis=0)[1]
else:
best_index = np.argmax(datasets_train_val_score, axis=0)[1]
test_score = datasets_test_score[best_index]
ax = None
if plot:
if (not xvalues):
xvalues = range(len(dataset_list))
ax = _plot_TrainVal_values(xvalues, datasets_train_val_score, plot_train, xlabel, title, figsize, bar=True)
return (datasets_train_val_score, datasets_best_params, best_index, test_score, ax) |
def datasets_models_validation(dataset_list, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_verbose=(6, 6)):
"\n Select the best dataset and the best model (i.e. select the best couple dataset-model).\n\n For each dataset in `dataset_list`, all the models in `model_paramGrid_list` are tested: each model is tested performing\n an exhaustive tuning of the specified hyperparameters. In fact, `model_paramGrid_list` also contains, for each model, the\n grid of the hyperparameters that have to be tested on that model (i.e. the grid which contains the values to test for\n each specified hyperparameter of the model).\n In other words, on each dataset the selection of the best model is performed: in fact, on each dataset, the function\n `models_validation` is applied. (See `models_validation`).\n In the end, the best couple dataset-model is selected.\n\n Despite the fact that a couple dataset-model is selected, the main viewpoint is focused with respect to the datasets.\n It's a validation focused on the datasets.\n In fact, first of all, for each dataset the model selection is performed: in this way the best model is selected\n and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other words, on each\n dataset the function `models_validation` is applied). Finally, after that, the best dataset is selected.\n It's a two-levels selection.\n\n This selection is made using the validation score (i.e. the best couple dataset-model is the one with best validation\n score).\n The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset\n selection. This is the 'main' plot.\n Moreover, still optionally, the 'secondary' plots can be done: for each dataset, the validation scores of the models are\n plotted, making a graphical visualization of the models selection on that dataset. (As the plot made by the\n `models_validation` function).\n\n Parameters\n ----------\n dataset_list: list\n List of couples, where each couple is a dataset.\n - The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.\n - The second element is y, the mono dimensional np.array containing the response feature of the dataset.\n model_paramGrid_list: list\n List that specifies the models and the relative grid of hyperparameters to be tested.\n It's a list of triples (i.e. tuples), where each triple represents a model:\n - the first element is a string, which is a mnemonic name of that model;\n - the second element is the sklearn model;\n - the third element is the grid of hyperparameters to test for that model. It's a dictionary, with the same\n structure of parameter `param_grid` of the function `hyperparameters_validation`.\n scale_list: list or bool\n List of booleans, which has as many elements as the number of models to test (i.e. number of elements in the\n `model_paramGrid_list` list).\n This list indicates, for each different model, if the features in 'X' have to be scaled or not (for all the datasets).\n `scale_list` can be None or False: in this case the 'X' features aren't scaled for any model. `scale_list` can be\n True: in this case the 'X' features are scaled for all the models.\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).\n time_series: bool\n Indicates if the given datasets are time series dataset (i.e. datasets indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the datasets.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values of the datasets (i.e. this is the 'main' plot).\n plot_train: bool\n Indicates whether to plot also the training scores (both in the 'main' and 'secondary' plots).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the 'main' plot.\n xlabel: str\n Label of the x axis of the 'main' plot.\n title: str\n Title of the 'main' plot.\n figsize: tuple\n Two dimensions of the 'main' plot.\n verbose: bool\n If True, for each dataset the validation scores of the models are plotted (i.e. these are the 'secondary' plots).\n (See 'models_validation').\n figsize_verbose: tuple\n Two dimensions of the 'secondary' plots.\n\n Returns\n ----------\n datasets_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.\n datasets_best_model: list\n List which has as many elements as the number of the datasets (i.e. number of elements in `dataset_list`). For\n each dataset, it contains the best model for that dataset.\n More precisely, it is a list of triple:\n - the first element is the index of `model_paramGrid_list` which indicates the best model;\n - the second element is the mnemonic name of the best model;\n - the third element is the best combination of hyperparameters values on that best model (i.e. it's a dictionary\n which has as keys the hyperparameters names and as values their associated values).\n best_index: int\n Index of `dataset_list` that indicates which is the best dataset.\n test_score: float\n Test score associated with the best couple dataset-model.\n axes: list\n List of the matplotlib Axes where the plots have been made.\n Firstly, the 'secondary' plots are put (if any). And, as last, the 'main' plot is put (if any).\n If no plot has been made, `axes` is an empty list.\n\n See also\n ----------\n models_validation: select the best model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n couple dataset-model is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
