def lpo_sklearn(X,y, regparam):
lpo = LeavePOut(p=2)
preda = []
predb = []
for train, test in lpo.split(X):
rls = KernelRidge(kernel="rbf", gamma=0.01)
rls.fit(X[train], y[train])
p = rls.predict(X[test])
preda.append(p[0])
predb.append(p[1])
return preda, predb
r_spearman[i, j, k] = 0.0
p_spearman[i, j, k] = 1.0
if np.isnan(r_pearson[i, j, k]):
r_pearson[i, j, k] = 0.0
p_pearson[i, j, k] = 1.0
# Move to linear discriminant analysis
lda_palatability = np.zeros((unique_lasers.shape[0], identity.shape[0]))
for i in range(unique_lasers.shape[0]):
for j in range(identity.shape[0]):
X = response[j, :, trials[i]]
Y = palatability[j, 0, trials[i]]
# Use k-fold cross validation where k = 1 sample left out
test_results = []
c_validator = LeavePOut(1)
for train, test in c_validator.split(X, Y):
model = LDA()
model.fit(X[train, :], Y[train])
# And test on the left out kth trial - compare to the actual class of the kth trial and store in test results
test_results.append(np.mean(model.predict(X[test]) == Y[test]))
lda_palatability[i, j] = np.mean(test_results)
# Save these arrays to file
hf5.create_array('/ancillary_analysis', 'r_pearson', r_pearson)
hf5.create_array('/ancillary_analysis', 'p_pearson', p_pearson)
hf5.create_array('/ancillary_analysis', 'r_spearman', r_spearman)
hf5.create_array('/ancillary_analysis', 'p_spearman', p_spearman)
hf5.create_array('/ancillary_analysis', 'lda_palatability', lda_palatability)
hf5.flush()
# --------End palatability calculation----------------------------------------------------------------------------
output_test = "{}({}: {}) ".format(output_test, i, data[i])
print("[ {} ]".format(" ".join(bar)))
print("Train: {}".format(output_train))
print("Test: {}\n".format(output_test))
# Create some data to split with
data = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8]])
# Our two methods
loocv = LeaveOneOut()
lpocv = LeavePOut(p=P_VAL)
split_loocv = loocv.split(data)
split_lpocv = lpocv.split(data)
print("""\
The Leave-P-Out method works by using every combination of P points as test data.
The following output shows the result of splitting some sample data by Leave-One-Out and Leave-P-Out methods.
A bar displaying the current train-test split as well as the actual data points are displayed for each split.
In the bar, "-" is a training point and "T" is a test point.
""")
print("Data:\n{}\n".format(data))
print("Leave-One-Out:\n")
print_result(split_loocv)
print("Leave-P-Out (where p = {}):\n".format(P_VAL))
X = [1, 2, 3, 4]
loo = LeaveOneOut()
print('LeaveOneOut')
for train, test in loo.split(X):
print(f"{train}, {test}")
#####
# Leave P out
#####
from sklearn.model_selection import LeavePOut
X = np.ones(4)
lpo = LeavePOut(2)
print('LeavePOut')
for train, test in lpo.split(X):
print(f"{train}, {test}")
#####
# ShuffleSplit
#####
from sklearn.model_selection import ShuffleSplit
X = np.arange(10) * 2
ss = ShuffleSplit(n_splits=3, test_size=0.25, random_state=0)
print('ShuffleSplit')
for train_index, test_index in ss.split(X):
print(f"{train_index}, {test_index}")
# You see, e.g., '8' appears twice, '9' never in the val set
#######
bar[i] = "T"
output_test = "{}({}: {}) ".format(output_test, i, data[i])
print("[ {} ]".format(" ".join(bar)))
print("Train: {}".format(output_train))
print("Test: {}\n".format(output_test))
P_VAL = 2
data = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8]])
loocv = LeaveOneOut()
lpocv = LeavePOut(p=P_VAL)
split_loocv = loocv.split(data)
split_lpocv = lpocv.split(data)
print("Data:\n{}\n".format(data))
