except:
warnings.warn(f'{date_str} is not a valid date, sample will be ignored')
date_str = '01/01/1800'
return datetime.datetime.strptime(date_str, '%d/%m/%Y')
def get_days(days_datetime):
return days_datetime.days
n_components = 20
use_recorded = False
if not use_recorded:
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, 'feature-table_Allergy_cleaned_taxa_290119_updated_in_140219.csv'),
os.path.join(SCRIPT_DIR, 'mf_merge_ok84_ok93_ok66_69_merged_by_RestoredSampleCode_as_ID_290119.csv'),
from_QIIME=True, id_col='Feature ID', taxonomy_col='Taxonomy')
preproccessed_data = preprocess_data(OtuMf.otu_file, visualize_data=False, taxnomy_level=5, taxonomy_col='Taxonomy',
preform_taxnomy_group=True)
otu_after_pca_wo_taxonomy, _, _ = apply_pca(preproccessed_data, n_components=n_components, visualize=False)
######## Pre process (Remove control group) ########
column_to_use_for_filter = 'AllergyTypeData131118'
OtuMf.mapping_file = OtuMf.mapping_file.loc[OtuMf.mapping_file['AllergyTypeData131118'] != 'Con']
######## get date of sample in date format ########
date_of_sample_col = 'Date'
OtuMf.mapping_file['Date_of_sample'] = OtuMf.mapping_file[date_of_sample_col].apply(get_datetime, time_format='%m/%d/%y')
######## remove invalid subjects (those who had samples with no dates or bad dates) ########
# bad dates
def _read_file(self, TITLE, PRINT, REG):
bactria_as_feature_file = 'feature-table_Allergy_cleaned_taxa_290119_updated_in_140219.csv'
features = pd.read_csv(bactria_as_feature_file, header=1)
cols = list(features.columns)
# remove non-numeric values
cols.remove('Feature ID')
cols.remove('Taxonomy')
if REG:
self.reg(features, cols)
samples_data_file = 'mf_merge_ok84_ok93_ok66_69_merged_by_RestoredSampleCode_as_ID_290119.csv'
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, bactria_as_feature_file),
os.path.join(SCRIPT_DIR, samples_data_file),
from_QIIME=True,
id_col='Feature ID',
taxonomy_col='Taxonomy')
preproccessed_data = preprocess_data(OtuMf.otu_file,
visualize_data=False,
taxnomy_level=6,
taxonomy_col='Taxonomy',
preform_taxnomy_group=True)
self._preproccessed_data = preproccessed_data
# drow_data(preproccessed_data)
# otu_after_pca_wo_taxonomy, _, _ = apply_pca(data_after_log_zcore, n_components=40, visualize=False)
otu_after_pca_wo_taxonomy, pca_obj, _ = apply_pca(
preproccessed_data, n_components=n_components, visualize=False)
control = otu_after_pca_wo_taxonomy.index[0:62] # 'Con'
self._pca_obj = pca_obj
# if we want to remove the healthy samples that are used for control
# otu_after_pca_wo_taxonomy = otu_after_pca_wo_taxonomy.drop(preproccessed_data.index[0:62])
index_to_id_map = {}
id_to_features_map = {}
for i, row in enumerate(otu_after_pca_wo_taxonomy.values):
id_to_features_map[otu_after_pca_wo_taxonomy.index[i]] = row
index_to_id_map[i] = otu_after_pca_wo_taxonomy.index[i]
self._index_to_id_map = index_to_id_map
self._id_to_features_map = id_to_features_map
success_tag_column = 'SuccessDescription'
stages_column = 'TreatmentTimePoint'
allergan_column = 'AllergyType'
code_column = 'ParticipentCode'
ids_list_w_con = otu_after_pca_wo_taxonomy.index.tolist()
ids_list_wo_con = otu_after_pca_wo_taxonomy.index.drop(
otu_after_pca_wo_taxonomy.index[0:62])
self._ids_list_w_con = ids_list_w_con
self._ids_list_wo_con = ids_list_wo_con
stages = []
# ##### separate samples by allergic and healthy==>'Con'
id_to_binary_health_tag_map = {}
for sample in ids_list_w_con:
if sample.startswith('Con'):
id_to_binary_health_tag_map[sample] = 1
else:
id_to_binary_health_tag_map[sample] = 0
self._id_to_binary_health_tag_map = id_to_binary_health_tag_map
# ##### separate samples by stage, success of treatment and allergen type
id_to_success_tag_map = {}
