def test_get_partial_table_wrong_input():
delay = 10
data = ML_prepare(delay)
section = "blah"
with pytest.raises(AssertionError):
t = data.get_partial_table(x_section=section, y_labels=True)
def test_get_columns_of_sections():
delay = 2
data = ML_prepare(delay)
res = data.get_columns_of_sections()
assert isinstance(res, tuple)
for i in range(3):
assert isinstance(res[i], list)
def confusion_matrix_SVC(label: str, section: str, delay: int):
data = ML_prepare(delay)
table_xy = data.get_partial_table(x_section=section, y_labels=True)
X = table_xy.loc[:, 'x']
y = table_xy['y', label]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
clf = make_pipeline(StandardScaler(),
LinearSVC(random_state=0, tol=1e-5, max_iter=100000))
y_pred = clf.fit(X_train, y_train).predict(X_test)
le = preprocessing.LabelEncoder()
le.fit(list(y_pred))
y_pred = le.transform(y_pred)
y_test = le.transform(np.array(y_test))
cnf_matrix = confusion_matrix(y_test, y_pred)
plt.figure()
np.set_printoptions(precision=2)
plot_confusion_matrix(cnf_matrix,
classes=le.classes_,
normalize=False,
title='Confusion matrix, without normalization')
plt.savefig(
f"figures/SVC/LinearSVC_Confusion matrix, without normalization.png",
bbox_inches="tight")
def test_get_partial_table_no_nans():
delay = 10
data = ML_prepare(delay)
sections = ["all", "total_counts", "filaments", "various"]
for section in sections:
t = data.get_partial_table(x_section=section)
assert not t.isnull().values.any()
def test_get_partial_table_x_sections_lengths():
delay = 6
data = ML_prepare(delay)
sections = {"all": 27, "total_counts": 9, "filaments": 9, "various": 18}
for section in sections:
t = data.get_partial_table(x_section=section)
assert t["x"].shape[1] == sections[section]
def test_get_partial_table_y_labels():
delay = 0
data = ML_prepare(delay)
t1 = data.get_partial_table(x_section="all", y_labels=True)
assert t1["y"].columns.to_list() == ["SV_label", "SVI_label"]
t2 = data.get_partial_table(x_section="all", y_labels=False)
assert t2["y"].columns.to_list() == ["Settling_velocity", "SVI"]
def test_find_closest_date_out_of_range():
delay = 8
data = ML_prepare(delay)
date0 = data._svi_lst[0].index[-1]
# this is out of range:
res = data._find_closest_date(bio_reactor_i=0, date0=date0)
assert not res # assert res is False
def test_regr_model_func():
data = ML_prepare(1)
table_xy = data.get_partial_table(x_section="various", y_labels=False)
X, y = table_xy.loc[:, "x"], table_xy.loc[:, "y"]
models_dict = create_models_dict()
regr_model = list(models_dict.keys())[0]
score, fitted_model = regr_model_func(X, y, regr_model)
assert isinstance(score, float)
module = getattr(fitted_model, "__module__", None)
assert "sklearn" in module
def test_find_closest_date_missing_date():
# find missing date in svi
delay = 8
data = ML_prepare(delay)
date0 = data._micro_lst[0].index[0]
missing_date = date0 + timedelta(days=data.delay)
# remove the date
data._svi_lst[0].drop(missing_date, inplace=True)
# assert that this date was really changed 1 back
found_date = data._find_closest_date(bio_reactor_i=0, date0=date0)
assert found_date + timedelta(days=1) == missing_date
def test_loop_over_sections_and_y():
data = ML_prepare(7)
models_dict = create_models_dict()
regr_model = list(models_dict.keys())[0]
res = loop_over_sections_and_y(data, regr_model)
assert isinstance(res, tuple)
assert len(res) == 8
def choose_k_value(section: str, label: str, delay: int):
"""
plot graph of scores_k by k_range for KNeighbors model.
The user need to choose the k value by the result.
Parameters
----------
section : str
microorganisms section - should be: all or total_counts or filaments or various
label : str
label - should be: SV_label or SVI_label
delay : int
delay between the microscopic test which took place and the SVI test
return
----------
k : int
k choosen by the user
"""
if section not in {'all', 'total_counts', 'filaments', 'various'}:
raise ValueError(
"Please supply a valid section value: 'all','total_counts', 'filaments', 'various' "
)
if label not in {'SV_label', 'SVI_label'}:
raise ValueError(
"Please supply a valid label value: 'SV_label', 'SVI_label' ")
data = ML_prepare(delay)
table_xy = data.get_partial_table(x_section=section, y_labels=True)
X = table_xy.loc[:, 'x']
y = table_xy['y', label]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
scores_data = check_K_values(20, X_train, y_train, X_test, y_test)
sns.set()
sns.stripplot(data=scores_data, x='k', y='score', size=10)
plt.ylim(0, 1)
print(
"please look at the graph and choose k value. press enter to continue")
input()
plt.show()
print("please insert k value")
k = int(input())
return k
def test_init_types():
delay = 3
data = ML_prepare(delay)
# check types and lengths
assert isinstance(data, ML_prepare)
assert data._delay == delay
assert isinstance(data._svi_lst, list)
assert isinstance(data._micro_lst, list)
def test_init_df_lists():
delay = 8
data = ML_prepare(delay)
assert len(data._svi_lst) == 4
assert len(data._micro_lst) == 4
for i in range(4):
assert isinstance(data._svi_lst[i], pd.DataFrame)
assert isinstance(data._micro_lst[i], pd.DataFrame)
def get_day3_filaments_svi_data():
'''
Generates the most promising data producing regression results:
filaments microscopic data, and svi results of 3 days later.