print("Leave-One-Out:\n")
print_result(split_loocv)
print("Leave-P-Out (where p = {}):\n".format(P_VAL))
print_result(split_lpocv)
'''
Data:
[[1 2]
[3 4]
[5 6]
[7 8]]
Leave-One-Out:
loo = LeaveOneOut()
print(loo)
for train_index, test_index in loo.split(X):
print("Train Index:", train_index, ",Test Index:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# print(X_train,X_test,y_train,y_test)
#LeavePOut
import numpy as np
from sklearn.model_selection import LeavePOut
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
y = np.array([1, 1, 1, 2, 2, 2])
lpo = LeavePOut(p=2)
print(lpo)
for train_index, test_index in lpo.split(X):
print("Train Index:", train_index, ",Test Index:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# print(X_train,X_test,y_train,y_test)
#随机划分法
#ShuffleSplit
import numpy as np
from sklearn.model_selection import ShuffleSplit
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
y = np.array([1, 2, 1, 2, 1, 2])
rs = ShuffleSplit(n_splits=3, test_size=.25, random_state=0)
print(rs)
for train_index, test_index in rs.split(X):
print("Train Index:", train_index, ",Test Index:", test_index)
LeavePOut, ShuffleSplit, TimeSeriesSplit)
data = list(range(1, 11))
print(data)
print(train_test_split(data, train_size=.8))
kf = KFold(n_splits=5)
for train, validate in kf.split(data):
print(train, validate)
kf = KFold(n_splits=5, shuffle=True, random_state=42)
for train, validate in kf.split(data):
print(train, validate)
loo = LeaveOneOut()
for train, validate in loo.split(data):
print(train, validate)
lpo = LeavePOut(p=2)
for train, validate in lpo.split(data):
print(train, validate)
ss = ShuffleSplit(n_splits=3, test_size=2, random_state=0)
for train, validate in ss.split(data):
print(train, validate)
tscv = TimeSeriesSplit(n_splits=5)
for train, validate in tscv.split(data):
print(train, validate)
X_raw.iloc[:, best_features_list[i]],
y,
cv=lpo))
for i in range(len(scores)):
print("%s Accuracy: %0.10f (+/- %0.4f)" %
(model_name_list[i], scores[i].mean(), scores[i].std()))
# print scores
print
# result_file.write(str(scores[i].mean()) + ' ' + str(scores[i].std()) + '\r\n')
import numpy as np
for i in range(len(model_list)):
expected = pd.DataFrame()
predicted = np.array([])
for train, test in lpo.split(X_raw, y):
X_train = X_raw.iloc[train, best_features_list[i]]
y_train = data.iloc[train, data.shape[1] - 1]
X_test = X_raw.iloc[test, best_features_list[i]]
y_test = data.iloc[test, data.shape[1] - 1]
model_list[i].fit(X_train, y_train)
# make predictions
expected = pd.concat([expected, y_test])
predicted = np.concatenate([predicted, model_list[i].predict(X_test)])
print(model_name_list[i] + str(
precision_recall_fscore_support(expected, predicted, average='macro')))
# def my_validation(model, X_f, y_f):
# score = np.array([])
# for train, test in lpo.split(X_f, y_f):
# n_min = 0
r_spearman[i, j, k] = 0.0
p_spearman[i, j, k] = 1.0
if np.isnan(r_pearson[i, j, k]):
r_pearson[i, j, k] = 0.0
p_pearson[i, j, k] = 1.0
# Move to linear discriminant analysis
lda_palatability = np.zeros((unique_lasers.shape[0], identity.shape[0]))
for i in range(unique_lasers.shape[0]):
for j in range(identity.shape[0]):
X = response[j, :, trials[i]]
Y = palatability[j, 0, trials[i]]
# Use k-fold cross validation where k = 1 sample left out
test_results = []
c_validator = LeavePOut(1)
for train, test in c_validator.split(X, Y):
model = LDA()
model.fit(X[train, :], Y[train])