id_to_stage_map = {}
id_to_binary_success_tag_map = {}
id_to_allergy_type_tag_map = {}
id_to_allergy_number_type_tag_map = {}
id_to_milk_allergy_tag_map = {}
allergan_types = set()
tag_to_allergy_type_map = {
0: 'Milk',
1: 'Tree_nut', # 'Cashew' + 'Hazelnut' + 'Walnut'
2: 'Peanut',
3: 'Sesame'
} # removed 'Egg' samples
allergy_type_to_instances_map = {
'Milk': 0,
'Tree_nut': 0,
'Peanut': 0,
'Sesame': 0
} # 'Non': 9 samples, 'Egg': 35 samples
"""
nuts_samples_list = []
for sample in ids_list_wo_con:
a = OtuMf.mapping_file.loc[sample, allergan_column]
if a == 'Nuts':
nuts_samples_list.append(sample)
with open("nuts_samples.txt", "w") as file:
for l in nuts_samples_list:
file.write(l + "\n")
"""
non_allergy_type_ids = []
egg_allergy_type_ids = []
for sample in ids_list_wo_con:
s = OtuMf.mapping_file.loc[sample, stages_column]
# stages
stages.append(s)
id_to_stage_map[sample] = s
stage_0_ids = [
key for key in id_to_stage_map
if id_to_stage_map[key] == '0_before'
]
self._stage_0_ids = stage_0_ids
# success
t = OtuMf.mapping_file.loc[sample, success_tag_column]
id_to_success_tag_map[sample] = t
# save tags from k-classes as success(A1)->1 and failure(the rest)->0
if t == 'A1':
id_to_binary_success_tag_map[sample] = 1
else:
id_to_binary_success_tag_map[sample] = 0
# allergy type
a = OtuMf.mapping_file.loc[sample, allergan_column]
allergan_types.add(a)
id_to_allergy_type_tag_map[sample] = a
if a == 'Milk' or a == 'Milk_suspected' or a == 'milk':
id_to_allergy_number_type_tag_map[sample] = 0
id_to_milk_allergy_tag_map[sample] = 1
allergy_type_to_instances_map[
'Milk'] = allergy_type_to_instances_map.get('Milk') + 1
elif a == 'Cashew' or a == 'Cashew ' or a == 'Hazelnut' or a == 'Walnut' or a == 'Nuts':
id_to_allergy_number_type_tag_map[sample] = 1
id_to_milk_allergy_tag_map[sample] = 0
allergy_type_to_instances_map[
'Tree_nut'] = allergy_type_to_instances_map.get(
'Tree_nut') + 1
elif a == 'Peanut':
id_to_allergy_number_type_tag_map[sample] = 2
id_to_milk_allergy_tag_map[sample] = 0
allergy_type_to_instances_map[
'Peanut'] = allergy_type_to_instances_map.get('Peanut') + 1
elif a == 'Sesame':
id_to_allergy_number_type_tag_map[sample] = 3
id_to_milk_allergy_tag_map[sample] = 0
allergy_type_to_instances_map[
'Sesame'] = allergy_type_to_instances_map.get('Sesame') + 1
elif a == 'Egg':
egg_allergy_type_ids.append(sample)
# id_to_allergy_number_type_tag_map[sample] = 1
# id_to_milk_allergy_tag_map[sample] = 0
# allergy_type_to_instances_map['Egg'] = allergy_type_to_instances_map.get('Egg') + 1
elif a == 'Non':
non_allergy_type_ids.append(sample)
# id_to_allergy_number_type_tag_map[sample] = None
# id_to_milk_allergy_tag_map[sample] = None
# allergy_type_to_instances_map['Non'] = allergy_type_to_instances_map.get('Non') + 1
else:
print("error in allergy type " + str(sample))
# -------------------------------------------- weights !--------------------------------------------
# calculate weights for types of allergy
total_sum = sum(list(allergy_type_to_instances_map.values()))
types = list(allergy_type_to_instances_map.keys())
allergy_type_to_weight_map = {}
for t in types:
allergy_type_to_weight_map[
t] = total_sum / allergy_type_to_instances_map[t]
# normalize
max_weight = max(list(allergy_type_to_weight_map.values()))
for t in types:
allergy_type_to_weight_map[t] = allergy_type_to_weight_map.get(
t) / max_weight
# calculate weights for milk vs. other types of allergy
milk_vs_other_allergy_weight_map = {
'Other':
total_sum /
(total_sum - allergy_type_to_instances_map.get("Milk")),
'Milk':
total_sum / allergy_type_to_instances_map.get("Milk")
}
# normalize
max_weight = max(list(milk_vs_other_allergy_weight_map.values()))
for t in ['Other', 'Milk']:
