def summary(n_samples,
min_n_clusters,
max_n_clusters,
n_features,
random_state=0,
n_loops=10):
gm_01_scores = []
gm_02_scores = []
xm_scores = []
for n_cluster in np.linspace(min_n_clusters, max_n_clusters,
max_n_clusters - min_n_clusters + 1):
total_gm_01_score = 0
total_gm_02_score = 0
total_xm_score = 0
for i in range(n_loops):
n_cluster = n_cluster.astype(int)
X, y = datasets.make_blobs(n_samples=n_samples,
n_features=n_features,
centers=n_cluster,
random_state=random_state)
X = StandardScaler().fit_transform(X)
X_1 = X.copy()
X_2 = X.copy()
X_3 = X.copy()
gm_01 = GMeans_01().fit(X_1)
gm_02 = GMeans_02().fit(X_2)
xm = XMeans().fit(X_3)
gm_01_score = silhouette_score(X_1,
gm_01.labels,
metric='euclidean')
gm_02_score = silhouette_score(X_2,
gm_02.labels,
metric='euclidean')
xm_score = silhouette_score(X_3, xm.labels_, metric='euclidean')
# print "xm = {}".format(xm_score)
total_gm_01_score += gm_01_score
total_gm_02_score += gm_02_score
total_xm_score += xm_score
total_gm_01_score = total_gm_01_score / (n_loops * 1.0)
total_gm_02_score = total_gm_02_score / (n_loops * 1.0)
total_xm_score = total_xm_score / (n_loops * 1.0)
print "n_samples = {}, n_features = {}, n_cluster = {}, gm_01_score = {}, gm_02_score = {}, xm_score = {}" \
.format(n_samples, n_features, n_cluster, total_gm_01_score, total_gm_02_score, total_xm_score)
gm_01_scores.append(total_gm_01_score)
gm_02_scores.append(total_gm_02_score)
xm_scores.append(total_xm_score)
return gm_01_scores, gm_02_scores, xm_scores
def predict(self, X=None):
"""Calculate connectivity-based outlier factor for each sample in A
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data to predict.
Returns
-------
probs : array, shape (n_samples,)
Outlier probabilities determined by stochastic outlier selection.
"""
if X is None:
if self.X_ is None:
raise Exception("No data")
X = self.X_
log_format = '%(asctime)-15s [%(levelname)s] - %(name)s: %(message)s'
logging.basicConfig(format=log_format, level=logging.INFO)
logger = logging.getLogger('SOS')
if self.verbose:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.ERROR)
if self.standard_scale:
X = StandardScaler().fit_transform(X.copy())
return sos(X, self.metric, self.perplexity, logger=logger)
def test_transformers_data_not_an_array():
# test if transformers do something sensible on training set
# also test all shapes / shape errors
transformers = all_estimators(type_filter='transformer')
X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
random_state=0, n_features=2, cluster_std=0.1)
X = StandardScaler().fit_transform(X)
# We need to make sure that we have non negative data, for things
# like NMF
X -= X.min() - .1
for name, Transformer in transformers:
# XXX: some transformers are transforming the input
# data. This is a bug that we'll fix later. Right now we copy
# the data each time
this_X = NotAnArray(X.copy())
this_y = NotAnArray(np.asarray(y))
if name in dont_test:
continue
# these don't actually fit the data:
if name in ['AdditiveChi2Sampler', 'Binarizer', 'Normalizer']:
continue
# And these wan't multivariate output
if name in ('PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD'):
continue
yield check_transformer, name, Transformer, this_X, this_y
def get_feature_patches(FV, patch_size, patch_shift, input_shape):
FV = StandardScaler(copy=False).fit_transform(FV)
# FV should be of the shape (nFeatures, nFrames)
if any(np.array([9, 10, 21, 22, 39]) == np.shape(FV)[1]):
FV = FV.T
patches = np.empty([])
if np.shape(FV)[1] < patch_size:
FV1 = FV.copy()
while np.shape(FV)[1] <= patch_size:
FV = np.append(FV, FV1, axis=1)
numPatches = int(np.ceil(np.shape(FV)[1] / patch_shift))
patches = PatchExtractor(patch_size=(np.shape(FV)[0], patch_size),
max_patches=numPatches).transform(
np.expand_dims(FV, axis=0))
# print('sklearn splitting: ', time.clock()-startTime, np.shape(patches))
# print('Patches: ', np.shape(patches))
if (np.shape(patches)[1] == 9) or (np.shape(patches)[1] == 10):
diff_dim = input_shape[0] - np.shape(patches)[1]
zero_padding = np.zeros(
(np.shape(patches)[0], diff_dim, np.shape(patches)[2]))
patches = np.append(patches, zero_padding, axis=1)
elif np.shape(patches)[1] == 22:
patches = patches[:, :21, :]
elif np.shape(patches)[1] == 39:
first_7_cep_dim = np.array(
list(range(0, 7)) + list(range(13, 20)) + list(range(26, 33)))
patches = patches[:, first_7_cep_dim, :]
# print('Patches: ', np.shape(patches))
return patches
def get_feature_patches(FV, patch_size, patch_shift, input_shape):
FV = StandardScaler(copy=False).fit_transform(FV)
# FV should be of the shape (nFeatures, nFrames)
if any(np.array([9,10,21,22,39])==np.shape(FV)[1]):