# And test on the left out kth trial - compare to the actual class of the kth trial and store in test results
test_results.append(np.mean(model.predict(X[test]) == Y[test]))
lda_palatability[i, j] = np.mean(test_results)
# Save these arrays to file
hf5.create_array('/ancillary_analysis', 'r_pearson', r_pearson)
hf5.create_array('/ancillary_analysis', 'p_pearson', p_pearson)
hf5.create_array('/ancillary_analysis', 'r_spearman', r_spearman)
hf5.create_array('/ancillary_analysis', 'p_spearman', p_spearman)
hf5.create_array('/ancillary_analysis', 'lda_palatability', lda_palatability)
hf5.flush()
# --------End palatability calculation----------------------------------------------------------------------------
def palatability_identity_calculations(rec_dir, pal_ranks=None,
params=None, shell=False):
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=RuntimeWarning)
dat = load_dataset(rec_dir)
dim = dat.dig_in_mapping
if 'palatability_rank' in dim.columns:
pass
elif pal_ranks is None:
dim = get_palatability_ranks(dim, shell=shell)
else:
dim['palatability_rank'] = dim['name'].map(pal_ranks)
dim = dim.dropna(subset=['palatability_rank'])
dim = dim[dim['palatability_rank'] > 0]
dim = dim.reset_index(drop=True)
num_tastes = len(dim)
taste_names = dim.name.to_list()
trial_list = dat.dig_in_trials.copy()
trial_list = trial_list[[True if x in taste_names else False
for x in trial_list.name]]
num_trials = trial_list.groupby('channel').count()['name'].unique()
if len(num_trials) > 1:
raise ValueError('Unequal number of trials for tastes to used')
else:
num_trials = num_trials[0]
dim['num_trials'] = num_trials
# Get which units to use
unit_table = h5io.get_unit_table(rec_dir)
unit_types = ['Single', 'Multi', 'All', 'Custom']
unit_type = params.get('unit_type')
if unit_type is None:
q = userIO.ask_user('Which units do you want to use for taste '
'discrimination and palatability analysis?',
choices=unit_types,
shell=shell)
unit_type = unit_types[q]
if unit_type == 'Single':
chosen_units = unit_table.loc[unit_table['single_unit'],
'unit_num'].to_list()
elif unit_type == 'Multi':
chosen_units = unit_table.loc[unit_table['single_unit'] == False,
'unit_num'].to_list()
elif unit_type == 'All':
chosen_units = unit_table['unit_num'].to_list()
else:
selection = userIO.select_from_list('Select units to use:',
unit_table['unit_num'],
'Select Units',
multi_select=True)
chosen_units = list(map(int, selection))
num_units = len(chosen_units)
unit_table = unit_table.loc[chosen_units]
# Enter Parameters
if params is None or params.keys() != default_pal_id_params.keys():
params = default_pal_id_params.copy()
params = userIO.confirm_parameter_dict(params,
('Palatability/Identity '
'Calculation Parameters'
'\nTimes in ms'), shell=shell)
win_size = params['window_size']
win_step = params['window_step']
print('Running palatability/identity calculations with parameters:\n%s' %
pt.print_dict(params))
with tables.open_file(dat.h5_file, 'r+') as hf5:
trains_dig_in = hf5.list_nodes('/spike_trains')
time = trains_dig_in[0].array_time[:]
bin_times = np.arange(time[0], time[-1] - win_size + win_step,
win_step)
num_bins = len(bin_times)
palatability = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=int)
identity = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=int)
unscaled_response = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=np.dtype('float64'))
response = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=np.dtype('float64'))
laser = np.empty((num_bins, num_units, num_tastes*num_trials, 2),
dtype=float)
# Fill arrays with data
print('Filling data arrays...')