milk_vs_other_allergy_weight_map[
t] = milk_vs_other_allergy_weight_map.get(t) / max_weight
# calculate weights for healthy and allergic
healthy_vs_allergic_weight_map = {
'Allergic': (len(ids_list_w_con)) / len(ids_list_wo_con),
'Healthy': (len(ids_list_w_con)) /
(len(ids_list_w_con) - len(ids_list_wo_con))
}
# normalize
max_weight = max(list(healthy_vs_allergic_weight_map.values()))
for t in ['Allergic', 'Healthy']:
healthy_vs_allergic_weight_map[
t] = healthy_vs_allergic_weight_map.get(t) / max_weight
# calculate weights for responding and not (success)
no_response = list(id_to_binary_success_tag_map.values()).count(0)
yes_response = list(id_to_binary_success_tag_map.values()).count(1)
responding_vs_not_weight_map = {
'No': (len(ids_list_wo_con)) / no_response,
'Yes': (len(ids_list_wo_con) / yes_response)
}
# normalize
max_weight = max(list(responding_vs_not_weight_map.values()))
for t in ['No', 'Yes']:
responding_vs_not_weight_map[t] = responding_vs_not_weight_map.get(
t) / max_weight
# calculate weights for responding and not (prognostic)
tags = []
for i in stage_0_ids:
tags.append(id_to_binary_success_tag_map.get(i))
no_response = tags.count(0)
yes_response = tags.count(1)
prognostic_responding_vs_not_weight_map = {
'No': (len(stage_0_ids)) / no_response,
'Yes': (len(stage_0_ids) / yes_response)
}
# normalize
max_weight = max(list(
prognostic_responding_vs_not_weight_map.values()))
for t in ['No', 'Yes']:
prognostic_responding_vs_not_weight_map[
t] = prognostic_responding_vs_not_weight_map.get(
t) / max_weight
self._id_wo_non_and_egg_allergy_type_list = [
x for x in self._ids_list_wo_con
if x not in non_allergy_type_ids + egg_allergy_type_ids
]
self._tag_to_allergy_type_map = tag_to_allergy_type_map
self._allergy_type_to_instances_map = allergy_type_to_instances_map
self._allergy_type_to_weight_map = allergy_type_to_weight_map
self._milk_vs_other_allergy_weight_map = milk_vs_other_allergy_weight_map
self._healthy_vs_allergic_weight_map = healthy_vs_allergic_weight_map
self._responding_vs_not_weight_map = responding_vs_not_weight_map
self._prognostic_responding_vs_not_weight_map = prognostic_responding_vs_not_weight_map
self._id_to_success_tag_map = id_to_success_tag_map
self._id_to_stage_map = id_to_stage_map
self._id_to_binary_success_tag_map = id_to_binary_success_tag_map
self._id_to_allergy_type_tag_map = id_to_allergy_type_tag_map
self._id_to_allergy_number_type_tag_map = id_to_allergy_number_type_tag_map
self._id_to_milk_allergy_tag_map = id_to_milk_allergy_tag_map
""" # count tags in all vs. stage_0
all_tags = list(id_to_binary_success_tag_map.values())
print("tags total len: " + str(len(all_tags)) + " pos tags: " + str(all_tags.count(1))
+ " percent: " + str(all_tags.count(1)/len(all_tags)))
stage_0_tags = [id_to_binary_success_tag_map[id] for id in stage_0_ids if id in id_to_binary_success_tag_map.keys()]
print("stage 0 tags total len: " + str(len(stage_0_tags)) + " pos tags: " + str(stage_0_tags.count(1))
+ " percent: " + str(stage_0_tags.count(1)/len(stage_0_tags)))
"""
# return the list of features and the list of ids in the same order
feature_list = [id_to_features_map[id] for id in ids_list_w_con]
return ids_list_w_con, ids_list_wo_con, feature_list
if pd.isnull(date_str):
date_str = '01/01/1900'
return datetime.datetime.strptime(date_str, '%d/%m/%Y')
def get_days(days_datetime):
return days_datetime.days
n_components = 20
use_recorded = True
if not use_recorded:
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, 'saliva_samples_231018.csv'),
os.path.join(SCRIPT_DIR, 'saliva_samples_mapping_file_231018.csv'), from_QIIME=True)
preproccessed_data = preprocess_data(OtuMf.otu_file, visualize_data=False, taxnomy_level=5)