FV = FV.T
patches = np.empty([])
if np.shape(FV)[1]<patch_size:
# print('Size append: ', np.shape(FV), patch_size)
FV1 = FV.copy()
while np.shape(FV)[1]<=patch_size:
FV = np.append(FV, FV1, axis=1)
numPatches = int(np.ceil(np.shape(FV)[1]/patch_shift))
patches = PatchExtractor(patch_size=(np.shape(FV)[0], patch_size), max_patches=numPatches).transform(np.expand_dims(FV, axis=0))
patches_mean = np.mean(patches, axis=2)
patches_var = np.var(patches, axis=2)
patches_mean_var = np.append(patches_mean, patches_var, axis=1)
# print('sklearn splitting: ', time.clock()-startTime, np.shape(patches))
# print('Patches: ', np.shape(patches))
if np.shape(patches_mean_var)[1]!=2*input_shape[0]: # This condition checks for 39CC
if np.shape(patches_mean_var)[1]==44:
patches_mean_var = patches_mean_var[:,list(range(0,21))+list(range(22,43))]
elif np.shape(patches_mean_var)[1]==78:
first_7_cep_dim = np.array(list(range(0,7))+list(range(13,20))+list(range(26,33))+list(range(39,46))+list(range(52,59))+list(range(65,72)))
patches_mean_var = patches_mean_var[:, first_7_cep_dim]
# print('patches_mean_var: ', np.shape(patches_mean_var))
return patches_mean_var
coef = np.zeros(n_features)
coef[:n_relevant_features] = coef_min + rng.rand(n_relevant_features)
# the correction of our design: variables corrected by blocs of 3
corr = np.zeros((n_features, n_features))
for i in range(0, n_features, block_size):
corr[i:i + block_size, i:i + block_size] = 1 - conditioning
corr.flat[::n_features + 1] = 1
corr = linalg.cholesky(corr)
# our design
x = rng.normal(size=(n_samples, n_features))
x = np.dot(x, corr)
x[:n_relevant_features] /= np.abs(linalg.svdvals(
x[:n_relevant_features])).max()
x = StandardScaler().fit_transform(x.copy())
# the output variable
y = np.dot(x, coef)
y /= np.std(y)
y += noise_level * rng.normal(size=n_samples)
mi = mutual_incoherence(x[:, :n_relevant_features],
x[:, n_relevant_features:])
# plot stability selection path, using a high eps for early stopping of the path, to save computing time
alpha_grid, scores_path = lasso_stability_path(x,
y,
random_state=42,
eps=0.05)
plt.figure()
def dj_rec(track_id):
'''get similar songs depending on the audio features'''
neighbors = 4
max_distance = 5.0
'''
[:-10] will return only 10 closest songs to the original track_id
by removing [:-10], code will return 20 songs.
It will take double of time to make a prediction though'''
rel_artists = sp.artist_related_artists(
sp.track(track_id=track_id)['artists'][0]['id'])['artists'][:-10]
artist_log = []
for a in rel_artists:
artist_log.append(a['id'])
feat_log = []
for artist in artist_log:
for track in sp.artist_top_tracks(artist)['tracks']:
feat_log.append(sp.audio_features(track['id'])[0])
catalog = pd.DataFrame.from_dict(feat_log)
root = pd.DataFrame.from_dict(sp.audio_features(tracks=[track_id]))
merged_df = root.append(catalog, ignore_index=True)
dropped_df = merged_df.drop(columns=[
'uri', 'track_href', 'id', 'duration_ms', 'time_signature', 'mode',
'loudness', 'type', 'analysis_url'
])
scaled_df = StandardScaler().fit_transform(dropped_df)
trans_array = scaled_df.copy()
trans_array[:, 0] = [u * 2.4 for u in trans_array[:, 0]] # acousticness
trans_array[:, 1] = [((u * u)**0.5) * u
for u in trans_array[:, 1]] # danceability
trans_array[:, 2] = [u * 1.7 for u in trans_array[:, 2]] # energy
trans_array[:,
3] = [u * 1.4 for u in trans_array[:, 3]] # instrumentalness
trans_array[:, 4] = [u * 0.9 for u in trans_array[:, 4]] # key
trans_array[:, 5] = [u * 1.0 for u in trans_array[:, 5]] # liveness
trans_array[:, 6] = [u * 1.0 for u in trans_array[:, 6]] # speechiness
trans_array[:, 7] = [u * 1.1 for u in trans_array[:, 7]] # tempo
trans_array[:, 8] = [u * 2.5 for u in trans_array[:, 8]] # valence
knn = NearestNeighbors()
knn.fit(trans_array)
rec = knn.kneighbors(trans_array[[0]], n_neighbors=neighbors + 1)
predict_response = []
for n in range(1, neighbors + 1):
if rec[0][0][n] <= max_distance:
pred_dict = (merged_df.loc[rec[1][0][n], 'id'], rec[0][0][n])
predict_response.append(pred_dict)
pred = pd.DataFrame(predict_response,
columns=['recommendation', 'distance'])
df_predict_tracks = pd.DataFrame() # create dataframe
a = [sp.track(ii)['artists'][0]['name'] for ii in pred['recommendation']]
b = [sp.track(ii)['name'] for ii in pred['recommendation']]
c = [sp.track(ii)['id'] for ii in pred['recommendation']]
d = [
sp.track(ii)['external_urls']['spotify']
for ii in pred['recommendation']
]
e = [sp.track(ii)['explicit'] for ii in pred['recommendation']]
f = [sp.track(ii)['preview_url'] for ii in pred['recommendation']]
g = [
sp.track(ii)['album']['images'][1]['url']