onesies = np.ones((num_bins, num_units, num_trials))
for i, row in dim.iterrows():
idx = range(num_trials*i, num_trials*(i+1))
palatability[:, :, idx] = row.palatability_rank * onesies
identity[:, :, idx] = row.channel * onesies
for j, u in enumerate(chosen_units):
for k,t in enumerate(bin_times):
t_idx = np.where((time >= t) & (time <= t+win_size))[0]
unscaled_response[k, j, idx] = \
np.mean(trains_dig_in[i].spike_array[:, u, t_idx],
axis=1)
try:
lasers[k, j, idx] = \
np.vstack((trains_dig_in[i].laser_durations[:],
trains_dig_in[i].laser_onset_lag[:]))
except:
laser[k, j, idx] = np.zeros((num_trials, 2))
# Scaling was not done, so:
response = unscaled_response.copy()
# Make ancillary_analysis node and put in arrays
if '/ancillary_analysis' in hf5:
hf5.remove_node('/ancillary_analysis', recursive=True)
hf5.create_group('/', 'ancillary_analysis')
hf5.create_array('/ancillary_analysis', 'palatability', palatability)
hf5.create_array('/ancillary_analysis', 'identity', identity)
hf5.create_array('/ancillary_analysis', 'laser', laser)
hf5.create_array('/ancillary_analysis', 'scaled_neural_response',
response)
hf5.create_array('/ancillary_analysis', 'window_params',
np.array([win_size, win_step]))
hf5.create_array('/ancillary_analysis', 'bin_times', bin_times)
hf5.create_array('/ancillary_analysis', 'unscaled_neural_response',
unscaled_response)
# for backwards compatibility
hf5.create_array('/ancillary_analysis', 'params',
np.array([win_size, win_step]))
hf5.create_array('/ancillary_analysis', 'pre_stim', np.array(time[0]))
hf5.flush()
# Get unique laser (duration, lag) combinations
print('Organizing trial data...')
unique_lasers = np.vstack(list({tuple(row) for row in laser[0, 0, :, :]}))
unique_lasers = unique_lasers[unique_lasers[:, 1].argsort(), :]
num_conditions = unique_lasers.shape[0]
trials = []
for row in unique_lasers:
tmp_trials = [j for j in range(num_trials * num_tastes)
if np.array_equal(laser[0, 0, j, :], row)]
trials.append(tmp_trials)
trials_per_condition = [len(x) for x in trials]
if not all(x == trials_per_condition[0] for x in trials_per_condition):
raise ValueError('Different number of trials for each laser condition')
trials_per_condition = int(trials_per_condition[0] / num_tastes) #assumes same number of trials per taste per condition
print('Detected:\n %i tastes\n %i laser conditions\n'
' %i trials per condition per taste' %
(num_tastes, num_conditions, trials_per_condition))
trials = np.array(trials)
# Store laser conditions and indices of trials per condition in trial x
# taste space
hf5.create_array('/ancillary_analysis', 'trials', trials)
hf5.create_array('/ancillary_analysis', 'laser_combination_d_l',
unique_lasers)
hf5.flush()
# Taste Similarity Calculation
neural_response_laser = np.empty((num_conditions, num_bins,
num_tastes, num_units,
trials_per_condition),
dtype=np.dtype('float64'))
taste_cosine_similarity = np.empty((num_conditions, num_bins,
num_tastes, num_tastes),
dtype=np.dtype('float64'))
taste_euclidean_distance = np.empty((num_conditions, num_bins,
num_tastes, num_tastes),
dtype=np.dtype('float64'))
# Re-format neural responses from bin x unit x (trial*taste) to
# laser_condition x bin x taste x unit x trial
print('Reformatting data arrays...')
for i, trial in enumerate(trials):
for j, _ in enumerate(bin_times):
for k, _ in dim.iterrows():
idx = np.where((trial >= num_trials*k) &
(trial < num_trials*(k+1)))[0]
neural_response_laser[i, j, k, :, :] = \
response[j, :, trial[idx]].T
# Compute taste cosine similarity and euclidean distances
print('Computing taste cosine similarity and euclidean distances...')