return
--------
filaments_x: pd.DataFrame
microscopic measurements of all filament organisms.
filaments_svi: pd.Series
matching svi results of 3 days later
'''
data = ML_prepare(delay=3)
filaments_table = data.get_partial_table(x_section="filaments",
y_labels=False)
filaments_x = filaments_table.loc[:, "x"]
filaments_svi = filaments_table.loc[:, ("y", "SVI")]
return filaments_x, filaments_svi
def create_score_list_Knn(labels: list, sections: list, delay_lst: list,
k: int) -> list:
"""
create KNeighbors score list of all the prediction by label type, section of microorganism and delay
Parameters
----------
labels : list
list of labels: SV and SVI
sections : list
list of microorganisms sections: all, total_counts, filaments and various
delay_lst : list
list of delays between the microscopic test which took place and the SVI test
K : int
k value for KNeighbors model
return
----------
score_delay : list
score list of score predictions for each combination of label, section and delay
"""
if k <= 0:
raise ValueError("Please supply k value > 0 ")
score_delay = []
for label in labels:
for section in sections:
for delay in delay_lst:
data = ML_prepare(delay)
table_xy = data.get_partial_table(x_section=section,
y_labels=True)
X = table_xy.loc[:, 'x']
y = table_xy['y', label]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42)
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
y_predict = (knn.predict(X_test))
score_label = score_by_label(y_test, y_predict)
score = knn.score(X_test, y_test)
score_label.append(score)
score_delay.append(score_label)
return score_delay
def create_score_list_SVC(labels: list, sections: list,
delay_lst: list) -> list:
"""
create LinearSVC score list of all the prediction by label type, section of microorganism and delay
Parameters
----------
labels : list
list of labels: SV and SVI
sections : list
list of microorganisms section: all, total_counts, filaments and various
delay_lst : list
list of delays between the microscopic test which took place and the SVI test
return
----------
score_delay : list
score list of score predictions for each combination of label, section and delay
"""
score_delay = []
for label in labels:
for section in sections:
for delay in delay_lst:
data = ML_prepare(delay)
table_xy = data.get_partial_table(x_section=section,
y_labels=True)
X = table_xy.loc[:, 'x']
y = table_xy['y', label]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42)
clf = make_pipeline(
StandardScaler(),
LinearSVC(random_state=0, tol=1e-5, max_iter=100000))
clf.fit(X_train, y_train)
y_predict = (clf.predict(X_test))
score_label = score_by_label(y_test, y_predict)
score = clf.score(X_test, y_test)
score_label.append(score)
score_delay.append(score_label)
# print(f"result for delay= {delay}, section= {section}, label={label} : score_bad={score_label[0]}, score_reasonable={score_label[1]}, score_good={score_label[2]}, score={score_label[3]}")
return score_delay
def test_indirect_join_x_y():
delay = 3
data = ML_prepare(delay)
x1 = data.delay_table.loc[:, "micro"].reset_index(drop=True)
x2 = data.x.reset_index(drop=True)
y1 = data.delay_table.loc[:, "svi"].reset_index(drop=True)
y2 = data.y.reset_index(drop=True)
assert x1.equals(x2)
assert y1.equals(y2)
def test_indirect_create_x_y_bioreactor():
delay = 10
data = ML_prepare(delay)
delay_time = timedelta(days=data.delay)
# assert that date difference is correct
data._y.loc["3", :].index[0] - data._x.loc["3", :].index[0] == delay_time
# assert that out_of_range dates were removed from x as well
assert data._micro_lst[3].shape[0] == data._x.loc["4", :].shape[0] + 1
def test_create_list_of_tidy_df_by_day_types():
data = ML_prepare(4)
models_dict = create_models_dict()
delay_range = range(8, 14)
scores_models_dict = get_scores_of_all_models(
models_dict, delay_range=delay_range, print_flag=False
)
days_df_dict = create_list_of_tidy_df_by_day(scores_models_dict, delay_range)
assert isinstance(days_df_dict, dict)
assert set(days_df_dict.keys()) == set(delay_range)
assert isinstance(days_df_dict[8], pd.DataFrame)
def test_create_list_of_tidy_df_by_day_values():
data = ML_prepare(4)
models_dict = create_models_dict()
delay_range = range(1, 13)
scores_models_dict = get_scores_of_all_models(
models_dict, delay_range=delay_range, print_flag=False
)
days_df_dict = create_list_of_tidy_df_by_day(scores_models_dict, delay_range)
df4 = days_df_dict[4]
assert df4.shape == (40, 4)
assert all(df4["model"].value_counts() == 8)
assert all(df4["sv_svi"].value_counts() == 20)
assert all(df4["section"].value_counts() == 10)
def get_scores_of_model(regr_model,
model_name: str,
delay_range,
print_flag: bool = True):
'''
Generates for a given model, a dictionary with
scores for all delay options, and all 8 "x" and "y" combinations.