datasets_train_val_score = []
datasets_best_model = []
datasets_test_score = []
axes = []
for (i, dataset) in enumerate(dataset_list):
(X, y) = dataset
(models_train_val_score, models_best_params, best_index, test_score, ax) = models_validation(X, y, model_paramGrid_list, scale_list=scale_list, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr, plot=verbose, plot_train=plot_train, xlabel='Models', title=(('Dataset ' + str(i)) + ' : models validation'), figsize=figsize_verbose)
datasets_train_val_score.append(tuple(models_train_val_score[best_index, :]))
datasets_best_model.append((best_index, model_paramGrid_list[best_index][0], models_best_params[best_index]))
datasets_test_score.append(test_score)
if ax:
axes.append(ax)
datasets_train_val_score = np.array(datasets_train_val_score)
if regr:
best_index = np.argmin(datasets_train_val_score, axis=0)[1]
else:
best_index = np.argmax(datasets_train_val_score, axis=0)[1]
test_score = datasets_test_score[best_index]
if plot:
if (not xvalues):
xvalues = range(len(dataset_list))
ax = _plot_TrainVal_values(xvalues, datasets_train_val_score, plot_train, xlabel, title, figsize, bar=True)
axes.append(ax)
return (datasets_train_val_score, datasets_best_model, best_index, test_score, axes) | 2,050,898,115,793,827,300 | Select the best dataset and the best model (i.e. select the best couple dataset-model).
For each dataset in `dataset_list`, all the models in `model_paramGrid_list` are tested: each model is tested performing
an exhaustive tuning of the specified hyperparameters. In fact, `model_paramGrid_list` also contains, for each model, the
grid of the hyperparameters that have to be tested on that model (i.e. the grid which contains the values to test for
each specified hyperparameter of the model).
In other words, on each dataset the selection of the best model is performed: in fact, on each dataset, the function
`models_validation` is applied. (See `models_validation`).
In the end, the best couple dataset-model is selected.
Despite the fact that a couple dataset-model is selected, the main viewpoint is focused with respect to the datasets.
It's a validation focused on the datasets.
In fact, first of all, for each dataset the model selection is performed: in this way the best model is selected
and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other words, on each
dataset the function `models_validation` is applied). Finally, after that, the best dataset is selected.
It's a two-levels selection.
This selection is made using the validation score (i.e. the best couple dataset-model is the one with best validation
score).
The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross
validation on the training set.
Additionally, the training and test scores are also computed.
Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset
selection. This is the 'main' plot.
Moreover, still optionally, the 'secondary' plots can be done: for each dataset, the validation scores of the models are
plotted, making a graphical visualization of the models selection on that dataset. (As the plot made by the
`models_validation` function).
Parameters
----------
dataset_list: list
List of couples, where each couple is a dataset.
- The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.
- The second element is y, the mono dimensional np.array containing the response feature of the dataset.
model_paramGrid_list: list
List that specifies the models and the relative grid of hyperparameters to be tested.
It's a list of triples (i.e. tuples), where each triple represents a model:
- the first element is a string, which is a mnemonic name of that model;
- the second element is the sklearn model;
- the third element is the grid of hyperparameters to test for that model. It's a dictionary, with the same
structure of parameter `param_grid` of the function `hyperparameters_validation`.
scale_list: list or bool
List of booleans, which has as many elements as the number of models to test (i.e. number of elements in the
`model_paramGrid_list` list).
This list indicates, for each different model, if the features in 'X' have to be scaled or not (for all the datasets).
`scale_list` can be None or False: in this case the 'X' features aren't scaled for any model. `scale_list` can be
True: in this case the 'X' features are scaled for all the models.
test_size: float
Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).
time_series: bool
Indicates if the given datasets are time series dataset (i.e. datasets indexed by days).
(This affects the computing of the validation score).
random_state: int
Used in the training-test splitting of the datasets.
n_folds: int
Indicates how many folds are made in order to compute the k-fold cross validation.