for i, _ in enumerate(trials):
for j, _ in enumerate(bin_times):
for k, _ in dim.iterrows():
for l, _ in dim.iterrows():
taste_cosine_similarity[i, j, k, l] = \
np.mean(cosine_similarity(
neural_response_laser[i, j, k, :, :].T,
neural_response_laser[i, j, l, :, :].T))
taste_euclidean_distance[i, j, k, l] = \
np.mean(cdist(
neural_response_laser[i, j, k, :, :].T,
neural_response_laser[i, j, l, :, :].T,
metric='euclidean'))
hf5.create_array('/ancillary_analysis', 'taste_cosine_similarity',
taste_cosine_similarity)
hf5.create_array('/ancillary_analysis', 'taste_euclidean_distance',
taste_euclidean_distance)
hf5.flush()
# Taste Responsiveness calculations
bin_params = [params['num_comparison_bins'],
params['comparison_bin_size']]
discrim_p = params['discrim_p']
responsive_neurons = []
discriminating_neurons = []
taste_responsiveness = np.zeros((bin_params[0], num_units, 2))
new_bin_times = np.arange(0, np.prod(bin_params), bin_params[1])
baseline = np.where(bin_times < 0)[0]
print('Computing taste responsiveness and taste discrimination...')
for i, t in enumerate(new_bin_times):
places = np.where((bin_times >= t) &
(bin_times <= t+bin_params[1]))[0]
for j, u in enumerate(chosen_units):
# Check taste responsiveness
f, p = f_oneway(np.mean(response[places, j, :], axis=0),
np.mean(response[baseline, j, :], axis=0))
if np.isnan(f):
f = 0.0
p = 1.0
if p <= discrim_p and u not in responsive_neurons:
responsive_neurons.append(u)
taste_responsiveness[i, j, 0] = 1
# Check taste discrimination
taste_idx = [np.arange(num_trials*k, num_trials*(k+1))
for k in range(num_tastes)]
taste_responses = [np.mean(response[places, j, :][:, k], axis=0)
for k in taste_idx]
f, p = f_oneway(*taste_responses)
if np.isnan(f):
f = 0.0
p = 1.0
if p <= discrim_p and u not in discriminating_neurons:
discriminating_neurons.append(u)
responsive_neurons = np.sort(responsive_neurons)
discriminating_neurons = np.sort(discriminating_neurons)
# Write taste responsive and taste discriminating units to text file
save_file = os.path.join(rec_dir, 'discriminative_responsive_neurons.txt')
with open(save_file, 'w') as f:
print('Taste discriminative neurons', file=f)
for u in discriminating_neurons:
print(u, file=f)
print('Taste responsive neurons', file=f)
for u in responsive_neurons:
print(u, file=f)
hf5.create_array('/ancillary_analysis', 'taste_disciminating_neurons',
discriminating_neurons)
hf5.create_array('/ancillary_analysis', 'taste_responsive_neurons',
responsive_neurons)
hf5.create_array('/ancillary_analysis', 'taste_responsiveness',
taste_responsiveness)
hf5.flush()
# Get time course of taste discrimibility
print('Getting taste discrimination time course...')
p_discrim = np.empty((num_conditions, num_bins, num_tastes, num_tastes,
num_units), dtype=np.dtype('float64'))
for i in range(num_conditions):
for j, t in enumerate(bin_times):
for k in range(num_tastes):
for l in range(num_tastes):
for m in range(num_units):
_, p = ttest_ind(neural_response_laser[i, j, k, m, :],
neural_response_laser[i, j, l, m, :],
equal_var = False)
if np.isnan(p):
p = 1.0
p_discrim[i, j, k, l, m] = p
hf5.create_array('/ancillary_analysis', 'p_discriminability',
p_discrim)
hf5.flush()
# Palatability Rank Order calculation (if > 2 tastes)
t_start = params['pal_deduce_start_time']
t_end = params['pal_deduce_end_time']
if num_tastes > 2:
print('Deducing palatability rank order...')