otu_after_pca_wo_taxonomy, _ = apply_pca(preproccessed_data, n_components=n_components, visualize=False)
# otu_after_pca = OtuMf.add_taxonomy_col_to_new_otu_data(otu_after_pca_wo_taxonomy)
# merged_data_after_pca = OtuMf.merge_mf_with_new_otu_data(otu_after_pca_wo_taxonomy)
# merged_data_with_age = otu_after_pca_wo_taxonomy.join(OtuMf.mapping_file['age_in_days'])
# merged_data_with_age = merged_data_with_age[merged_data_with_age.age_in_days.notnull()] # remove NaN days
# merged_data_with_age_group = otu_after_pca_wo_taxonomy.join(OtuMf.mapping_file[['age_group', 'age_in_days','MouseNumber']])
# merged_data_with_age_group = merged_data_with_age_group[merged_data_with_age_group.age_group.notnull()] # remove NaN days
# OtuMf.mapping_file.apply(lambda x: -999 if x['Mucositis_Start'] is None else (datetime.datetime.strptime(x['DATE'], '%d/%m/%Y') - datetime.datetime.strptime(x['Mucositis_Start'], '%d/%m/%Y')).days)
OtuMf.mapping_file['DATE_datetime'] = OtuMf.mapping_file['DATE'].apply(get_datetime)
OtuMf.mapping_file['Mocosities_start_datetime'] = OtuMf.mapping_file['Mucositis_Start'].apply(get_datetime)
OtuMf.mapping_file['TIME_BEFORE_MOCO_START'] = OtuMf.mapping_file['Mocosities_start_datetime'] - OtuMf.mapping_file[
def prepare_data(n_components = 20):
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, 'saliva_samples_231018.csv'),
os.path.join(SCRIPT_DIR,
'saliva_samples_mapping_file_231018.csv'), from_QIIME=True)
preproccessed_data = preprocess_data(OtuMf.otu_file, visualize_data=True, taxnomy_level=5,
preform_taxnomy_group=True)
otu_after_pca_wo_taxonomy, _, _ = apply_pca(preproccessed_data, n_components=n_components, visualize=True)
######## Pre process (Remove control group) ########
OtuMf.mapping_file['DATE_datetime'] = OtuMf.mapping_file['DATE'].apply(get_datetime)
OtuMf.mapping_file['Mocosities_start_datetime'] = OtuMf.mapping_file['Mucositis_Start'].apply(get_datetime)
OtuMf.mapping_file['TIME_BEFORE_MOCO_START'] = OtuMf.mapping_file['Mocosities_start_datetime'] - OtuMf.mapping_file[
'DATE_datetime']
OtuMf.mapping_file['time_for_the_event'] = OtuMf.mapping_file['TIME_BEFORE_MOCO_START'].apply(get_days)
OtuMf.mapping_file['time_for_the_event'][
OtuMf.mapping_file['Mocosities_start_datetime'] == datetime.datetime.strptime('01/01/1900', '%d/%m/%Y')] = 9999
# create groups
data_grouped = OtuMf.mapping_file.groupby('Personal_ID')
censored_data = {}
not_censored = pd.DataFrame()
dilated_df = pd.DataFrame()
y_for_deep = pd.DataFrame()
x_for_deep = pd.DataFrame()
x_for_deep_censored = pd.DataFrame()
y_for_deep_censored = pd.DataFrame()
for subject_id, subject_data in data_grouped:
if 9999 in subject_data['time_for_the_event'].values: # censored
tmp_data = subject_data.join(otu_after_pca_wo_taxonomy)
tmp_data_only_valid = tmp_data.loc[tmp_data[0].notnull()]
if not tmp_data_only_valid.empty:
x_for_deep_censored = x_for_deep_censored.append(subject_data)
tmp_data_only_valid['time_before_moco_start_days'] = tmp_data_only_valid[
'TIME_BEFORE_MOCO_START'].apply(get_days)
tmp_data_only_valid.sort_index(by='time_before_moco_start_days', ascending=False, inplace=True)
tmp_data_only_valid['relative_start_date'] = tmp_data_only_valid['time_before_moco_start_days'].iloc[
0] - tmp_data_only_valid[
'time_before_moco_start_days']
tmp_data_only_valid['relative_max_date'] = tmp_data_only_valid['relative_start_date'][-1] - \
tmp_data_only_valid['relative_start_date']
tmp_data_only_valid['delta_time'] = -1
tmp_data_only_valid['mse_coeff'] = 0
tmp_data_only_valid['time_sense_coeff'] = 1
y_for_deep_censored = y_for_deep_censored.append(
tmp_data_only_valid[['relative_start_date', 'delta_time', 'relative_max_date', 'mse_coeff', 'time_sense_coeff']])