for ii in pred['recommendation']
]
# Save the results
df_predict_tracks['artist_name'] = a
df_predict_tracks['song_name'] = b
df_predict_tracks['id'] = c
df_predict_tracks['url'] = d
df_predict_tracks['explicit'] = e
df_predict_tracks['preview'] = f
df_predict_tracks['image'] = g
df_predict_tracks['preview'] = df_predict_tracks['preview'].apply(
get_rid_of_nulls)
df_predict_tracks.index += 1
return json.dumps(json.loads(df_predict_tracks.to_json(orient='index')),
indent=2)
def reduce_dim(no_of_components, U, X):
U_red = U[:, :no_of_components]
X = np.array(X)
Z = np.matmul(U_red.T, X.T)
Z = Z.T
Z_new = pd.DataFrame(Z,
columns=["pc" + str(i) for i in xrange(Z.shape[1])])
return Z_new
# In[4]:
U_copy = U.copy()
X_copy = X.copy()
Z_1 = reduce_dim(17, U_copy, X_copy)
U_copy = U.copy()
X_copy = X.copy()
Z_2 = reduce_dim(26, U_copy, X_copy)
U_copy = U.copy()
X_copy = X.copy()
Z_3 = reduce_dim(38, U_copy, X_copy)
# In[5]:
print Z_1.shape
print Z_2.shape
print Z_3.shape
# x_data = processing.snv(x_data)
x_data = StandardScaler().fit_transform(x_data)
for i, test_model in enumerate(models_to_test):
for j, y_transformation in enumerate(y_transformations):
for k, x_transformation in enumerate(x_transformations):
title = "{0}\ny transformer: {1}" \
"\nx transformer: {2}".format(model_names[i],
transormation_names[j],
transormation_names[k])
if x_transformation:
x_data_new = x_transformation(x_data)
else:
x_data_new = x_data.copy()
if type(y_transformation) == tuple:
_model = TransformedTargetRegressor(
regressor=clone(test_model),
func=y_transformation[0],
inverse_func=y_transformation[1])
elif y_transformation:
_model = TransformedTargetRegressor(
regressor=clone(test_model), transformer=y_transformation)
else:
_model = clone(test_model)
print(title)
try:
Y['AGE2'] = Y['AGE']**2
# Y['SITE'] = Y['SITE'].astype('category').cat.codes
# Y['PTRACCAT'] = Y['PTRACCAT'].astype('category').cat.codes
Y['PTGENDER'] = Y['PTGENDER'].astype('category').cat.codes
Y = Y.fillna(Y.mean())
print(Y.shape)
print(X.shape)
print(icv.shape)
W = np.linalg.inv(Y.T.dot(Y)).dot(Y.T.dot(X))
# Substract effect of age
X = X - Y.dot(W)
# Save_corrected_data: cdf
cdf = X.copy()
cdf.columns = cols
cdf['label'] = df['label']
corrected_csv = join(data_csv[:-4] + '_corrected.csv')
cdf.to_csv(corrected_csv)
print('CSV saved!')
# Reduce dimmensionality: X_ld
n_comp = 5 if X.shape[1] > 5 else X.shape[1]
X_ld = PCA(n_components=n_comp).fit_transform(X)
#Convert in DataFrame
cols = ['PC%d' % (c + 1) for c in range(X_ld.shape[1])]
X_ld = pd.DataFrame(data=X_ld, columns=cols, index=df.index)
X_ld['label'] = df['label']
X_ld = X_ld[(X_ld['label'] == 'MCIc') | (X_ld['label'] == 'MCInc')]
def get_subsampling_index2(data_process, standard_scale = True, cutoff_sig = 0.02, rate = 0.3, \
method = "pykdtree", verbose = 1):
"""
Using Nearest-Neighbor search based algorithm, find the list of indices of the subsampled dataset
Parameters
-------------
data_process: List. the list of datapoints, with selected features
standard_scale [True]: Boolean. Whether to apply standard scaler to the dataset prior to subsampling
cutoff_sig [0.02]: Float. cutoff significance. the cutoff distance equals to the Euclidean
norm of the standard deviations in all dimensions of the data points
rate [0.3]: Float. possibility of deletion
method ["pykdtree"]: String. which backend nearest neighbour model to use.
possible choices: ["pykdtree", "nmslib", "sklearn", "scipy", "annoy", "flann"]
verbose [1]: integer. level of verbosity
Return
-------------
overall_keep_list: The list of indices of the final subsampled entries
"""
if verbose >= 1:
print("Started NN-subsampling, original length: {}".format(
len(data_process)))
method = method.lower()
start = time.time()
if method == "flann":
if verbose >= 1:
print("use flann backend")
elif method == "pykdtree":
if verbose >= 1:
print("use pykdtree backend")
elif method == "sklearn":
if verbose >= 1:
print("use slearn nearest neighbors backend")
elif method == "scipy":
if verbose >= 1:
print("use scipy cKDTree backend")
elif method == "annoy":
if verbose >= 1:
print("use annoy backend")
elif method == "nmslib":
if verbose >= 1:
print("use nmslib backend")
else:
print("method {} not impletemented".format(method))
raise NotImplemented
# apply standard scaling
if standard_scale:
if verbose >= 2:
print("Subample with standard scaled data")
data_process = StandardScaler().fit_transform(
np.asarray(data_process).copy())
else:
if verbose >= 2:
print("Subample with original data")
data_process = np.asarray(data_process).copy()
#set cutoff distance
list_of_descs = zip(*data_process)
sum_std2 = 0.