palatability_rank_order_deduction(rec_dir, neural_response_laser,
unique_lasers,
bin_times, [t_start, t_end])
# Palatability calculation
r_spearman = np.zeros((num_conditions, num_bins, num_units))
p_spearman = np.ones((num_conditions, num_bins, num_units))
r_pearson = np.zeros((num_conditions, num_bins, num_units))
p_pearson = np.ones((num_conditions, num_bins, num_units))
f_identity = np.ones((num_conditions, num_bins, num_units))
p_identity = np.ones((num_conditions, num_bins, num_units))
lda_palatability = np.zeros((num_conditions, num_bins))
lda_identity = np.zeros((num_conditions, num_bins))
r_isotonic = np.zeros((num_conditions, num_bins, num_units))
id_pal_regress = np.zeros((num_conditions, num_bins, num_units, 2))
pairwise_identity = np.zeros((num_conditions, num_bins, num_tastes, num_tastes))
print('Computing palatability metrics...')
for i, t in enumerate(trials):
for j in range(num_bins):
for k in range(num_units):
ranks = rankdata(response[j, k, t])
r_spearman[i, j, k], p_spearman[i, j, k] = \
spearmanr(ranks, palatability[j, k, t])
r_pearson[i, j, k], p_pearson[i, j, k] = \
pearsonr(response[j, k, t], palatability[j, k, t])
if np.isnan(r_spearman[i, j, k]):
r_spearman[i, j, k] = 0.0
p_spearman[i, j, k] = 1.0
if np.isnan(r_pearson[i, j, k]):
r_pearson[i, j, k] = 0.0
p_pearson[i, j, k] = 1.0
# Isotonic regression of firing against palatability
model = IsotonicRegression(increasing = 'auto')
model.fit(palatability[j, k, t], response[j, k, t])
r_isotonic[i, j, k] = model.score(palatability[j, k, t],
response[j, k, t])
# Multiple Regression of firing rate against palatability and identity
# Regress palatability on identity
tmp_id = identity[j, k, t].reshape(-1, 1)
tmp_pal = palatability[j, k, t].reshape(-1, 1)
tmp_resp = response[j, k, t].reshape(-1, 1)
model_pi = LinearRegression()
model_pi.fit(tmp_id, tmp_pal)
pi_residuals = tmp_pal - model_pi.predict(tmp_id)
# Regress identity on palatability
model_ip = LinearRegression()
model_ip.fit(tmp_pal, tmp_id)
ip_residuals = tmp_id - model_ip.predict(tmp_pal)
# Regress firing on identity
model_fi = LinearRegression()
model_fi.fit(tmp_id, tmp_resp)
fi_residuals = tmp_resp - model_fi.predict(tmp_id)
# Regress firing on palatability
model_fp = LinearRegression()
model_fp.fit(tmp_pal, tmp_resp)
fp_residuals = tmp_resp - model_fp.predict(tmp_pal)
# Get partial correlation coefficient of response with identity
idp_reg0, p = pearsonr(fp_residuals, ip_residuals)
if np.isnan(idp_reg0):
idp_reg0 = 0.0
idp_reg1, p = pearsonr(fi_residuals, pi_residuals)
if np.isnan(idp_reg1):
idp_reg1 = 0.0
id_pal_regress[i, j, k, 0] = idp_reg0
id_pal_regress[i, j, k, 1] = idp_reg1
# Identity Calculation
samples = []
for _, row in dim.iterrows():
taste = row.channel
samples.append([trial for trial in t
if identity[j, k, trial] == taste])
tmp_resp = [response[j, k, sample] for sample in samples]
f_identity[i, j, k], p_identity[i, j, k] = f_oneway(*tmp_resp)
if np.isnan(f_identity[i, j, k]):
f_identity[i, j, k] = 0.0