(It's used only if `time_series` is False).
regr: bool
Indicates if it's either a regression or a classification problem.
plot: bool
Indicates whether to plot or not the validation score values of the datasets (i.e. this is the 'main' plot).
plot_train: bool
Indicates whether to plot also the training scores (both in the 'main' and 'secondary' plots).
xvalues: list (in general, iterable)
Values that have to be put in the x axis of the 'main' plot.
xlabel: str
Label of the x axis of the 'main' plot.
title: str
Title of the 'main' plot.
figsize: tuple
Two dimensions of the 'main' plot.
verbose: bool
If True, for each dataset the validation scores of the models are plotted (i.e. these are the 'secondary' plots).
(See 'models_validation').
figsize_verbose: tuple
Two dimensions of the 'secondary' plots.
Returns
----------
datasets_train_val_score: np.array
Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation
scores.
It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.
datasets_best_model: list
List which has as many elements as the number of the datasets (i.e. number of elements in `dataset_list`). For
each dataset, it contains the best model for that dataset.
More precisely, it is a list of triple:
- the first element is the index of `model_paramGrid_list` which indicates the best model;
- the second element is the mnemonic name of the best model;
- the third element is the best combination of hyperparameters values on that best model (i.e. it's a dictionary
which has as keys the hyperparameters names and as values their associated values).
best_index: int
Index of `dataset_list` that indicates which is the best dataset.
test_score: float
Test score associated with the best couple dataset-model.
axes: list
List of the matplotlib Axes where the plots have been made.
Firstly, the 'secondary' plots are put (if any). And, as last, the 'main' plot is put (if any).
If no plot has been made, `axes` is an empty list.
See also
----------
models_validation: select the best model on the given dataset.
Notes
----------
- If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best
couple dataset-model is the one associated with the minimum validation score.
Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the
maximum validation score.
- If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross
validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.
Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two
contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit. | model_selection.py | datasets_models_validation | EnricoPittini/model-selection | python | def datasets_models_validation(dataset_list, model_paramGrid_list, scale_list=None, test_size=0.2, time_series=False, random_state=123, n_folds=5, regr=True, plot=False, plot_train=False, xvalues=None, xlabel='Datasets', title='Datasets validation', figsize=(6, 6), verbose=False, figsize_verbose=(6, 6)):
"\n Select the best dataset and the best model (i.e. select the best couple dataset-model).\n\n For each dataset in `dataset_list`, all the models in `model_paramGrid_list` are tested: each model is tested performing\n an exhaustive tuning of the specified hyperparameters. In fact, `model_paramGrid_list` also contains, for each model, the\n grid of the hyperparameters that have to be tested on that model (i.e. the grid which contains the values to test for\n each specified hyperparameter of the model).\n In other words, on each dataset the selection of the best model is performed: in fact, on each dataset, the function\n `models_validation` is applied. (See `models_validation`).\n In the end, the best couple dataset-model is selected.\n\n Despite the fact that a couple dataset-model is selected, the main viewpoint is focused with respect to the datasets.\n It's a validation focused on the datasets.\n In fact, first of all, for each dataset the model selection is performed: in this way the best model is selected\n and its relative score is associated with the dataset (i.e. it's the score of the dataset). (In other words, on each\n dataset the function `models_validation` is applied). Finally, after that, the best dataset is selected.\n It's a two-levels selection.\n\n This selection is made using the validation score (i.e. the best couple dataset-model is the one with best validation\n score).\n The validation score is computed by splitting each dataset into the training-test sets and then by applying the cross\n validation on the training set.