for descs in list_of_descs:
temp_std = np.std(descs)
sum_std2 += temp_std**2
cutoff = cutoff_sig * np.sqrt(sum_std2)
#initialize the index
overall_keep_list = np.arange(len(data_process)).tolist()
keep_going = True
iter_count = 1
while keep_going:
if verbose >= 2:
print('start iteration {}, total length: {}'.format(
iter_count, len(overall_keep_list)))
start_cycle = time.time()
temp_data_process = get_array_based_on_index(data_process.copy(),
overall_keep_list)
#build and query nearest neighbour model
if method == "flann":
flann = FLANN()
indices, distances = flann.nn(temp_data_process,
temp_data_process,
2,
algorithm="kmeans")
elif method == "scipy":
kd_tree = cKDTree(temp_data_process)
distances, indices = kd_tree.query(temp_data_process, k=2)
elif method == "pykdtree":
kd_tree = KDTree(temp_data_process, leafsize=6)
distances, indices = kd_tree.query(temp_data_process, k=2)
elif method == "sklearn":
nbrs = NearestNeighbors(n_neighbors=2,
algorithm='kd_tree',
n_jobs=-1).fit(temp_data_process)
distances, indices = nbrs.kneighbors(temp_data_process)
elif method == "annoy":
annoy = AnnoyIndex(len(temp_data_process[0]), metric='euclidean')
for i in range(len(temp_data_process)):
annoy.add_item(i, temp_data_process[i])
annoy.build(1)
distances = []
indices = []
for i in range(len(temp_data_process)):
temp_index, temp_dist = annoy.get_nns_by_vector(
temp_data_process[i], 2, include_distances=True)
indices.append([i, temp_index[1]])
distances.append([0.0, temp_dist[1]])
elif method == "nmslib":
index = nmslib.init(method='hnsw', space='l2')
index.addDataPointBatch(temp_data_process)
index.createIndex(print_progress=False)
neighbours = index.knnQueryBatch(temp_data_process, k=2)
distances = []
indices = []
for item in neighbours:
indices.append(item[0])
distances.append(item[1])
else:
raise NotImplemented
# if distance between each point and its nearest neighbor is below cutoff distance,
# add the nearest neighbout to the candidate removal list
remove_index_li = []
index_li = []
for index, distance in zip(indices, distances):
index_li.append(index[0])
if distance[1] <= cutoff:
remove_index_li.append(index[1])
# randomly select datapoints in the candidate removal list (based on rate)
# and form the final removal list of this iteration
# stop the cycle if the final removal list is empty
temp_num = int(ceil(float(len(remove_index_li)) * rate))
if temp_num == 0:
keep_going = False
remove_index_li = random_subsampling(remove_index_li, temp_num)
temp_keep_list = remove_list_from_list(index_li, remove_index_li)
overall_keep_list = [overall_keep_list[i] for i in temp_keep_list]
if verbose >= 2:
print('end iteration {}. length: {}\t time:{}'.format(
iter_count, len(overall_keep_list),
time.time() - start_cycle))
iter_count += 1
if verbose >= 1:
print('end NN-subsampling. length: {}\t time:{}'.format(
len(overall_keep_list),
time.time() - start))
return overall_keep_list
class Cell2Patients():
def __init__(self,
clf,
threshold,
clf_transfer=None,
max_run=None,
out_dir="./semi_supervised/",
field_separator="\t",
increment_rate=10,
verbose=True,
normalize=False):
self._clf = clone(clf)
if clf_transfer == None:
self._clf_transfer = clone(clf)
else:
self._clf_transfer = clf_transfer
self._normalize = normalize
self._increment_rate = increment_rate
self._max_run = max_run
self._out_dir = os.path.realpath(out_dir)
os.makedirs(out_dir, exist_ok=True)
self._Xl = []
self._Xu = []
self._names_l = []
self._features_l = []
self._names_u = []
self._features_u = []
self._Y = []
self._patient_added = []
self._verbose = verbose
self._FS = field_separator
self._leftover = ""
self._thr = threshold
if self._thr < 0.51 or self._thr > 0.99:
raise ValueError("Threshold must be between 0.51 and 0.99")
def _mex(self, txt, end="\n"):
if self._verbose:
print(txt, flush=True, end=end)
else:
if end == "\n":
logger.debug("{}{}".format(self._leftover, txt))
self._leftover = ""
else:
self._leftover = txt
def import_data(self, input_file, labelled=True):
if labelled:
X, features, names, Y = self._Xl, self._features_l, self._names_l, self._Y
else:
X, features, names, Y = self._Xu, self._features_u, self._names_u, None
if not os.path.exists(input_file):
raise FileNotFoundError("File {} not found!".format(input_file))
head_n = 2 if labelled else 1
with open(input_file, "r") as f:
while head_n != 0:
line = [v.strip() for v in f.readline().split(self._FS)]
if line[0] == "group" or line[0] == "label":
for idx in range(1, len(line)):
Y.append(line[idx])
elif line[0] == "name":
for idx in range(1, len(line)):
names.append(line[idx])
else:
raise ValueError(
"File {} is not well formatted. Found {} as first field, should be 'name' or 'group'."
.format(input_file, line[0]))
head_n -= 1
line = f.readline()
while line:
line = [v.strip() for v in line.split(self._FS)]
if len(line) == len(names) + 1:
features.append(line[0])
vals = []
for idx in range(1, len(line)):
try:
vals.append(float(line[idx]))
except ValueError:
vals.append("NaN")
X.append(vals)
line = f.readline()
def _initData(self):
self._class_names = sorted(list(set(self._Y)), reverse=True)
if len(self._class_names) != 2:
raise ValueError(
"Cell2Patients works only with binary classification.\n{} labels found: {}"
.format(len(self._class_names),
", ".join(list(self._class_names))))
for i in range(len(self._Y)):
self._Y[i] = self._class_names.index(self._Y[i])
if sorted(self._features_l) != sorted(self._features_u):
raise ValueError(
"Features in labelled and unlabelled are not the same!")