p_identity[i, j, k] = 1.0
# Linear Discriminant analysis for palatability
X = response[j, :, t]
Y = palatability[j, 0, t]
test_results = []
c_validator = LeavePOut(1)
for train, test in c_validator.split(X, Y):
model = LDA()
model.fit(X[train, :], Y[train])
tmp = np.mean(model.predict(X[test]) == Y[test])
test_results.append(tmp)
lda_palatability[i, j] = np.mean(test_results)
# Linear Discriminant analysis for identity
Y = identity[j, 0, t]
test_results = []
c_validator = LeavePOut(1)
for train, test in c_validator.split(X, Y):
model = LDA()
model.fit(X[train, :], Y[train])
tmp = np.mean(model.predict(X[test]) == Y[test])
test_results.append(tmp)
lda_identity[i, j] = np.mean(test_results)
# Pairwise Identity Calculation
for ti1, r1 in dim.iterrows():
for ti2, r2 in dim.iterrows():
t1 = r1.channel
t2 = r2.channel
tmp_trials = np.where((identity[j, 0, :] == t1) |
(identity[j, 0, :] == t2))[0]
idx = [trial for trial in t if trial in tmp_trials]
X = response[j, :, idx]
Y = identity[j, 0, idx]
test_results = []
c_validator = StratifiedShuffleSplit(n_splits=10,
test_size=0.25,
random_state=0)
for train, test in c_validator.split(X, Y):
model = GaussianNB()
model.fit(X[train, :], Y[train])
tmp_score = model.score(X[test, :], Y[test])
test_results.append(tmp_score)
pairwise_identity[i, j, ti1, ti2] = np.mean(test_results)
hf5.create_array('/ancillary_analysis', 'r_pearson', r_pearson)
hf5.create_array('/ancillary_analysis', 'r_spearman', r_spearman)
hf5.create_array('/ancillary_analysis', 'p_pearson', p_pearson)
hf5.create_array('/ancillary_analysis', 'p_spearman', p_spearman)
hf5.create_array('/ancillary_analysis', 'lda_palatability', lda_palatability)
hf5.create_array('/ancillary_analysis', 'lda_identity', lda_identity)
hf5.create_array('/ancillary_analysis', 'r_isotonic', r_isotonic)
hf5.create_array('/ancillary_analysis', 'id_pal_regress', id_pal_regress)
hf5.create_array('/ancillary_analysis', 'f_identity', f_identity)
hf5.create_array('/ancillary_analysis', 'p_identity', p_identity)
hf5.create_array('/ancillary_analysis', 'pairwise_NB_identity', pairwise_identity)
hf5.flush()
warnings.filterwarnings('default', category=UserWarning)
warnings.filterwarnings('default', category=RuntimeWarning)
random_state = 12883823
rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=random_state)
for train, test in rkf.split(X): print("%s %s" % (train, test))
# Leave One Out (LOO)
from sklearn.model_selection import LeaveOneOut
X = [1, 2, 3, 4]
loo = LeaveOneOut()
for train, test in loo.split(X): print("%s %s" % (train, test))
# Leave P out (LPO)
# Example of Leave-2-Out on a dataset with 4 samples:
from sklearn.model_selection import LeavePOut
X = np.ones(4)
lpo = LeavePOut(p=2)
for train, test in lpo.split(X): print("%s %s" % (train, test))
## Cross validation of time series data
# Example of 3-split time series cross-validation on a dataset with 6 samples:
from sklearn.model_selection import TimeSeriesSplit
X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