\n Additionally, the training and test scores are also computed.\n\n Optionally, the validation scores of the datasets can be plotted, making a graphical visualization of the dataset\n selection. This is the 'main' plot.\n Moreover, still optionally, the 'secondary' plots can be done: for each dataset, the validation scores of the models are\n plotted, making a graphical visualization of the models selection on that dataset. (As the plot made by the\n `models_validation` function).\n\n Parameters\n ----------\n dataset_list: list\n List of couples, where each couple is a dataset.\n - The first element is X, the two-dimensional np.array containing the explanatory features of the dataset.\n - The second element is y, the mono dimensional np.array containing the response feature of the dataset.\n model_paramGrid_list: list\n List that specifies the models and the relative grid of hyperparameters to be tested.\n It's a list of triples (i.e. tuples), where each triple represents a model:\n - the first element is a string, which is a mnemonic name of that model;\n - the second element is the sklearn model;\n - the third element is the grid of hyperparameters to test for that model. It's a dictionary, with the same\n structure of parameter `param_grid` of the function `hyperparameters_validation`.\n scale_list: list or bool\n List of booleans, which has as many elements as the number of models to test (i.e. number of elements in the\n `model_paramGrid_list` list).\n This list indicates, for each different model, if the features in 'X' have to be scaled or not (for all the datasets).\n `scale_list` can be None or False: in this case the 'X' features aren't scaled for any model. `scale_list` can be\n True: in this case the 'X' features are scaled for all the models.\n test_size: float\n Decimal number between 0 and 1, which indicates the proportion of the test set (for each dataset).\n time_series: bool\n Indicates if the given datasets are time series dataset (i.e. datasets indexed by days).\n (This affects the computing of the validation score).\n random_state: int\n Used in the training-test splitting of the datasets.\n n_folds: int\n Indicates how many folds are made in order to compute the k-fold cross validation.\n (It's used only if `time_series` is False).\n regr: bool\n Indicates if it's either a regression or a classification problem.\n plot: bool\n Indicates whether to plot or not the validation score values of the datasets (i.e. this is the 'main' plot).\n plot_train: bool\n Indicates whether to plot also the training scores (both in the 'main' and 'secondary' plots).\n xvalues: list (in general, iterable)\n Values that have to be put in the x axis of the 'main' plot.\n xlabel: str\n Label of the x axis of the 'main' plot.\n title: str\n Title of the 'main' plot.\n figsize: tuple\n Two dimensions of the 'main' plot.\n verbose: bool\n If True, for each dataset the validation scores of the models are plotted (i.e. these are the 'secondary' plots).\n (See 'models_validation').\n figsize_verbose: tuple\n Two dimensions of the 'secondary' plots.\n\n Returns\n ----------\n datasets_train_val_score: np.array\n Two dimensional np.array, containing two columns: the first contains the training scores, the second the validation\n scores.\n It has as many rows as the number of datasets to test, i.e. as the number of elements in `dataset_list`.\n datasets_best_model: list\n List which has as many elements as the number of the datasets (i.e. number of elements in `dataset_list`). For\n each dataset, it contains the best model for that dataset.\n More precisely, it is a list of triple:\n - the first element is the index of `model_paramGrid_list` which indicates the best model;\n - the second element is the mnemonic name of the best model;\n - the third element is the best combination of hyperparameters values on that best model (i.e. it's a dictionary\n which has as keys the hyperparameters names and as values their associated values).\n best_index: int\n Index of `dataset_list` that indicates which is the best dataset.\n test_score: float\n Test score associated with the best couple dataset-model.\n axes: list\n List of the matplotlib Axes where the plots have been made.\n Firstly, the 'secondary' plots are put (if any). And, as last, the 'main' plot is put (if any).\n If no plot has been made, `axes` is an empty list.\n\n See also\n ----------\n models_validation: select the best model on the given dataset.