self._features = self._features_l.copy()
for i in range(len(self._features_l)):
self._features_l[i] = self._features.index(self._features_l[i])
for i in range(len(self._features_u)):
self._features_u[i] = self._features.index(self._features_u[i])
warnings.filterwarnings("ignore")
self._Y = np.asarray(self._Y)
self._Xl = SimpleImputer(strategy="mean").fit_transform(
np.array(self._Xl)[self._features_l, :].T)
self._Xu = SimpleImputer(strategy="mean").fit_transform(
np.array(self._Xu)[self._features_u, :].T)
if self._normalize:
self._Xl = StandardScaler().fit_transform(self._Xl)
self._Xu = StandardScaler().fit_transform(self._Xu)
self._Ypatients = [-1 for _ in self._names_u]
self._mex("Working with {} {} and {} {} cell lines".format(
sum(self._Y), self._class_names[1],
len(self._Y) - sum(self._Y), self._class_names[1]))
def run(self):
self._initData()
self._mex(
"Starting the tranfer learning of {} cell lines to {} patients".
format(len(self._names_l), len(self._names_u)))
self._patients_proba = []
self._cell_proba = []
running = True
gen = 0
Xl = self._Xl.copy()
Y = self._Y.copy()
self._feature_importances = []
transferred = []
prev_novel = [1, 1]
while running:
gen += 1
self._mex("-" * 20)
self._mex("Training the generation number {}".format(gen))
if gen == 1:
clf = clone(self._clf)
else:
clf = clone(self._clf_transfer)
clf.fit(Xl, Y)
self._feature_importances.append(clf.feature_importances_)
newX, newY, novel = self._evaluate(clf, transferred,
gen * self._increment_rate)
tot_pos = sum(
[1 if v >= self._thr else 0 for v in self._patients_proba[-1]])
if novel[0] + novel[1] == 0:
self._mex("No patients added.")
running = False
elif (prev_novel[0] == 0 and novel[0] == 0 and tot_pos == 0):
self._mex(
"Model is overfitting over {}. Stopping the iteration.".
format(self._class_names[1]))
running = False
elif (prev_novel[1] == 0 and novel[1] == 0
and tot_pos == len(self._patients_proba[-1])):
self._mex(
"Model is overfitting over {}. Stopping the iteration.".
format(self._class_names[0]))
running = False
else:
self._mex("Adding {} patients, {} novel:".format(
len(newY), novel[0] + novel[1]))
self._mex(" - {} labelled as {} ( {} novel ) ".format(
len(newY) - sum(newY), self._class_names[0], novel[0]))
self._mex(" - {} labelled as {} ( {} novel ) ".format(
sum(newY), self._class_names[1], novel[1]))
self._patient_added.append([len(newY) - sum(newY), sum(newY)])
Xl = np.append(self._Xl, newX, axis=0)
Y = np.append(self._Y, newY, axis=0)
prev_novel = novel.copy()
self._mex("-" * 20)
self._write_results()
return self.get_patient_labelled_data()
def _evaluate(self, clf, transferred, limit):
proba = clf.predict_proba(self._Xu)[:, 0]
self._patients_proba.append(proba.copy())
self._cell_proba.append(clf.predict_proba(self._Xl)[:, 0])
newX, newY = [], []
proba = sorted(enumerate(proba), key=lambda tup: tup[1])
novel = [0, 0]
i = min(limit, len(proba)) - 1
while i >= 0:
if 1 - proba[i][1] >= self._thr:
newX.append(list(self._Xu[proba[i][0], :]))
newY.append(1)
if not proba[i][0] in transferred:
novel[1] += 1
transferred.append(proba[i][0])
self._Ypatients[proba[i][0]] = 1
i -= 1
i = len(proba) - 1
n = 0
while n < limit and proba[i][1] >= self._thr:
newX.append(list(self._Xu[proba[i][0], :]))
newY.append(0)
if not proba[i][0] in transferred:
novel[0] += 1
transferred.append(proba[i][0])
self._Ypatients[proba[i][0]] = 0
n += 1
i -= 1
return np.array(newX), np.array(newY), novel
def _plot_importances(self, pdf):
plt.close()
top_10 = [[
v[0] for v in sorted(enumerate(self._feature_importances[0]),
key=lambda tup: tup[1],
reverse=True)[:10]
],
[
v[0]
for v in sorted(enumerate(self._feature_importances[-1]),
key=lambda tup: tup[1],
reverse=True)[:10]
]]
for idx in top_10[0]:
plt.plot(range(len(self._feature_importances)),
[v[idx] for v in self._feature_importances],
"-r",
label="Top10 Begin")
for idx in top_10[1]:
plt.plot(range(len(self._feature_importances)),
[v[idx] for v in self._feature_importances],
"--b",
label="Top10 End")
plt.xlabel('Generation')
plt.ylabel('Feature Importance')
handles, labels = plt.gca().get_legend_handles_labels()
newLabels, newHandles = [], []
for handle, label in zip(handles, labels):
if label not in newLabels:
newLabels.append(label)
newHandles.append(handle)
plt.legend(newHandles, newLabels)
plt.title("Top10 feature importance evolution")
plt.savefig(pdf, format="pdf")
return
def _plot_bar(self, pdf):
plt.close()
plt.bar([v - 0.21 for v in range(1,
len(self._patient_added) + 1)],
[v[0] for v in self._patient_added],
width=0.40,
color="#F8766D",
label=self._class_names[0])
plt.bar([v + 0.21 for v in range(1,
len(self._patient_added) + 1)],
[v[1] for v in self._patient_added],
width=0.40,
color="#00BFC4",
label=self._class_names[1])
plt.xlabel("Round")
plt.ylabel("Number of patients added")
plt.title("Patients added each round")