# get only the last sample
censored_data[subject_id] = tmp_data_only_valid.loc[
tmp_data_only_valid['TIME_BEFORE_MOCO_START'] == min(tmp_data_only_valid['TIME_BEFORE_MOCO_START'])]
else: # not censored
before_event_mask = subject_data['time_for_the_event'] > 0
before_event_subjects = subject_data.loc[before_event_mask]
if not before_event_subjects.empty:
not_censored = not_censored.append(before_event_subjects)
dilated_df = dilated_df.append(before_event_subjects)
x_for_deep = x_for_deep.append(before_event_subjects)
before_event_subjects['time_before_moco_start_days'] = before_event_subjects[
'TIME_BEFORE_MOCO_START'].apply(get_days)
before_event_subjects.sort_index(by='time_before_moco_start_days', ascending=False, inplace=True)
before_event_subjects['relative_start_date'] = before_event_subjects['time_before_moco_start_days'].iloc[
0] - before_event_subjects['time_before_moco_start_days']
before_event_subjects['relative_max_date'] = before_event_subjects['relative_start_date'] + \
before_event_subjects['time_before_moco_start_days']
before_event_subjects['delta_time'] = before_event_subjects['time_for_the_event']
before_event_subjects['mse_coeff'] = 1
before_event_subjects['time_sense_coeff'] = 0
y_for_deep = y_for_deep.append(
before_event_subjects[['relative_start_date', 'delta_time', 'relative_max_date', 'mse_coeff', 'time_sense_coeff']])
x_for_deep = x_for_deep.join(otu_after_pca_wo_taxonomy)
x_for_deep = x_for_deep.loc[x_for_deep[0].notnull()]
y_for_deep = y_for_deep.loc[x_for_deep.index]
x_for_deep_censored = x_for_deep_censored.join(otu_after_pca_wo_taxonomy)
x_for_deep_censored = x_for_deep_censored.loc[x_for_deep_censored[0].notnull()]
y_for_deep_censored = y_for_deep_censored.loc[x_for_deep_censored.index]
return x_for_deep, y_for_deep, x_for_deep_censored, y_for_deep_censored, censored_data, not_censored, otu_after_pca_wo_taxonomy, OtuMf
def get_days(days_datetime):
return days_datetime.days
n_components = 10
use_recorded = False
script_dir = sys.path[0]
if not use_recorded:
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, 'ronies_Data',
'saliva_samples_231018.csv'),
os.path.join(
SCRIPT_DIR, 'ronies_Data',
'saliva_samples_mapping_file_231018.csv'),
from_QIIME=True)
preproccessed_data = preprocess_data(OtuMf.otu_file,
visualize_data=False,
taxnomy_level=6)
otu_after_pca_wo_taxonomy, _ = apply_pca(preproccessed_data,
n_components=n_components,
visualize=False)
# otu_after_pca = OtuMf.add_taxonomy_col_to_new_otu_data(otu_after_pca_wo_taxonomy)
# merged_data_after_pca = OtuMf.merge_mf_with_new_otu_data(otu_after_pca_wo_taxonomy)
# merged_data_with_age = otu_after_pca_wo_taxonomy.join(OtuMf.mapping_file['age_in_days'])
# merged_data_with_age = merged_data_with_age[merged_data_with_age.age_in_days.notnull()] # remove NaN days
# merged_data_with_age_group = otu_after_pca_wo_taxonomy.join(OtuMf.mapping_file[['age_group', 'age_in_days','MouseNumber']])
# merged_data_with_age_group = merged_data_with_age_group[merged_data_with_age_group.age_group.notnull()] # remove NaN days
def prepare_data(n_components=20):
OtuMf = OtuMfHandler(
os.path.join(
SCRIPT_DIR,
'feature-table_Allergy_cleaned_taxa_290119_updated_in_140219.csv'),
os.path.join(
SCRIPT_DIR,
'mf_merge_ok84_ok93_ok66_69_merged_by_RestoredSampleCode_as_ID_290119.csv'
),
from_QIIME=True,
id_col='Feature ID',
taxonomy_col='Taxonomy')
preproccessed_data = preprocess_data(OtuMf.otu_file,
visualize_data=False,
taxnomy_level=5,
taxonomy_col='Taxonomy',
preform_taxnomy_group=True)
otu_after_pca_wo_taxonomy, _, _ = apply_pca(preproccessed_data,
n_components=n_components,
visualize=False)
######## Pre process (Remove control group) ########
column_to_use_for_filter = 'AllergyTypeData131118'