\n\n Notes\n ----------\n - If `regr` is True, the validation scores are errors (MSE, i.e. Mean Squared Errors): this means that the best\n couple dataset-model is the one associated with the minimum validation score.\n Otherwise, the validation scores are accuracies: this means that the best couple is the one associated with the\n maximum validation score.\n - If `time_series` is False, the training-test splitting of each dataset is made randomly. In addition, the cross\n validation strategy performed is the classic k-fold cross validation: the number of folds is specified by `n_folds`.\n Otherwise, if `time_series` is True, the training-test sets are simply obtained by splitting each dataset into two\n contiguous parts. In addition, the cross validation strategy performed is the sklearn TimeSeriesSplit.\n "
datasets_train_val_score = []
datasets_best_model = []
datasets_test_score = []
axes = []
for (i, dataset) in enumerate(dataset_list):
(X, y) = dataset
(models_train_val_score, models_best_params, best_index, test_score, ax) = models_validation(X, y, model_paramGrid_list, scale_list=scale_list, test_size=test_size, time_series=time_series, random_state=random_state, n_folds=n_folds, regr=regr, plot=verbose, plot_train=plot_train, xlabel='Models', title=(('Dataset ' + str(i)) + ' : models validation'), figsize=figsize_verbose)
datasets_train_val_score.append(tuple(models_train_val_score[best_index, :]))
datasets_best_model.append((best_index, model_paramGrid_list[best_index][0], models_best_params[best_index]))
datasets_test_score.append(test_score)
if ax:
axes.append(ax)
datasets_train_val_score = np.array(datasets_train_val_score)
if regr:
best_index = np.argmin(datasets_train_val_score, axis=0)[1]
else:
best_index = np.argmax(datasets_train_val_score, axis=0)[1]
test_score = datasets_test_score[best_index]
if plot:
if (not xvalues):
xvalues = range(len(dataset_list))
ax = _plot_TrainVal_values(xvalues, datasets_train_val_score, plot_train, xlabel, title, figsize, bar=True)
axes.append(ax)
return (datasets_train_val_score, datasets_best_model, best_index, test_score, axes) |
def append(self, other):
' append the recarrays from one MfList to another\n Parameters\n ----------\n other: variable: an item that can be cast in to an MfList\n that corresponds with self\n Returns\n -------\n dict of {kper:recarray}\n '
if (not isinstance(other, MfList)):
other = MfList(self.package, data=other, dtype=self.dtype, model=self._model, list_free_format=self.list_free_format)
msg = ('MfList.append(): other arg must be ' + 'MfList or dict, not {0}'.format(type(other)))
assert isinstance(other, MfList), msg
other_kpers = list(other.data.keys())
other_kpers.sort()
self_kpers = list(self.data.keys())
self_kpers.sort()
new_dict = {}
for kper in range(self._model.nper):
other_data = other[kper].copy()
self_data = self[kper].copy()
other_len = other_data.shape[0]
self_len = self_data.shape[0]
if (((other_len == 0) and (self_len == 0)) or ((kper not in self_kpers) and (kper not in other_kpers))):
continue
elif (self_len == 0):
new_dict[kper] = other_data
elif (other_len == 0):
new_dict[kper] = self_data
else:
new_len = (other_data.shape[0] + self_data.shape[0])
new_data = np.recarray(new_len, dtype=self.dtype)
new_data[:self_len] = self_data
new_data[self_len:(self_len + other_len)] = other_data
new_dict[kper] = new_data
return new_dict | 3,458,584,039,420,723,000 | append the recarrays from one MfList to another
Parameters
----------
other: variable: an item that can be cast in to an MfList
that corresponds with self
Returns
-------
dict of {kper:recarray} | flopy/utils/util_list.py | append | aleaf/flopy | python | def append(self, other):
' append the recarrays from one MfList to another\n Parameters\n ----------\n other: variable: an item that can be cast in to an MfList\n that corresponds with self\n Returns\n -------\n dict of {kper:recarray}\n '
if (not isinstance(other, MfList)):
other = MfList(self.package, data=other, dtype=self.dtype, model=self._model, list_free_format=self.list_free_format)
msg = ('MfList.append(): other arg must be ' + 'MfList or dict, not {0}'.format(type(other)))
assert isinstance(other, MfList), msg
other_kpers = list(other.data.keys())
other_kpers.sort()
self_kpers = list(self.data.keys())
self_kpers.sort()
new_dict = {}
for kper in range(self._model.nper):
other_data = other[kper].copy()
self_data = self[kper].copy()
other_len = other_data.shape[0]
self_len = self_data.shape[0]