plt.savefig(pdf, format="pdf")
handles, labels = plt.gca().get_legend_handles_labels()
newLabels, newHandles = [], []
for handle, label in zip(handles, labels):
if label not in newLabels:
newLabels.append(label)
newHandles.append(handle)
plt.legend(newHandles, newLabels)
plt.legend()
plt.close()
def _plotPCA(self, pdf):
X_pca = PCA(n_components=2, svd_solver='full').fit_transform(self._Xl)
plt.close()
colors = ['#F8766D', '#00BFC4', 'grey']
for i in range(2):
plt.scatter(X_pca[self._Y == i, 0],
X_pca[self._Y == i, 1],
color=colors[i],
lw=2,
label="{}".format(self._class_names[i]))
plt.legend()
plt.title("Cell lines PCA")
plt.savefig(pdf, format="pdf")
plt.close()
X_pca = PCA(n_components=2, svd_solver='full').fit_transform(self._Xu)
class_names = self._class_names.copy()
class_names.append("Unclassified")
Y = np.array(self._Ypatients)
for i in range(-1, 2):
plt.scatter(X_pca[Y == i, 0],
X_pca[Y == i, 1],
color=colors[i],
lw=2,
label="{}".format(class_names[i]))
plt.legend()
plt.title("Patients PCA")
plt.savefig(pdf, format="pdf")
plt.close()
def _plot_proba(self, pdf):
plt.close()
n_gen = len(self._patients_proba)
plt.plot(range(n_gen), [self._thr for _ in range(n_gen)],
"--",
color="grey")
plt.plot(range(n_gen), [1 - self._thr for _ in range(n_gen)],
"--",
color="grey")
for gen in range(n_gen):
blue, red, grey = [], [], []
for p in self._patients_proba[gen]:
if p >= self._thr:
red.append(p)
elif p <= 1 - self._thr:
blue.append(p)
else:
grey.append(p)
if len(red) > 0:
plt.plot([
gen + ((np.random.rand() - np.random.rand()) * 0.3)
for _ in range(len(red))
],
red,
".",
color="#F8766D",
label=self._class_names[0])
if len(blue) > 0:
plt.plot([
gen + ((np.random.rand() - np.random.rand()) * 0.3)
for _ in range(len(blue))
],
blue,
".",
color="#00BFC4",
label=self._class_names[1])
if len(grey) > 0:
plt.plot([
gen + ((np.random.rand() - np.random.rand()) * 0.3)
for _ in range(len(grey))
],
grey,
".",
color="grey",
label="Unclassified")
plt.xlabel("Generation")
plt.ylabel("{} Probability".format(self._class_names[0]))
plt.ylim(0, 1)
plt.title("Patients probabilities")
handles, labels = plt.gca().get_legend_handles_labels()
newLabels, newHandles = [], []
for handle, label in zip(handles, labels):
if label not in newLabels:
newLabels.append(label)
newHandles.append(handle)
plt.legend(newHandles, newLabels)
plt.savefig(pdf, format="pdf")
plt.close()
return
def _plot_proba_cells(self, pdf):
plt.close()
n_gen = len(self._cell_proba)
plt.plot(range(n_gen), [0.6 for _ in range(n_gen)], "--", color="grey")
plt.plot(range(n_gen), [0.4 for _ in range(n_gen)], "--", color="grey")
for gen in range(n_gen):
blue, red, grey = [], [], []
for p in self._cell_proba[gen]:
if p >= 0.6:
red.append(p)
elif p <= 0.4:
blue.append(p)
else:
grey.append(p)
if len(red) > 0:
plt.plot([
gen + ((np.random.rand() - np.random.rand()) * 0.3)
for _ in range(len(red))
],
red,
".",
color="#F8766D")
if len(blue) > 0:
plt.plot([
gen + ((np.random.rand() - np.random.rand()) * 0.3)
for _ in range(len(blue))
],
blue,
".",
color="#00BFC4")
if len(grey) > 0:
plt.plot([
gen + ((np.random.rand() - np.random.rand()) * 0.3)
for _ in range(len(grey))
],
grey,
".",
color="grey")
plt.xlabel("Generation")
plt.ylabel("{} Probability".format(self._class_names[0]))
plt.title("Cell lines probabilities")
plt.ylim(0, 1)
plt.savefig(pdf, format="pdf")
plt.close()
return
def get_patient_labelled_data(self):
X = []
Y = []
for p in range(len(self._names_u)):
if self._Ypatients[p] != -1:
Y.append(self._Ypatients[p])
X.append(self._Xu[p, :])
return np.array(X), np.array(Y)
def get_patient_unlabelled_data(self):
X = []
for p in range(len(self._names_u)):
if self._Ypatients[p] == -1:
X.append(self._Xu[p, :])
return np.array(X)
def _write_results(self):
pdf = PdfPages("{}/graphs.pdf".format(self._out_dir))
self._plot_importances(pdf)
self._plot_proba(pdf)
self._plot_proba_cells(pdf)
self._plot_bar(pdf)
self._plotPCA(pdf)
pdf.close()
with open("{}/feature_importances.tsv".format(self._out_dir),
"w") as ofs:
ofs.write("Generation\tFeatureName\tImportance\n")
for gen in range(len(self._feature_importances)):
for feat in range(len(self._features)):
ofs.write("{}\t{}\t{:.3f}\n".format(
gen, self._features[feat],
self._feature_importances[gen][feat]))
with open("{}/patients_probabilities.tsv".format(self._out_dir),
"w") as ofs:
ofs.write(
"Generation\tPatientName\tProbability_{}\tProbability_{}\tGroup\n"
.format(self._class_names[0], self._class_names[1]))
for gen in range(len(self._patients_proba)):
for p in range(len(self._names_u)):
ofs.write("{}\t{}\t{}\t{}\n".format(
gen, self._names_u[p], self._patients_proba[gen][p],
1 - self._patients_proba[gen][p]))
with open("{}/cells_probabilities.tsv".format(self._out_dir),
"w") as ofs:
ofs.write("Generation\tCellName\tProbability_{}\tProbability_{}\n".