OtuMf.mapping_file = OtuMf.mapping_file.loc[
OtuMf.mapping_file['AllergyTypeData131118'] != 'Con']
######## get date of sample in date format ########
date_of_sample_col = 'Date'
OtuMf.mapping_file['Date_of_sample'] = OtuMf.mapping_file[
date_of_sample_col].apply(get_datetime, time_format='%m/%d/%y')
######## remove invalid subjects (those who had samples with no dates or bad dates) ########
# bad dates
tmp = OtuMf.mapping_file.loc[OtuMf.mapping_file['Date_of_sample'].isin(
['1800-01-01', '1900-01-01'])]
patients_with_bad_date = tmp['PatientNumber210119'].unique()
# remove bad dates
OtuMf.mapping_file = OtuMf.mapping_file.loc[
~OtuMf.mapping_file['PatientNumber210119'].isin(patients_with_bad_date
)]
######## Calculate time for event ########
OtuMf.mapping_file['time_for_the_event'] = 9999
col_to_group_by = 'PatientNumber210119'
data_grouped = OtuMf.mapping_file.groupby(col_to_group_by)
for subject_id, subject_data in data_grouped:
if any(subject_data['SuccessDescription'] == 'A1'): # Uncensored
date_of_event = subject_data['Date_of_sample'].max()
time_for_the_event = date_of_event - subject_data['Date_of_sample']
tmp_df = OtuMf.mapping_file.loc[subject_data.index]
tmp_df['time_for_the_event'] = time_for_the_event.apply(get_days)
OtuMf.mapping_file.update(tmp_df)
else: # Censored
pass
######## Filter alergies ########
# allergy types ['Sesame', 'Peanut', 'Egg', 'Non', 'Walnut', 'Milk', 'Cashew', 'Hazelnut']
# OtuMf.mapping_file['AllergyTypeData131118'].value_counts()
# Peanut 134
# Milk 112
# Sesame 80
# Walnut 72
# Egg 28
# Cashew 18
# Hazelnut 9
# Non 9
allergy_to_use = ['Peanut']
OtuMf.mapping_file = OtuMf.mapping_file[
OtuMf.mapping_file['AllergyTypeData131118'].isin(allergy_to_use)]
######## Create inputs ########
# create groups
col_to_group_by = 'PatientNumber210119'
data_grouped = OtuMf.mapping_file.groupby(col_to_group_by)
censored_data = {}
not_censored = pd.DataFrame()
y_for_deep = pd.DataFrame()
x_for_deep = pd.DataFrame()
x_for_deep_censored = pd.DataFrame()
y_for_deep_censored = pd.DataFrame()
def calculate_y_for_deep_per_row(row):
a = row.sort_values()
return a.index[0]
for subject_id, subject_data in data_grouped:
if 9999 in subject_data['time_for_the_event'].values: # censored
tmp_data = subject_data.join(otu_after_pca_wo_taxonomy)
tmp_data_only_valid = tmp_data.loc[tmp_data[0].notnull()]
if not tmp_data_only_valid.empty:
x_for_deep_censored = x_for_deep_censored.append(subject_data)
tmp_data_only_valid.sort_index(by='Date_of_sample',
ascending=True,
inplace=True)
tmp_data_only_valid['relative_start_date'] = (
tmp_data_only_valid['Date_of_sample'] -
tmp_data_only_valid['Date_of_sample'].iloc[0]
).apply(get_days)
tmp_data_only_valid['relative_max_date'] = (
tmp_data_only_valid['Date_of_sample'].iloc[-1] -
tmp_data_only_valid['Date_of_sample']).apply(get_days)
tmp_data_only_valid['delta_time'] = -1
tmp_data_only_valid['mse_coeff'] = 0
tmp_data_only_valid['time_sense_coeff'] = 1
y_for_deep_censored = y_for_deep_censored.append(
tmp_data_only_valid[[
'relative_start_date', 'delta_time',
'relative_max_date', 'mse_coeff', 'time_sense_coeff'
]])
# get only the last sample
censored_data[subject_id] = tmp_data_only_valid.loc[
tmp_data_only_valid['relative_max_date'] == min(
tmp_data_only_valid['relative_max_date'])]
else: # not censored
before_event_mask = subject_data['time_for_the_event'] > 0
before_event_subjects = subject_data.loc[before_event_mask]
if not before_event_subjects.empty:
not_censored = not_censored.append(before_event_subjects)
x_for_deep = x_for_deep.append(before_event_subjects)
before_event_subjects.sort_index(by='time_for_the_event',
ascending=False,
inplace=True)
before_event_subjects[
'relative_start_date'] = before_event_subjects[