if (((other_len == 0) and (self_len == 0)) or ((kper not in self_kpers) and (kper not in other_kpers))):
continue
elif (self_len == 0):
new_dict[kper] = other_data
elif (other_len == 0):
new_dict[kper] = self_data
else:
new_len = (other_data.shape[0] + self_data.shape[0])
new_data = np.recarray(new_len, dtype=self.dtype)
new_data[:self_len] = self_data
new_data[self_len:(self_len + other_len)] = other_data
new_dict[kper] = new_data
return new_dict |
def drop(self, fields):
'drop fields from an MfList\n\n Parameters\n ----------\n fields : list or set of field names to drop\n\n Returns\n -------\n dropped : MfList without the dropped fields\n '
if (not isinstance(fields, list)):
fields = [fields]
names = [n for n in self.dtype.names if (n not in fields)]
dtype = np.dtype([(k, d) for (k, d) in self.dtype.descr if (k not in fields)])
spd = {}
for (k, v) in self.data.items():
newarr = np.array([self.data[k][n] for n in names]).transpose()
newarr = np.array(list(map(tuple, newarr)), dtype=dtype).view(np.recarray)
for n in dtype.names:
newarr[n] = self.data[k][n]
spd[k] = newarr
return MfList(self.package, spd, dtype=dtype) | 1,912,237,700,778,697,200 | drop fields from an MfList
Parameters
----------
fields : list or set of field names to drop
Returns
-------
dropped : MfList without the dropped fields | flopy/utils/util_list.py | drop | aleaf/flopy | python | def drop(self, fields):
'drop fields from an MfList\n\n Parameters\n ----------\n fields : list or set of field names to drop\n\n Returns\n -------\n dropped : MfList without the dropped fields\n '
if (not isinstance(fields, list)):
fields = [fields]
names = [n for n in self.dtype.names if (n not in fields)]
dtype = np.dtype([(k, d) for (k, d) in self.dtype.descr if (k not in fields)])
spd = {}
for (k, v) in self.data.items():
newarr = np.array([self.data[k][n] for n in names]).transpose()
newarr = np.array(list(map(tuple, newarr)), dtype=dtype).view(np.recarray)
for n in dtype.names:
newarr[n] = self.data[k][n]
spd[k] = newarr
return MfList(self.package, spd, dtype=dtype) |
@property
def fmt_string(self):
'Returns a C-style fmt string for numpy savetxt that corresponds to\n the dtype'
if (self.list_free_format is not None):
use_free = self.list_free_format
else:
use_free = True
if self.package.parent.has_package('bas6'):
use_free = self.package.parent.bas6.ifrefm
if ('mt3d' in self.package.parent.version.lower()):
use_free = False
fmts = []
for field in self.dtype.descr:
vtype = field[1][1].lower()
if (vtype in ('i', 'b')):
if use_free:
fmts.append('%9d')
else:
fmts.append('%10d')
elif (vtype == 'f'):
if use_free:
if numpy114:
fmts.append('%15s')
else:
fmts.append('%15.7E')
else:
fmts.append('%10G')
elif (vtype == 'o'):
if use_free:
fmts.append('%9s')
else:
fmts.append('%10s')
elif (vtype == 's'):
msg = "MfList.fmt_string error: 'str' type found in dtype. This gives unpredictable results when recarray to file - change to 'object' type"
raise TypeError(msg)
else:
raise TypeError('MfList.fmt_string error: unknown vtype in field: {}'.format(field))
if use_free:
fmt_string = (' ' + ' '.join(fmts))
else:
fmt_string = ''.join(fmts)
return fmt_string | 8,351,358,389,882,369,000 | Returns a C-style fmt string for numpy savetxt that corresponds to
the dtype | flopy/utils/util_list.py | fmt_string | aleaf/flopy | python | @property
def fmt_string(self):
'Returns a C-style fmt string for numpy savetxt that corresponds to\n the dtype'
if (self.list_free_format is not None):
use_free = self.list_free_format
else:
use_free = True
if self.package.parent.has_package('bas6'):
use_free = self.package.parent.bas6.ifrefm
if ('mt3d' in self.package.parent.version.lower()):
use_free = False
fmts = []
for field in self.dtype.descr:
vtype = field[1][1].lower()
if (vtype in ('i', 'b')):
if use_free:
fmts.append('%9d')
else:
fmts.append('%10d')
elif (vtype == 'f'):
if use_free:
if numpy114:
fmts.append('%15s')
else:
fmts.append('%15.7E')
else:
fmts.append('%10G')
elif (vtype == 'o'):
if use_free:
fmts.append('%9s')
else:
fmts.append('%10s')
elif (vtype == 's'):
msg = "MfList.fmt_string error: 'str' type found in dtype. This gives unpredictable results when recarray to file - change to 'object' type"
raise TypeError(msg)
else:
raise TypeError('MfList.fmt_string error: unknown vtype in field: {}'.format(field))
if use_free:
fmt_string = (' ' + ' '.join(fmts))
else:
fmt_string = .join(fmts)
return fmt_string |
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