format(self._class_names[0], self._class_names[1]))
for gen in range(len(self._cell_proba)):
for p in range(len(self._names_l)):
ofs.write("{}\t{}\t{}\t{}\n".format(
gen, self._names_l[p], self._cell_proba[gen][p],
1 - self._cell_proba[gen][p]))
with open("{}/patients_labels.tsv".format(self._out_dir), "w") as ofs:
ofs.write("Name\tLabel\n")
for i in range(len(self._names_u)):
ofs.write("{}\t{}\n".format(
self._names_u[i], self._class_names[self._Ypatients[i]]
if self._Ypatients[i] != -1 else "NA"))
def process_data(regressor_name, X, normalise=None):
if regressor_name in classical_ml:
tmp = []
for i in tqdm(range(len(X))):
# 1. flatten
# 2. fill missing values
x = X.iloc[i, 0].reset_index(drop=True)
x.interpolate(method='linear',
inplace=True,
limit_direction='both')
if normalise == "standard":
x = StandardScaler().fit_transform(x.values.reshape(-1, 1))
x = pd.DataFrame(x)
elif normalise == "minmax":
x = MinMaxScaler().fit_transform(x.values.reshape(-1, 1))
x = pd.DataFrame(x)
tmp2 = x.values.tolist()
for j in range(1, len(X.columns)):
x = X.iloc[i, j].reset_index(drop=True)
x.interpolate(method='linear',
inplace=True,
limit_direction='both')
if normalise == "standard":
x = StandardScaler().fit_transform(x.values.reshape(-1, 1))
x = pd.DataFrame(x)
elif normalise == "minmax":
x = MinMaxScaler().fit_transform(x.values.reshape(-1, 1))
x = pd.DataFrame(x)
tmp2 = tmp2 + x.values.tolist()
tmp2 = pd.DataFrame(tmp2).transpose()
tmp.append(tmp2)
X = pd.concat(tmp).reset_index(drop=True)
else:
tmp = []
for i in tqdm(range(len(X))):
x = X.iloc[i, :]
_x = x.copy(deep=True)
# 1. find the maximum length of each dimension
all_len = [len(y) for y in _x]
max_len = max(all_len)
# 2. adjust the length of each dimension
_y = []
for y in _x:
# 2.1 fill missing values
if y.isnull().any():
y = y.interpolate(method='linear', limit_direction='both')
# 2.2. if length of each dimension is different, uniformly scale the shorted one to the max length
if len(y) < max_len:
y = uniform_scaling(y, max_len)
_y.append(y)
_y = np.array(np.transpose(_y))
if normalise == "standard":
scaler = StandardScaler().fit(_y)
_y = scaler.transform(_y)
if normalise == "minmax":
scaler = MinMaxScaler().fit(_y)
_y = scaler.transform(_y)
tmp.append(_y)
X = np.array(tmp)
return X
clf2.fit(X_train)
clf3 = AutoEncoder(hidden_neurons =[25, 15, 10, 2, 10,15, 25])
clf3.fit(X_train)
# Predict the anomaly scores
y_test_scores = clf1.decision_function(X_test)
y_test_scores = pd.Series(y_test_scores)
# Step 2: Determine the cut point
import matplotlib.pyplot as plt
plt.hist(y_test_scores, bins='auto')
plt.title("Histogram with Model Clf3 Anomaly Scores")
plt.show()
df_test = X_test.copy()
df_test['score'] = y_test_scores
df_test['cluster'] = np.where(df_test['score']<4, 0, 1)
df_test['cluster'].value_counts()
# Step 3: Get the summary statistics by cluster
df_test.groupby('cluster').mean()
# ensemble
# Put all the predictions in a data frame
from pyod.models.combination import aom, moa, average, maximization
# Put all the predictions in a data frame
train_scores = pd.DataFrame({'clf1': clf1.decision_scores_,
def get_feature_patches(PARAMS, FV, patch_size, patch_shift, input_shape):
# Removing NaN and Inf
if any(np.array([9, 10, 21, 22, 39]) == np.shape(FV)[1]):
FV = FV[~np.isnan(FV).any(axis=1), :]
FV = FV[~np.isinf(FV).any(axis=1), :]
else:
FV = FV[:, ~np.isnan(FV).any(axis=0)]
FV = FV[:, ~np.isinf(FV).any(axis=0)]
FV = StandardScaler(copy=False).fit_transform(FV)
# FV should be of the shape (nFeatures, nFrames)
if any(np.array([9, 10, 21, 22, 39]) == np.shape(FV)[1]):
FV = FV.T
frmStart = 0
frmEnd = 0
patchNum = 0
patches = np.empty([])
if np.shape(FV)[1] < patch_size:
FV1 = FV.copy()
while np.shape(FV)[1] <= patch_size:
FV = np.append(FV, FV1, axis=1)
# while frmEnd<np.shape(FV)[1]:
# # print('get_feature_patches: ', frmStart, frmEnd, np.shape(FV))
# frmStart = patchNum*patch_shift
# frmEnd = np.min([patchNum*patch_shift+patch_size, np.shape(FV)[1]])
# if frmEnd-frmStart<patch_size:
# frmStart = frmEnd - patch_size
# if np.size(patches)<=1:
# patches = np.expand_dims(FV[:, frmStart:frmEnd], axis=0)
# else:
# patches = np.append(patches, np.expand_dims(FV[:, frmStart:frmEnd], axis=0), axis=0)
# patchNum += 1
# startTime = time.clock()
# for frmStart in range(0, np.shape(FV)[1], patch_shift):
# # print('get_feature_patches: ', frmStart, frmEnd, np.shape(FV))
# frmEnd = np.min([frmStart+patch_size, np.shape(FV)[1]])
# if frmEnd-frmStart<patch_size:
# frmStart = frmEnd - patch_size
# if np.size(patches)<=1:
# patches = np.array(FV[:, frmStart:frmEnd], ndmin=3)
# else:
# patches = np.append(patches, np.array(FV[:, frmStart:frmEnd], ndmin=3), axis=0)
# print('My splitting: ', time.clock()-startTime, np.shape(patches))
startTime = time.clock()