'time_for_the_event'].iloc[0] - before_event_subjects[
'time_for_the_event']
before_event_subjects[
'relative_max_date'] = before_event_subjects[
'time_for_the_event']
before_event_subjects['delta_time'] = before_event_subjects[
'time_for_the_event']
before_event_subjects['mse_coeff'] = 1
before_event_subjects['time_sense_coeff'] = 0
y_for_deep = y_for_deep.append(before_event_subjects[[
'relative_start_date', 'delta_time', 'relative_max_date',
'mse_coeff', 'time_sense_coeff'
]])
x_for_deep = x_for_deep.join(otu_after_pca_wo_taxonomy)
x_for_deep = x_for_deep.loc[x_for_deep[0].notnull()]
y_for_deep = y_for_deep.loc[x_for_deep.index]
x_for_deep_censored = x_for_deep_censored.join(otu_after_pca_wo_taxonomy)
x_for_deep_censored = x_for_deep_censored.loc[
x_for_deep_censored[0].notnull()]
y_for_deep_censored = y_for_deep_censored.loc[x_for_deep_censored.index]
return x_for_deep, y_for_deep, x_for_deep_censored, y_for_deep_censored, censored_data, not_censored, otu_after_pca_wo_taxonomy, OtuMf
from sklearn import svm
# from sklearn.svm import SV
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.cross_validation import train_test_split
import xgboost as xgb
import datetime
from gvhd.show_data import calc_results
from gvhd.calculate_distances import calculate_distance
from gvhd.cluster_time_events import cluster_based_on_time
n_components = 20
OtuMf = OtuMfHandler('otu_IBD_table.csv',
'metadata_ok94_ok59.csv',
from_QIIME=True)
preproccessed_data = preprocess_data(OtuMf.otu_file,
visualize_data=True,
taxnomy_level=5,
preform_taxnomy_group=False)
otu_after_pca_wo_taxonomy, _ = apply_pca(preproccessed_data,
n_components=n_components,
visualize=False)
# otu_after_pca = OtuMf.add_taxonomy_col_to_new_otu_data(otu_after_pca_wo_taxonomy)
# merged_data_after_pca = OtuMf.merge_mf_with_new_otu_data(otu_after_pca_wo_taxonomy)
# merged_data_with_age = otu_after_pca_wo_taxonomy.join(OtuMf.mapping_file['age_in_days'])
# merged_data_with_age = merged_data_with_age[merged_data_with_age.age_in_days.notnull()] # remove NaN days
# merged_data_with_age_group = otu_after_pca_wo_taxonomy.join(OtuMf.mapping_file[['age_group', 'age_in_days','MouseNumber']])
# merged_data_with_age_group = merged_data_with_age_group[merged_data_with_age_group.age_group.notnull()] # remove NaN days
,
x_label='Bacteria',
y1_data=means,
y1_color='#539caf',
y1_label='Means',
y2_data=normal_means,
y2_color='#7663b0',
y2_label='Normalized means',
title='before_and_after_z_score_per_person_bar_plot')
#
samples_data_file = 'mf_merge_ok84_ok93_ok66_69_merged_by_RestoredSampleCode_as_ID_290119.csv'
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, bactria_as_feature_file),
os.path.join(SCRIPT_DIR, samples_data_file),
from_QIIME=True,
id_col='Feature ID',
taxonomy_col='Taxonomy')
preproccessed_data = preprocess_data(OtuMf.otu_file,
visualize_data=False,
taxnomy_level=5,
taxonomy_col='Taxonomy',
preform_taxnomy_group=True)
otu_after_pca_wo_taxonomy, _, _ = apply_pca(preproccessed_data,
n_components=n_components,
visualize=False)
# Remove control group == 'Con'
tag_column = 'SuccessDescription'
X = otu_after_pca_wo_taxonomy.index.tolist()
plt.title(r'$1-\rho$ vs params')
plt.xlabel('sample #')
plt.ylabel(r'$1-\rho$ value')
def predict_get_spearman_value(test_set, regressor):
test_df = pd.DataFrame(test_set['age_in_days'])
test_df['predicted'] = regressor.predict(
test_set.loc[:, test_set.columns != 'age_in_days'])
spearman_values = use_spearmanr(test_set['age_in_days'].values,
test_df['predicted'].values)
return test_df, spearman_values
if __name__ == "__main__":
OtuMf = OtuMfHandler('aging_otu_table.csv', 'mf.csv', from_QIIME=True)
preproccessed_data = preprocess_data(OtuMf.otu_file,