Using helper functions.
Parameters
---------
regr_model: sklearn model object
model_name: str
delay_range: range
print_flag: bool
Print or not all results
return
---------
scores_by_delay_dict: dict
'''
scores_by_delay_dict = {}
for delay in delay_range:
data = ML_prepare(delay=delay)
scores_by_delay_dict[delay] = loop_over_sections_and_y(
data=data, regr_model=regr_model)
if print_flag:
print(f"\nmodel: {model_name}")
for delay in scores_by_delay_dict:
tup_delay = scores_by_delay_dict[delay]
max_score = max(tup_delay)
name = [
tup_delay._fields[i] for i in range(len(tup_delay))
if tup_delay[i] == max_score
]
print(
f"max score for delay {delay}\t {max_score:.2f}, for {name[0]}"
)
return scores_by_delay_dict
def create_section_and_PCA(data: ML_prepare, labled: bool = False):
"""
Creates PCA for every section (organism group) of the data:
"all", "filaments", "total_counts", "various".
Using helper function "pca_plot".
Plots by the "y", results, whether labeled or not.
Parameters
----------
data: ML_prepare
labled: bool
"""
section_lst = ["all", "filaments", "total_counts", "various"]
fig, ax = plt.subplots(4, 2)
for i in range(len(section_lst)):
table_xy = data.get_partial_table(x_section=section_lst[i],
y_labels=labled)
y_cols = table_xy.loc[:, "y"].columns.tolist()
for j in range(2):
### model on y = y_cols[j]
pca_plot(table_xy,
color_col=y_cols[j],
section=section_lst[i],
ax_i=ax[i, j])
fig.set_figheight(15)
fig.set_figwidth(15)
fig.suptitle(
f"PCA of groups, colored by output, delay = {data.delay} days",
fontsize=20,
y=1.02,
)
plt.tight_layout()
fig_name = "PCA_by_groups"
if labled:
fig_name = fig_name + "_labled"
plt.tight_layout()
fig.savefig("figures/" + fig_name + ".png", dpi=150, bbox_inches="tight")
plt.show()
def loop_over_sections_and_y(data: ML_prepare, regr_model):
'''
Generates for a given model, for a given delayed data,
scores of all 8 combinations:
"all_sv",
"all_svi",
"filaments_sv",
"filaments_svi",
"total_counts_sv",
"total_counts_svi",
"various_sv",
"various_svi"
Saves results in namedtuple.
Parameters
---------
data: ML_prepare
allready generated with chosen delay
regr_model: sklearn model object
return
---------
tup_scores: namedtuple,
'''
scores_lst = []
section_lst = ["all", "filaments", "total_counts", "various"]
for i in range(len(section_lst)):
table_xy = data.get_partial_table(x_section=section_lst[i],
y_labels=False)
y_cols = table_xy.loc[:, "y"].columns.tolist()
for j in range(2):
X = table_xy.loc[:, "x"]
y = table_xy.loc[:, ("y", y_cols[j])]
score, _ = regr_model_func(X, y, regr_model)
scores_lst.append(score)
tup_scores = insert_scores_to_namedtuple(scores_lst)
return tup_scores
fontsize=20,
y=1.00)
sns.set()
g = sns.stripplot(data=df_weights, x="organism", y="weight", size=10)
g.set_xlabel("Organism", fontsize=20)
g.set_ylabel("weight in model", fontsize=20)
plt.xticks(rotation=90)
fig3.savefig("figures/Last_model_coefs.png", dpi=150, bbox_inches="tight")
plt.show()
return df_weights
if __name__ == "__main__":
# PCA for first glance, using 4 days delay
data = ML_prepare(delay=4)
create_section_and_PCA(data, labled=True)
create_section_and_PCA(data, labled=False)
# Create models and desired delay range
models_dict = create_models_dict()
delay_range = range(1, 13)
# get scores for all models for all sections, and plot them by days
scores_models_dict = get_scores_of_all_models(models_dict,
delay_range=delay_range,
print_flag=False)
days_df_dict = create_list_of_tidy_df_by_day(scores_models_dict,
delay_range)
days_swarmplot(days_df_dict)
def test_indirect_read_and_index_svi_tables():
delay = 2
data = ML_prepare(delay)
for i in range(4):
assert isinstance(data._svi_lst[i].index, pd.DatetimeIndex)
assert isinstance(data._svi_lst[i].index, pd.DatetimeIndex)
def test_indirect_create_x_y_delayed_length():
delay = 8
data = ML_prepare(delay)
assert data._x.shape[0] == data._y.shape[0]
def test_init_df_xy():
delay = 5
data = ML_prepare(delay)
assert isinstance(data._x, pd.DataFrame)
assert isinstance(data._y, pd.DataFrame)
assert isinstance(data.delay_table, pd.DataFrame)