numPatches = int(np.ceil(np.shape(FV)[1] / patch_shift))
patches = PatchExtractor(patch_size=(np.shape(FV)[0], patch_size),
max_patches=numPatches).transform(
np.expand_dims(FV, axis=0))
# print('sklearn splitting: ', time.clock()-startTime, np.shape(patches))
# print('Patches: ', np.shape(patches))
if (np.shape(patches)[1] == 9) or (np.shape(patches)[1] == 10):
diff_dim = input_shape[0] - np.shape(patches)[1]
zero_padding = np.zeros(
(np.shape(patches)[0], diff_dim, np.shape(patches)[2]))
patches = np.append(patches, zero_padding, axis=1)
elif np.shape(patches)[1] == 22:
patches = patches[:, :21, :]
elif np.shape(patches)[1] == 39:
if not PARAMS['39_dim_CC_feat']:
first_7_cep_dim = np.array(
list(range(0, 7)) + list(range(13, 20)) + list(range(26, 33)))
patches = patches[:, first_7_cep_dim, :]
# print('Patches: ', np.shape(patches))
return patches
from tempfile import mkdtemp
from functools import wraps
import pytest
from sklearn import datasets
import warnings
n_clusters = 3
# X = generate_clustered_data(n_clusters=n_clusters, n_samples_per_cluster=50)
X, y = make_blobs(n_samples=200, random_state=10)
X, y = shuffle(X, y, random_state=7)
X = StandardScaler().fit_transform(X)
X_missing_data = X.copy()
X_missing_data[0] = [np.nan, 1]
X_missing_data[5] = [np.nan, np.nan]
def test_missing_data():
"""Tests if nan data are treated as infinite distance from all other points and assigned to -1 cluster"""
model = HDBSCAN().fit(X_missing_data)
assert model.labels_[0] == -1
assert model.labels_[5] == -1
assert model.probabilities_[0] == 0
assert model.probabilities_[5] == 0
assert model.probabilities_[5] == 0
clean_indices = list(range(1, 5)) + list(range(6, 200))
clean_model = HDBSCAN().fit(X_missing_data[clean_indices])
assert np.allclose(clean_model.labels_, model.labels_[clean_indices])
def load_data(path, file_name):
with open(path + file_name + '.pickle', 'rb') as handle:
data = pickle.load(handle)
return data
# -------------------------------------------------------------------------------------------------------------
orient = 9
pix_per_cell = 8
cell_per_block = 2
hist_bins = 32
X_scaler = StandardScaler()
X_scaler.copy = True
X_scaler.with_mean = True
X_scaler.with_std = True
filename = 'svm_model.sav'
data_vehicle = load_data("./data/", "Vehicle")
data_no_vehicle = load_data("./data/", "No_Vehicle")
ones = np.ones(len(data_vehicle))
zeros = np.zeros(len(data_no_vehicle))
feature_list = [ones, zeros]
label = np.hstack(feature_list)
# for idx in range(len(data_no_vehicle)):
# img = data_no_vehicle[idx]
coef[:n_relevant_features] = coef_min + rng.rand(n_relevant_features)
# The correlation of our design: variables correlated by blocs of 3
corr = np.zeros((n_features, n_features))
for i in range(0, n_features, block_size):
corr[i:i + block_size, i:i + block_size] = 1 - conditioning
corr.flat[::n_features + 1] = 1
corr = linalg.cholesky(corr)
# Our design
X = rng.normal(size=(n_samples, n_features))
X = np.dot(X, corr)
# Keep [Wainwright2006] (26c) constant
X[:n_relevant_features] /= np.abs(
linalg.svdvals(X[:n_relevant_features])).max()
X = StandardScaler().fit_transform(X.copy())
# The output variable
y = np.dot(X, coef)
y /= np.std(y)
# We scale the added noise as a function of the average correlation
# between the design and the output variable
y += noise_level * rng.normal(size=n_samples)
mi = mutual_incoherence(X[:, :n_relevant_features],
X[:, n_relevant_features:])
###########################################################################
# Plot stability selection path, using a high eps for early stopping
# of the path, to save computation time
alpha_grid, scores_path = lasso_stability_path(X, y, random_state=42,
eps=0.05)
keras.layers.Dense(240, activation=tf.nn.relu),
keras.layers.Dropout(0.2),
keras.layers.Dense(240, activation=tf.nn.relu),
keras.layers.Dropout(0.1),
keras.layers.Dense(240, activation=tf.nn.tanh),
keras.layers.Dropout(0.1),
keras.layers.Dense(5, activation=tf.nn.softmax),
])
model.name = 'model' + str(cont)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
### drop outliers
tmp = pd.DataFrame(data=X.copy())
tmp.insert(0, column='y', value=y)
X_curr, y_curr = drop_outliers(tmp, cont)
# model.fit(X_curr, y_curr, epochs = 50)
model.fit(X_curr, y_curr, epochs=200)
# results[cont] = model.evaluate(X_test, y_test)
# print("done with ", model.name)
# print(results[cont])
# results ={}
# # exit()