visualize_data=True,
taxnomy_level=5)
otu_after_pca_wo_taxonomy, _ = apply_pca(preproccessed_data,
n_components=80)
# otu_after_pca = OtuMf.add_taxonomy_col_to_new_otu_data(otu_after_pca_wo_taxonomy)
# merged_data_after_pca = OtuMf.merge_mf_with_new_otu_data(otu_after_pca_wo_taxonomy)
merged_data_with_age = otu_after_pca_wo_taxonomy.join(
OtuMf.mapping_file['age_in_days'])
merged_data_with_age = merged_data_with_age[
merged_data_with_age.age_in_days.notnull()] # remove NaN days
# create train set and test set
merged_data_with_age = merged_data_with_age.sample(frac=1)
train_size = math.ceil(merged_data_with_age.shape[0] * 0.85)
def _read_file(self, title, bactria_as_feature_file, samples_data_file,
allow_printing, perform_anna_preprocess):
features = pd.read_csv(bactria_as_feature_file, header=1)
cols = list(features.columns)
# remove non-numeric values
cols.remove('Feature ID')
cols.remove('Taxonomy')
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, bactria_as_feature_file),
os.path.join(SCRIPT_DIR, samples_data_file),
from_QIIME=True,
id_col='Feature ID',
taxonomy_col='Taxonomy')
preproccessed_data = preprocess_data(OtuMf.otu_file,
visualize_data=False,
taxnomy_level=self._taxnomy_level,
taxonomy_col='Taxonomy',
preform_taxnomy_group=True)
self._preproccessed_data = preproccessed_data
# drow_data(preproccessed_data)
# otu_after_pca_wo_taxonomy, _, _ = apply_pca(data_after_log_zcore, n_components=40, visualize=False)
otu_after_pca_wo_taxonomy, pca_obj, _ = apply_pca(
preproccessed_data, n_components=n_components, visualize=False)
self._pca_obj = pca_obj
index_to_id_map = {}
id_to_features_map = {}
for i, row in enumerate(otu_after_pca_wo_taxonomy.values):
id_to_features_map[otu_after_pca_wo_taxonomy.index[i]] = row
index_to_id_map[i] = otu_after_pca_wo_taxonomy.index[i]
self._index_to_id_map = index_to_id_map
self._id_to_features_map = id_to_features_map
ids_list = otu_after_pca_wo_taxonomy.index.tolist()
self._ids_list = ids_list
XXXXX_column = 'SuccessDescription'
id_to_tag_map = {}
for sample in ids_list:
t = OtuMf.mapping_file.loc[sample, XXXXX_column]
id_to_tag_map[sample] = t
if t == 'A1':
id_to_tag_map[sample] = 1
else:
id_to_tag_map[sample] = 0
self._id_to_tag_map = id_to_tag_map
# -------------------------------------------- weights !--------------------------------------------
# calculate weights
y = list(id_to_tag_map.values())
classes_sum = [
np.sum(np.array(y) == unique_class)
for unique_class in np.unique(np.array(y))
]
classes_ratio = [1 - (a / sum(classes_sum)) for a in classes_sum]
weights = [classes_ratio[a] for a in np.array(y)]
self._weight_map = classes_ratio
# return the list of features and the list of ids in the same order
feature_list = [id_to_features_map[id] for id in ids_list]
self._feature_list = feature_list
def get_days(days_datetime):
return days_datetime.days
n_components = 20
taxnomy_level = 3
n = n_components
file_name = f'combined_only_report_n_comps_{n_components}_taxonomy_level_{taxnomy_level}_using_ronies_and_{n}_pca'
use_recorded = False
script_dir = sys.path[0]
if not use_recorded:
OtuMf = OtuMfHandler(os.path.join(SCRIPT_DIR, 'ronies_Data',
'saliva_samples_231018.csv'),
os.path.join(
SCRIPT_DIR, 'ronies_Data',
'saliva_samples_mapping_file_231018.csv'),
from_QIIME=True)
OtuMf = OtuMfHandler(
os.path.join(SCRIPT_DIR, 'learn dataset',
'mucositis_first_table_260219.csv'),
os.path.join(SCRIPT_DIR, 'learn dataset',
'mucositis_mapping_file_first_250219_numbers.csv'),
from_QIIME=True)
OtuMf = OtuMfHandler(
os.path.join(SCRIPT_DIR, 'combined_data', 'dataset',
'mucositis_combine_table_260219.csv'),
os.path.join(SCRIPT_DIR, 'combined_data', 'dataset',
'mucositis_mapping_file_combine_250219_numbers.csv'),