y = np.array([1, 2, 3, 4, 5, 6])
tscv = TimeSeriesSplit(n_splits=3)
print(tscv)
TimeSeriesSplit(max_train_size=None, n_splits=3)
for train, test in tscv.split(X): print("%s %s" % (train, test))
#### Cross validation and model selection
### Model evaluation: Quantifying the quality of prediction
## Classification metrics
def main():
path_boy = "F:\\study in school\\machine learning\\forstudent\\实验数据\\boynew.txt"
path_girl = "F:\\study in school\\machine learning\\forstudent\\实验数据\\girlnew.txt"
# height = []
# weight = []
# feetsize = []
x_boy = []
x_girl = []
label_boy = [] # 1表示男，0表示女
label_girl = []
readdata1(path_boy, x_boy, label_boy, 1)
readdata1(path_girl, x_girl, label_girl, 0)
x_boy = np.mat(x_boy)
x_girl = np.mat(x_girl)
m1 = x_boy.mean(0)
m0 = x_girl.mean(0)
S1 = (x_boy - m1[0]).T * (x_boy - m1[0])
S0 = (x_girl - m0[0]).T * (x_girl - m0[0])
Sw = S1 + S0
S_inverse = Sw.I
W = S_inverse * (m1 - m0).T
M1 = float(W.T * m1.T)
M0 = float(W.T * m0.T)
w_decision0 = (M0 + M1) / 2
path_boy_test = "F:\\study in school\\machine learning\\forstudent\\实验数据\\boy.txt"
path_girl_test = "F:\\study in school\\machine learning\\forstudent\\实验数据\\girl.txt"
x = []
label = []
readdata1(path_boy_test, x, label, 1)
readdata1(path_girl_test, x, label, 0)
label_test = []
y = x * W
errorcount = 0
for i in range(len(label)):
if float(y[i] > w_decision0):
label_test.append(1)
if label[i] != 1:
errorcount = errorcount + 1
else:
label_test.append(0)
if label[i] != 0:
errorcount = errorcount + 1
e_percentage = errorcount / len(label_test)
print('fisher测试集的错误率为%f' % e_percentage)
#留一法
loo = LeavePOut(p=1)
error = 0
for train, test in loo.split(x, label):
x_boy = []
x_girl = []
label_boy = [] # 1表示男，0表示女
label_girl = []
for i in train:
if label[i] == 1:
x_boy.append(x[i])
label_boy.append(1)
else:
x_girl.append(x[i])
label_girl.append(0)
x_boy = np.mat(x_boy)
x_girl = np.mat(x_girl)
m1 = x_boy.mean(0)
m0 = x_girl.mean(0)
S1 = (x_boy - m1[0]).T * (x_boy - m1[0])
S0 = (x_girl - m0[0]).T * (x_girl - m0[0])
Sw = S1 + S0
S_inverse = Sw.I
W = S_inverse * (m1 - m0).T
M1 = float(W.T * m1.T)
M0 = float(W.T * m0.T)
w_decision0 = (M0 + M1) / 2
for j in test:
if float(x[j] * W > w_decision0):
if label[j] != 1:
error = error + 1
else:
label_test.append(0)
if label[j] != 0:
error = error + 1
print('fisher留一法的错误率为%f' % (error / len(label)))
figure(3)
FPR, TPR = get_roc_fisher(W, w_decision0, x, label)
plot(FPR, TPR, label='fisher')
figure(5)
x1 = np.arange(130, 190, 0.01)
y1 = (w_decision0 - W[0] * x1) / W[1]
plot(x1, array(y1)[0])
plot(x1, x1 * float(W[1]) / float(W[0]))
for i in range(len(label)):
if label[i] == 1:
plot(float(x[i][0]), float(x[i][1]), 'o', color='r')
else:
plot(float(x[i][0]), float(x[i][1]), 'o', color='g')
a=(float(x[i][1])+float(x[i][0])*float(W[0])/float(W[1]))/\
(float(W[1])/float(W[0])+float(W[0])/float(W[1]))
b = a * float(W[1]) / float(W[0])
plot([float(x[i][0]), a], [float(x[i][1]), b], '--', color='0.75')
axis([140, 190, 35, 85])
Bayes()
